Question 1. Explain the uses and application of statistics in nursing.
Answer:

Statistics in Nursing Definition

• Statistics can be defined as the collection presentation and interpretation of numerical data.
• Statistics are the numerical statement of facts in any department of inquiry placed in interrelation to each other

“Understanding statistics through FAQs: Q&A explained”

Application of statistics in nursing.

1. To find out the growth rate, the fertility status, and the population size of the country.
2. It facilitates knowing about the health status of the community.
3. To assess the adequacy of the medical and paramedical manpower and of the health
   institutions in the country
4. To look for the differences in the magnitude of disease by person, place, and time.
5. Statistics helps in providing a better understanding and exact description of a phenomenon of nature.
6. Statistical helps in the proper and efficient planning of a statistical inquiry in any field of study.
7. Statistical help in collecting appropriate quantitative data.
8. Statistics helps in presenting complex data in a suitable tabular, diagrammatic, and graphic form for an easy and clear comprehension of the data.
9. Statistics helps in understanding the nature and pattern of variability of a phenomenon
   through quantitative observations.
10. Statistics helps in drawing valid inferences, along with a measure of their reliability about the population parameters from the sample data.

Read And Learn More: BSc Nursing 3rd Year Nursing Research And Statistics Previous year Question And Answers 

“How does statistics support nursing research outcomes? FAQ answered”

Question 2. Functions of statistics
Answer:

1. Expression of facts in numbers – One of the main functions of Statistics is to express numbers in easily understandable language and interpret the results with certainty.
2. Simple presentation – Statistics enables the presentation of complex data in a simple format in terms of aggregate, average, percentage, graphs, diagrams, etc.
3. Enlarges individual knowledge and experience – Statistics expands the horizons of individual knowledge and understanding.
4. It compares facts – It facilitates the comparison of data and identifying the interrelations
   between large sets of data for drawing suitable inferences.
5. Facilitates policy formulation – By doing analysis and interpretation of data, the precise nature of the problem can be ascertained thus assisting policy formulation.
6. It helps other scientists in testing their laws – Statistics helps other laws establish their assumptions. E.g., many laws of economics namely the law of demand, the law of supply, and Keynes’s theory of employment can be verified by Statistics.
7. It helps in forecasting – Extrapolating present data aids in forecasting likely changes that can be expected in the future.

Question 3. Explain the most commonly used measures of dispersion.
Answer:

Methods Of Dispersion Methods of studying dispersion are divided into two types:

Mathematical Methods: We can study the ’degree‘ and ’extent‘ of variation by these methods. In this category, commonly used measures of dispersion are:

1. Range
2. Quartile Deviation
3. Average Deviation
4. Standard deviation and coefficient of variation.

Measures Of Dispersion

Dispersion is another analytical method to study data.

• A main use of dispersion is to compare the amounts of spread in two (or more) data sets.
• A common technique in inferential statistics is to draw comparisons between populations by analyzing samples that come from those populations.
• Two of the most common measures of dispersion are the range and the standard deviation.

Range:

• For any set of data, the range of the set is given by the following formula:
• Range greatest value in setting least value in the set.

Range

Example:

• The two sets below have the same mean and median (7). Find the range of each set.

Range of Set A: 13-1 = 12

Range of Set A: 9-5 = 4

Standard Deviation:

• One of the most useful measures of dispersion is the standard deviation.
• It is based on deviations from the mean of the data.
• Find the deviations from the mean for all data values of the samples 1, 2, 8, 11, 13.
• The mean is 7.
• To find each deviation, subtract the mean from each data value.

The sum of the deviations is always equal to zero.

“Importance of studying statistics for BSc Nursing students: Questions explained”

Standard Deviation

Calculating the Sample Standard Deviation

• The sample standard deviation is found by calculating the square root of the variance.
• The variance is found by summing the squares of the deviations and dividing that sum by (since it is a sample instead of a population).
• The sample standard deviation is denoted by the letter s.
• The standard deviation of a population is denoted by σ.

Standard Deviation

Calculating the Sample Standard Deviation

1. Calculate the mean of the numbers.
2. Find the deviations from the mean.
3. Square each deviation.
4. Sum the squared deviations.
5. Divide the sum in Step 4 by.
6. Take the square root of the quotient in Step 5.

Standard Deviation

Calculating the Sample Standard Deviation

Example:

Find the standard deviation of the sample set{1,2,8,11,13}

x = (1+2+8+11+13)/5 = 7

Some of the (Deviations)²2 = 36+25+1+16+36 = 114

Standard Deviation

Calculating the Sample Standard Deviation

• Some of the (Deviations)2 = 36+25+1+16+36 = 114
• Divide 114 by n- 1 with n = 5:
• Take the square root of 28.5: 5.34
• The sample standard deviation of the data is 5.34.

“Common challenges in understanding statistics effectively: FAQs provided”

Question 4. Explain the uses and sources of the vital statistics.
Answer:

• Vital statistics are statistics on live births, deaths, fetal deaths, marriages, and divorces. The most common way of collecting information on these events is through civil registration an administrative system used by governments to record vital events which occur in their populations.
• Efforts to improve the quality of vital statistics will therefore be closely related to the development of civil registration systems in countries.

Vital statistics Definition:

• Vital statistics are conventionally numerical records of marriage, births, sicknesses, and deaths by which the health and growth of a community may be studied.
• Vital statistics is a part of demography and the collective study of mankind. It deals with the data related to vital events.

Uses of vital statistics:

1. Vital statistics show changing patterns in the population of a country. (medical / facilities, standard of living, etc)
2. Manufacturers plan their production by using vital statistics. (food, clothing, etc.)
3. Birth and death certificates are needed in many circumstances.
4. Accurately registered and systematically collected vital statistics can be used to check up the accuracy of data provided by the census.
5. The vital statistics are the administrative and research needs of public health agencies.

For the Individual:

1. Vital statistics are of much use to an individual.
2. A birth certificate issued by the registering authority is an important document which
   records the date, time, place, and parentage of the person.
3. It establishes his identity as a citizen of the country.
4. It is a legal document that is used for admission to a school, for getting a passport to travel abroad and even to migrate to another country, etc.
5. Similarly, a marriage certificate records the marital status of a couple and legalizes the birth of children from that marriage.

Legal Use:

1. Vital statistics are legally very useful.
2. Certificates relating to birth, death, marriage, divorce, etc. have legal importance.
3. For instance, a death certificate is an important legal document for the settlement of property of the deceased person, the claim of his/her insurance policy, etc.

Health and Family Planning Programmes:

1. Vital statistics relating to births and deaths can be used in health and family planning programs of the government.
2. The causes of death and the mortality rates of different categories help in assessing the health condition of the people.
3. Accordingly, the state can formulate such health programs as malaria eradication, polio, and smallpox immunization, tuberculosis, etc.
4. In keeping with the requirements of the population, the government can open hospitals, maternity and child welfare centers, etc.

Study of Social Conditions:

• Vital statistics like birth and death rates, divorce rate, widow remarriage, widowhood, etc. throw light on the social conditions of a society, as well as its customs and traditions.

For Administrators and Planners:

• Data provided by vital statistics relating to trends and growth of the population in the various age groups and on the whole, help planners and administrators to plan and formulate policies for public health, education, housing, transport and communications, food supplies, etc.

For the Nation:

1. Vital statistics are of much importance to the nation.
2. They help in analyzing the population trends at any given point of time.
3. They try to fill the gap between the two censuses.
4. They relate to the composition, size, distribution, and growth of population
5. It is on their basis that population projections can be made.
6. Vital statistics help in formulating policies for providing social security to the people.
7. Even the rules for immigration and emigration can be framed on the basis of population growth data.
8. Vital statistics are also used for updating electoral rolls and demarcation of constituencies.

Sources Of Vital Statistics:

1. There are four major sources of vital statistics namely;
2. The Sample Registration System (, SRS)
3. The Civil Registration System (CRS),
4. Indirect estimates from the decennial census and
5. Indirect estimates from the National Family Health Surveys (NFHS).

The SRS is the most regular source of demographic statistics in India. It is based on a system of dual recording of births and deaths in fairly representative sample units spread all over the country.

1. The SRS provides annual estimates of
2. Population composition,
3. Fertility,
4. Mortality, and
5. Medical attention at the time of birth or death which gives some idea about access to
   medical care.

“Why is early learning of statistics critical for evidence-based practice? Answered”

Question 5. Scales of Measurements.
Answer:

• In statistics and quantitative research methodology, various attempts have been made to classify variables (or types of data) and thereby develop a taxonomy of levels of measurement or scales of measure.
• Perhaps the best known are those developed by the psychologist Stanley Smith Stevens.
• He proposed four types: nominal, ordinal, interval, and ratio.

Scales of Measurements Definition:

• Measurements are the process of assigning numbers to variables. Measurements, as used in research, imply the qualification of information which is the assigning of some types of numbers to data.

Levels Of Measurement:

• The level of measurement describes the relationship among these three values.

Ordinal measurements:

• It involves the sorting of objects on the basis of their relative standing to each other on a specified attribute.

Interval measurement:

• It indicates not only the rank ordering of objects on an attribute but also the amount of distance between each object. Distance between numeric values on the interval scale represents an equivalent distance in the attribute being measured.

Nominal measurements:

• Are distinguished interval measurements by virtue of having a rational zero point. Most sophisticated statistical procedures require measures on the interval or ratio scales.

Ratio measurement

• There is always an absolute zero that is meaningful. This means that you can construct a meaningful fraction (or ratio) with a ratio variable. Weight is a ratio variable.

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Functions Of Measurement:

1. Measurements are widely used for
2. Selection of personnel in industry/institution.
3. Various types of classification effectively.
4. In order to compare two individuals.
5. In research, It is a basic part of the research.
6. Improving classroom instruction.

“Role of mean, median, and mode in nursing research: Questions answered”

Question 6. Measurement of central tendency.
Answer:

• Measures of central tendency are used to describe what is normal for a set of data. Mean, median, and mode are the three measures of central tendency.
• The mode can be used with both numerical and nominal data The term central tendency refers to the “middle” value or perhaps a typical value of the data and is measured using the mean, median, or mode.
• Each of these measures is calculated differently, and the one that is best to use depends upon the situation.
• The central tendency is otherwise called statistical averages. The word “average” implies a value in the distribution, around which the other values are distributed.

“How do standard deviation and variance measure data variability? FAQ explained”

Measurement of central tendency Definition:

• Central tendency is defined as “the statistical measure that identifies a single value as representative of an entire distribution.”
• Central tendency aims to provide an accurate description of the entire data. It is the single value that is most typical/representative of the collected data

Meaning Of Central Tendency:

1. A measure of central tendency is a single value that attempts to describe a set of data by identifying the central position within that set of data.
2. Measures of central tendency are sometimes called measures of central location.
3. They are also classed as summary statistics.
4. The mean (often called the average) is most likely the measure of central tendency that you are most familiar with, but there are others, such as the median and the mode.
5. The mean, median, and mode are all valid measures of central tendency, but under different conditions, some measures of central tendency become more appropriate to use than others.

Scales of Measurements Characteristics:

1. The important characteristics of an ideal measure of central tendency are
2. It should be based on all the observations of the series.
3. It should be easy to calculate and simple to understand.
4. It should not be affected by extreme values.
5. It should be rigidly defined.
6. It should be capable of further mathematical treatment
7. It should be least affected by the fluctuations of sampling.

Question 7. Advantages and disadvantages of Mean, Medium.
Answer:

“Early warning signs of gaps in understanding statistical basics: Common questions”

Question 8. Normal probability curve.
Answer:

• In probability theory, the normal (or Gaussian) distribution is a very commonly occurring continuous probability distribution function that tells the probability that any real observation will fall between any two real limits or real numbers, as the curve approaches zero on either side.

Concepts Of Normal Distribution:

1. The curve is a symmetrical, bell-shaped curve with the highest frequency occurring in the middle and gradually tapering toward the extremes.
2. In a normal distribution, 68.2% of all scores cluster around the mean within approximately 1 standard deviation, 95.4% within approximately 2 standard deviations, and 99.7% within approximately 3 standard deviations.
3. It is also called a normal curve, Gauss’ curve.
4. The normal distribution (often referred to as the “bell curve” is at the core of most inferential statistics.
5. By assuming that most complex processes result in a normal distribution (we’ll see why this is reasonable), we can gauge the probability of it happening by chance.
6. To best enjoy this tutorial, it is good to come to it understanding of what probability distributions and random variables are.

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Characteristics of the normal curve.

1. It will be bell-shaped.
2. It is symmetrical in distribution on either side of the mean are equal in number.
3. Its maximum height is at the mean.
4. Mean, median and mode are coinciding.
5. It is asymptomatic (the tails never touch the baseline theoretically)
6. It has two curves – the central part is convex – when comes down, it becomes concave on both sides.
7. The normal probability curve is a continuous-type probability curve.
8. All the measures of central tendency are equal and suitable on the highest peak axis (i.e., mean = median = mode) –
9. The total area under the curve is equal to unity.
10. The normal curve has two parameters i.e., mean and standard deviation.

Importance Of Normal Probability Curve

1. The normal distribution is important is that many psychological and educational variables are distributed approximately normally.
2. Measures of reading ability, introversion, job satisfaction, and memory are among the many psychological variables approximately normally distributed. although the distributions are only approximately normal, they are usually quite close.
3. It is very helpful in forecasting. We can calculate the estimated length of the bones of animals and wood and leaves. Because if animals are one type their numbering is normally distributed.
4. The normal distribution is so important is that it is easy for mathematical statisticians to work with.
5. Normal distribution is very useful for controlling the quality of the business. With this, we can fix the limit of quality. that will be helpful for controlling the quality.
6. If we take one sample out of the universe and calculate the mean size of growing then it will normal distribution
7. This means that many kinds of statistical tests can be derived for normal distributions.
8. Almost all statistical tests discussed in this text assume normal distributions.
9. These tests work very well even if the distribution is only approximately normally
   distributed. Some tests work well even with very wide deviations from normality.
10. If the mean and standard deviation of a normal distribution are known, it is easy to convert back and forth from raw scores to percentiles.

“Can targeted interventions improve outcomes using statistical knowledge? FAQs provided”

Question 9. Type-1 and -2 Errors
Answer:

Type-1 and -1 Errors

Type-1 error occurs when the null hypothesis is rejected when it should have been accepted; also called alpha error. Type-2 error occurs when a null hypothesis is accepted when it should actually have been rejected. It is also known as beta error. These errors generally occur due to an unrepresentative sample drawn from the population.

Question 10. Level of Significance
Answer: 

• The probability of making a type-1 error is called as level of significance. It is represented by a or the p. In other words, the level of significance is the probability of rejecting the null hypothesis when it is true. In health sciences, we generally consider the level of significance either 1% (.01) or 5% (.05).
• A significance of .05 means that the researcher is willing to take the risk of being wrong 5% of the time, 5 times out of 100 when rejecting the null hypothesis.
• If the decision needs to be much more accurate, such as deciding if a nursing intervention is effective or not, the level of significance might be set at .01 or even at .001.
• With a .01 level of significance, the researcher stands the risk of being wrong 1 time out of 100 when rejecting the null hypothesis.
• At the .001 level of significance, the risk is 1 time out of 1,000. However, most of the nursing studies consider a .05 level of significance to make a decision regarding whether to accept or reject the null hypotheses.

Question 11. Descriptive And Inferential Statistics
Answer:

• Statistics is broadly classified into two categories, i.e. descriptive and inferential. Descriptive statistics deal with the enumeration, organization, and graphical representation of data.
• An example of descriptive statistics is the decennial census of India, in which all the residents are requested to provide information such as age, gender, religion, marital status, education, and occupation.
• The data obtained in such a census can be compiled and arranged into tables and graphs that describe the characteristics of the population at a given time.
• Inferential statistics provides the procedures to draw an inference about the conditions that exist in a large set of observations, i.e. an entire population from the study of a part of that set (sample). This branch of statistics is also known as sampling statistics.
• An example of inferential static is to test the efficacy of a new antihypertensive drug in which the physician will have only a limited number of hypertensive subjects on which efficacy is tested and inferences are drawn.

Descriptive Statistics:

• Descriptive statistics are used to organize and summarize data to draw meaningful interpretations.
• Descriptive statistics also allow the researcher to interpret the data meaningfully, so that research questions can be answered completely and appropriately. Descriptive statistics may be categorized in several ways; however, this chapter presents the most simplified.

Classification of the descriptive statistics that includes:

• Frequency distribution and graphical presentation (measures of condensation)
• Measures of central tendency
• Measures of dispersion
• Measures of relationship (correlation coefficient)

“Differential applications of parametric vs non-parametric tests: Questions answered”

Question 12. Frequency Distribution
Answer:

• An appropriate presentation of data involves the organization of data in such a manner that meaningful conclusions and inferences can drawn to answer the research question. Unsorted and ungrouped records do not allow us to draw clear conclusions.
• Quantitative data are generally condensed and frequency distribution is presented through tables, charts, graphs, and diagrams.

Tables: A table presents data in a concise, systematic manner from masses of statistical data.

• Tabulation is the first step before data is used for further statistical analysis and interpretation.
• Tabulation means a systematic presentation of information contained in the data in rows and columns in accordance with some features and characteristics. Rows are horizontal and columns are vertical arrangements.

Question 13. Types and graphs in data presentation
Answer:

Graphical presentation of data:

• The main reasons for using the diagrammatic and graphic representation of data are as follows:
  • They are the most convenient and appealing ways in which statistical results may be presented.
  • They give an overall view of the entire data. They are visually more attractive than other ways of representing data.
  • It is easier to understand and memorize data through graphical representation.
  • They facilitate the comparison of data relating to different periods of time of different origins.

Types of diagrams and graphs:

• The commonly used diagrams and graphs in the presentation of data of the research studies are bar diagrams, pie diagrams, histograms, frequency polygons, line graphs, cumulative frequency curves, scattered diagrams, pictograms, and map diagrams.

Bar diagram:

• It is a convenient, graphical device that is particularly useful for displaying nominal or ordinal data. It is an easy method adopted for visual comparison of the magnitude of different frequencies.
• The length of the bars drawn vertically or horizontally indicates the frequency of a character.
• The bar charts are called vertical bar charts (or column charts) if the bars are placed vertically. When the bars are placed horizontally, we get horizontal bar charts.

There are three types of bar diagrams:

1. Simple bar diagram
2. Multiple bar diagram
3. Proportion bar diagram.

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Pie diagram/sector diagram: It is another useful pictorial device for presenting discrete data of qualitative characteristics such as age groups, genders, and occupational groups in a population.

• The total area of the circle represents the entire data under consideration. Researchers must remember that only percentage data must be used to prepare pie diagrams.
• It gives comparative differences at a glance. size of each angle is calculated by multiple class percentages with a 360 or a formula may be used that.

Histogram: It is the most commonly used graphical representation of grouped frequency distribution. Variable characters of the different groups are indicated on the horizontal line (x-axis) and frequencies (number of observations) are indicated on the vertical line (y-axis).

• The frequency of each group forms a column or rectangle. Such a diagram is called a ‘histogram’.
• The area of a rectangle is proportional to the frequency of the correspondence class interval, and the total area of the histogram is proportional to the total frequency of all the class intervals.

A histogram may be drawn by using the following steps:

• Set of vertical bars the areas of which are proportional to frequencies represented.
• The difference between a histogram and from bar diagram is that a bar diagram is one-dimensional and only the length of the bar has its significance while in histograms both length and width matter.
• When class intervals are equal, frequency is taken on the y-axis, the variables on the x-axis, and adjacent rectangles are constructed.
• When the class intervals are unequal, a correction for unequal class intervals must be made.

Frequency polygon:

• It is the curve obtained by joining the midpoints of the tops of the rectangles Points of the line drawn are joined to the horizontal axis at the midpoint of the empty class-intervals histogram by straight lines. It gives a polygon, i.e. figure with many angles.
• In this, the two end data patterns are clearer than histograms. On the same axis, one can plot frequency polygons of both ends of the frequency distribution. Frequency polygons are simple and sketch an outline of Several distributions, thereby making comparisons possible.

A histogram can be drawn by using the following steps:

• Draw the histogram of the given data.
• Join the midpoints of the upper horizontal sides of each rectangle with the adjacent one by a straight line.
• Close the polygon at both ends of the distribution by extending them to the baseline.
• Hypothetical classes at each end would have to be included at each end with a frequency of zero.

Line graphs:

• In this, variables in the frequency polygon are depicted by line. It is mostly used where data is collected over a long period of time. On the x-axis, values of independent variables are taken and values of dependent variables are taken on the y-axis.
• The vertical axis may not start from zero, but at some point, from where frequency starts. With reference to the x- and y-axis, the given data may be plotted and these consecutive points or data are then joined by a straight line.

Cumulative frequency curve or ogive:

• This graph represents the data of a cumulative frequency distribution.
• For drawing an ogive, an ordinary frequency distribution table is converted into a cumulative Frequency table.
• The cumulative frequencies are then plotted corresponding to the upper limits of the classes.
• The points corresponding to cumulative frequency at each upper limit of the classes are joined by a free-hand curve. The diagram made is called ogive.

“Most common complications of poorly understood statistical concepts: FAQs”

• Scattered or dotted diagrams: It is a graphic presentation that shows the nature of the correlation between two variable characters x and y on similar features or characteristics, e.g. height and weight 20 years old. Therefore, it is also called a correlation diagram.
• An example of a scattered or doctored gram is presented in

Pictograms or picture diagrams:

• This method is used to impress the frequency of the occurrence of events to common people, such as attacks, deaths, number of operations, admissions, accidents, and discharges in a population.

Map diagram or spot map:

• These maps are prepared to show the geographical distribution of frequencies of characteristics.

Limitations of graphs:

1. Confusing (may be false or true).
2. Present only quantitative aspect.
3. Get information only on one aspect or on limited characteristics.
4. They can present only approximate values.

Question 14. Degree of Freedom
Answer: 

• The interpretation of a statistical test depends on the degree of freedom. It is denoted by the abbreviation ’df‘ and a number (e.g. df=3).
• The degree of freedom indicates the number of values that are free to vary. Although the degree of freedom indicates the number of values that can vary, the concern is really focused on the number of values that are not free to vary.
• The procedure to calculate the degree of freedom varies from test to test; further details are discussed with individual tests of significance, and can be referred from there.

Question 15. Tests of Significance
Answer:

• There are several parametric (t-test, Z-test, ANOVA) and nonparametric tests (chi-square test, median test, McNemar test, Mann-Whitney Test, Wilcoxon Test, Fisher’s exact test) available to establish the statistical significance.

Parametric tests:

• These tests are also known as normal distribution statistical tests. The statistical methods of inference make certain assumptions about the populations from which the samples are drawn.
• For example, the assumptions may be that the populations are normally distributed, have the same variance, etc.; population values, as we have seen, are known as parameters; the statistical techniques that make assumptions about the parameters are called parametric techniques

Nonparametric tests:

• Researchers in the field of health sciences many times may not be aware of the nature of the distribution or other required population parameters.
• In addition, the sample may be too small to test the hypotheses and generalize the findings for the population from which the sample is drawn.
• Furthermore, many times in the observations presented in numerical figures, the scale of measurements may not be really numerical, such as grading bedsores, or ranks given to analgesic drug’s effectiveness in cancer pain management.
• In these situations, parametric tests may not be suitable, and a researcher may need different types of tests to draw inferences; those tests are known as nonparametric tests.

“Why are statistical methods often misunderstood in practice? Questions answered”

Question 16. Analysis of Variance (ANOVA) Test
Answer:

• When a researcher wants to compare the difference between more than two samples means; t-test I will be useful and a need for an alternative test will be felt. This need can be fulfilled by a test known as the analysis of variance (ANOVA) test.
• Therefore, it is clear that ANOVA is used to compare the more than two sample means drawn from the corresponding normal population.
• For example, a researcher wants to examine the difference in the effect of ginger on three different conditions such as nausea, vomiting, and retching, here t-test can be applied to examine the difference in the mean scores, because there are more than two groups; therefore, ANOVA will be used in this case.

Steps of application of ANOVA:

Calculate the total submissions of all the groups of observations. Calculate the sum squares of all the observations.

Calculate the total sum of squares by using the following formula;

= \(=\Sigma X^2-\frac{(\Sigma X)^2}{N}\)

Calculate the sum of squares between the groups by using the following formula.

= \(=\frac{\left(\sum \bar{X}_1\right)^2}{n_1}+\frac{\left(\sum \bar{X}_2\right)^2}{n_1}+\frac{\left(\sum \bar{X}_3\right)^2}{n_3}+\frac{\left(\sum \bar{X}_4\right)^2}{n_4}+\cdots \cdot \text { so on }-\frac{\left(\sum \bar{X}\right)^2}{N}\)

Calculate the sum of squares within the groups (error sum of squares) by using the following formula:

• = Total sum of squares – sum of squares between the groups
• Calculate the degree of freedom between and within the groups.
• df for between the groups = number of groups – 1
• df for within the groups = number of subjects in all the groups – number of groups
• df total is = number of subjects in all the groups – 1.

Calculate the mean of the sum of squares by using the following formula:

• Mean of sum of square between the groups = \(\frac{Sum of squares between the groups}{df for between the groups}\)
• Mean of sum of squares within the groups = \(\frac{Sum of squares within the groups}{df for within the groups}\)

Finally compute the F-ratio by using the following formula:

• F-ratio of square = Mean of the sum of squares between the groups
• Mean of the sum of squares within the groups the tabulated ’F” value for horizontal (df of between the groups) and vertical df (of within the groups) at the specified level of significance such as .05, 01, etc.
• If the calculated ‘F’ value is more than the tabulated ‘F’ value, then we reject the null hypothesis. If the calculated ‘F” value is less than the tabulated ’F‘ value, then we accept the null hypothesis.
• ’If the F‘ value is less than the tabulated ’F’ value, then we accept the null hypothesis.

“Cost of ignoring proper statistical analysis vs benefits of accurate interpretation: Q&A”

Question 17. Vancouver style of reference.
Answer:

• Vancouver style. Vancouver is a numbered referencing style commonly used in medicine and science and consists of Citations to someone else’s work in the text, indicated by the use of a number.

Vancouver is a numbered referencing style commonly used in medicine and science, and consists of:

• Citations to someone else’s work in the text, indicated by the use of a number
• A sequentially numbered reference list at the end of the document providing full details of the corresponding in-text reference
• It follows rules established by the International Committee of Medical Journal Editors, now maintained by the U.S. National Library of Medicine.
• It is also known as Uniform Requirements for Manuscripts submitted to Biomedical Journals.
• Before using this guide check with your faculty, school, or department for their specific referencing guidelines

“Success rate of interventions using modern statistical techniques: FAQ”

In-text citations:

• Insert an in-text citation:
  • when your work has been influenced by someone else’s work, for example:
    • When you directly quote someone else’s work
    • When you paraphrase someone else’s work
• General rules of in-text citation:
  • A number is allocated to a source in the order in which it is cited in the text. If the source is referred to again, the same number is used.
  • Use Arabic numerals (1,2,3,4,5,6,7,8,9)
  • Either square or curved brackets () can be used as long as it is consistent. Please check with your faculty/lecturer to see if they have a preference. For consistency in this guide, we have chosen to use round brackets for our examples
  • Superscripts can also be used rather than brackets eg….was discovered.
  • Reference numbers should be inserted to the left or inside of colons and semi-colons.
  • Reference numbers are generally placed outside or after full stops and commas – however, check with your faculty/journal publisher to determine their preference. For consistency in this guide, we are placing reference numbers after full stops.
  • Whatever format is chosen, it is important that the punctuation is consistently applied to the whole document.

Multiple works by the same author:

Each individual work by the same author, even if it is published in the same year, has its own reference number.

Citing secondary sources:

• A secondary source, or indirect citation, occurs when the ideas of one author are published in another author’s work, and you have not accessed or read the original piece of work.
• Cite the author of the work you have read and also include this source in your reference list.

In-text citation examples:

• The in-text citation is placed immediately after the text which refers to the source being cited

Using round brackets:

• As one author has put it “The darkest days were still ahead”.

Using square brackets:

• As one author has put it “the darkest days were still ahead”.

Using superscript:

• As one author has put it “the darkest days were still ahead”.
• The author’s name can also be integrated into the text
• Scholtz has argued that

“Is statistical-related risk reversible if addressed promptly? Answer provided”

Including page numbers with in-text citations:

• Page numbers are not usually included with the citation number. However, should you wish to specify the page number of the source the page/s should be included in the following format:
• As one author has put it “The darkest days were still ahead”,
• As one author has put it “the darkest days were still ahead”.
• Scholtz (1 p16-18) has argued that

Citing more than one reference at a time:

• The preferred method is to list each reference number separated by a comma, or by a dash for a sequence of consecutive numbers. There should be no spaces between commas or dashes

For example: (1,5,6-8)

Reference List:

• References are listed in numerical order, and in the same order in which they are cited in text. The reference list appears at the end of the paper.
• Begin your reference list on a new page and title it ‘References.”
• The reference list should include all and only those references you have cited in the text. (However, do not include unpublished items such as correspondence).
• Use Arabic numerals (1, 2, 3, 4, 5, 6, 7,8,9).
• Abbreviate journal titles in the style used in the NLM Catalog
• Check the reference details against the actual source indicating that you have read a source when you cite it.

The post BSc Nursing 3rd Year Nursing Research And Statistics Chapter 14 Statistics Question And Answers appeared first on BDS Notes.

Question 1. Data presentation and Interpretation.
Answer:

• Data presentation and analysis play an essential role in every field. An excellent presentation can be a deal maker or deal breaker.
• Some people make an increasable useful presentation with the same set of facts and figures that are available to others.

“Understanding data analysis methods through FAQs: Q&A explained”

“Importance of studying data analysis for BSc Nursing students: Questions explained”

Read And Learn More: BSc Nursing 3rd Year Nursing Research And Statistics Previous year Question And Answers 

1. Presentation of data is generally done by these two methods:
   1. Tabular presentation,
   2. presentation
2. Tabulated data will give some information and also allow for further analysis.
3. The columns and rows in a table make eye strain and there are chances of poor visual impression of data presented in a tabular form.
4. In such circumstances data can be presented in the form of picture, diagram or figure which will help in good comparison through good visual impression.

“Common challenges in understanding data analysis effectively: FAQs provided”

1. The graphs and diagrams are of utmost importance in creating interest from the observational data.
2. One should take care to select major data presentable in graph or diagram.
3. The presentation of data by diagram proves a very considerable aid and has much to commend it, if certain basic principles are not forgotten.
4. Main objective of diagram is to help the eye to grasp series of numbers and to grasp the meaning of series of data and also to assist intelligence.

The post BSc Nursing 3rd Year Nursing Research And Statistics Chapter 13 Analysis Of Data Question And Answers appeared first on BDS Notes.

Question 1. Define Data Collection. List The Sources Of Data Collection. Explain Any One Method Of Data Collection In Detail.
Answer:

Data Collection Definition:

• “Data are the observable and measurable facts that provide information about the phenomenon under study.
• In research studies, two types of data are collected; those are primary and secondary data.”
• “Data is any information that has been collected, observed, generated or created to validate original research findings.
• Although usually digital, research data also includes non-digital formats such as laboratory notebooks and diaries.”

“Understanding data collection methods through FAQs: Q&A explained”

Read And Learn More: BSc Nursing 3rd Year Nursing Research And Statistics Previous year Question And Answers 

The sources of data collection are generally categorized into two broad categories, they are as follows:

1. Primary sources
2. Secondary sources

1. Primary sources:

• Primary data are directly collected from the research units, which may be individuals, objects, programmes, or institutions.
• Primary sources provide the first-hand information collected by the researcher directly from the respondents or the situations, which may be collected through interviews, questioning, observation, biochemical measurements, and psychosocial measurement scales.

“Importance of studying data collection for BSc Nursing students: Questions explained”

2. Secondary sources:

• Secondary data are collected from either internal or external secondary sources. External sources involve existing materials, such as published or unpublished records.
• Published records may include journals, magazines, newspapers, government reports, statistical abstracts, census reports, constituents of mass communication, and commission reports.
• Unpublished records may include official records, patient records, thesis, dissertations, and reports.
• In addition, internal secondary sources also known as private documents include biographies, personal diaries, letters, memos, etc.

The methods of data collection are as follows:

1. Interview
2. Questioning
3. Observation
4. Biophysiologic methods
5. Other methods

“Common challenges in understanding data collection methods effectively: FAQs provided”

1. Interview Introduction:

• The interview is a process of communication or interaction in which the subjects or interviewer gives the needed information verbally in a face-to-face situation. Interviewing requires face-to-face contact or contact over the telephone and calls for interviewing skills.
• Interviews may be used either as a main method or as a supplementary one in a collection of research-related data.

Interview Definition:

“A method of data collection in which one person (interviewer) asks the questions from another person (respondent) conducted either face-to-face or telephonically.”

Characteristics of interview:

• The participants, the interviewer, and the respondent are strangers.
• The relationship between the participants is a transitory one.
• The interview is a mode of obtaining verbal answers to questions put verbally.
• The interaction between the interviewer and the respondent need not necessarily be face-to-face because interviews can be conducted over the telephone.

“Steps to explain types of data collection tools: Interviews vs observations: Q&A guide”

Benefits of interview:

1. Provide in-depth and detailed information.
2. Permits greater depth of response.
3. Data from illiterate subjects.
4. Higher response.
5. Help to gather other supplementary information.

Types of Interviews:

1. Structured Interview:

• A structured interview is a means of data collection in which the interviewer has an interview schedule in which the questions are listed in the order in which they are to be answered.

2. Unstructured Interview:

• These interviews are also known as nonstandardized interviews. An unstructured interview is a method wherein the questions can be changed to meet the respondent’s intelligence, understanding, and beliefs.

“Role of questionnaires in gathering nursing research data: Questions answered”

3. Semistructured Interview:

• A semi-structured interview is a flexible method that allows new questions to be brought up during the interview, depending upon the situation during the interview.

4. In-depth Interview:

• This is an intensive and investigative interview conducted and aimed at studying the respondent’s opinion and emotions on the basis of the interview guide.

5. Focused Group Interview:

• Group-focused interview is an unstructured group interview technique where 8-12 members are brought together under the guidance of a trained interviewer to focus on a specific phenomenon.

“How do checklists enhance accuracy in data collection? FAQ explained”

6. Telephone Interview:

• Telephone interviewing is a nonpersonal method of data collection. This method of collecting information consists of contacting respondents on the telephone itself.
• It is used widely in industrial surveys, particularly in developed regions.

The Interviewing process is as follows:

1. Preparation for interview
2. Pre-interview introduction.
3. Developing rapport
4. Carrying the interview forward.
5. Recording the interview
6. Closing the interview.

The data collection, sources of data collection and methods of data collection are given above.

Question 2. Enumerate the sources of data
Answer:

Data means information that is systematically collected in the courses of study.

Data Sources:

• Information collected from different research studies generally depends on various sources.
• However, a quality research study requires that highly reliable and valid data are collected; therefore diligence and application of the researcher can be of high importance.
• Sources of data collection in different research studies largely depend on several factors such as type of research study, phenomenon under study, purpose of the study, etc.

However, basically, sources of data are generally categorized into two broad categories, namely primary and secondary sources as discussed below:

Primary sources: Primary data are directly collected from the research units, which may be individuals, objects, programmes, or institutions.

Primary sources provide the first-hand information that is collected by the researcher directly from the respondents or the situations, which may be collected through interviews, questioning, observation, biochemical measurements, and psychosocial measurement scales.

“Early warning signs of gaps in understanding data collection basics: Common questions”

Secondary sources:

• Secondary data are collected from either internal or external secondary sources. External sources involve existing material such as published or unpublished records.
• Published records may include journals, magazines, newspapers, government reports, statistical abstracts, census reports, constituents of mass communication, and commission reports. Unpublished records may include official records, patient records, thesis, dissertations, and reports.
• In addition, internal secondary sources also known as private documents may include biographies, personal diaries, letters, memos, etc.

Question 3. Methods of Data Collections.
Answer:

Data means information that is systematically collected in the courses of study.

Methods Of Collecting Primary Data:

• Primary data are directly collected by the researcher from their original sources.
• In this case, the researcher can collect the required data precisely according to his research needs, he can collect them when he wants them and in the form he needs them. However, the collection of primary data is costly and time-consuming.
• Yet, for several types of social science research required data are not available from secondary sources and they have to be directly gathered from primary sources.
• In such cases where the available data are inappropriate, inadequate or obsolete, primary data have to be gathered.
• They include socio-economic surveys, social anthropological studies of rural communities and tribal communities, sociological studies of social problems and social institutions, nursing research, leadership studies, opinion polls, attitudinal surveys, readership, radio listening and T.V. viewing surveys, knowledge-awareness practice (KAP) studies, nursing management studies, hospital management studies, etc.
• There are various methods of data collection. A ‘Method’ is different from a ‘Tool.’ While a method refers to the way or mode of gathering data, a tool is an instrument used for the method.

“Asymptomatic vs symptomatic effects of ignoring proper data collection methods: Q&A”

For example, a schedule is used for interviewing. The important methods are

1. Observation,
2. Interviewing,
3. Mail Survey,
4. Experimentation,
5. Simulation
6. Projective technique.

To collect primary data during the course of doing experiments in experimental research but in case we do research of the descriptive type and perform surveys, whether sample surveys or census surveys, then we can obtain primary data either through observation or through direct communication with respondents in one form or another or through personal interviews.

This, in other words, means that there are several methods of collecting primary data, particularly in surveys and descriptive research.

Important ones are:

1. Observation Method,
2. Interview Method,
3. Through Questionnaires,
4. Through Schedules And
5. Other Methods Which Include
6. Warranty Cards;
7. Distributor Audits;
8. Pantry Audits;
9. Consumer Panels;
10. Using Mechanical Devices;
11. Through Projective Techniques
12. Depth Interviews, And
13. Content analysis.

• Observations involve gathering data relating to the selected research by viewing and or listening.
• Interviewing involves a face-to-face conversation between the investigator and the respondent.
• Mailing is used for collecting data by getting questionnaires completed by respondents.
• Experimentation involves the study of independent variables under controlled conditions.
• Experiments may be conducted in a laboratory or in the field in a natural setting. Simulation involves the creation of an artificial situation similar to the actual life situation.
• Projective methods aim at drawing inferences on the characteristics of respondents by presenting to them stimuli.
• Even the method has its advantages and disadvantages.

“Can targeted interventions improve outcomes using data collection tools? FAQs provided”

Question 4. Discuss on advantages and disadvantages of observation as a method of data collection.
Answer:

Method of Data Collection Observations:

• Observation is a way of gathering data by watching behaviour, and events, or noting physical characteristics in their natural setting.
• Observation is a method of data collection that can be used to gather such information as characteristics and conditions of individuals, verbal and nonverbal communication, etc.
• The observation method of data collection is particularly well suited to nursing research.
• There are several situations that require nurses to use the observation method for data collection such as the behaviour and attributes of the patients, their families, hospital staff, and so on.
• Observations can be overt (everyone knows they are being observed) or covert (they do not know that they are being observed and the observer is concealed).
• The benefit of covert observation is that people are more likely to behave naturally if they do not know that they are being observed.

Method of Data Collection Definitions:

• Observation is a technique for collecting all the data or acquiring information through occurrences that can be observed through senses with or without mechanical devices.
• It is a two-part process to collect data for study that includes an observer (someone who is observing) and the observed (there is something to observe).

Advantages of Observation:

• Collect data on where and when an event or activity is occurring.
• Does not rely on people’s willingness or ability to provide information.
• Allows you to directly see what people do rather than relying on what people say they did.

Disadvantages of Observation:

• Susceptible to observer bias.
• Susceptible to the ‘Hawthorne effect’, that is, people usually perform better when they know they are being observed, although indirect observation may decrease this problem.
• Can be expensive and time-consuming compared to other data collection
• Does not increase understanding of why people behave as they do.

Question 5. Likert Scale
Answer:

• Likert scale was named after psychologist Rensis Likert, who developed it in 1932 as a psychological concept measurement scale.
• Likert scale is one of the most commonly used scaling techniques.
• It was developed to measure the attitudes, values, and feelings of people.
• Primarily original version of this scale was developed with a five-point scale (strongly agree, agree, uncertain, disagree, and strongly disagree) containing a mixture of positive and negative declarative statements regarding measuring variables.
• An example of 5 points Likert’s scale may be perused from Table

“Difference between primary and secondary data collection methods: Q&A explained”

Likert Scale Definition:

• Likert scale is a composite measurement scale used to measure the attitude, value, and, feeling of people that involve summation of sources on the set of positive and negative declarative statements regarding measuring variables to which respondents are asked to indicate their degree of agreement or disagreement

Use Of Likert Scale:

1. Use to measure the attitudes, values, and feelings of the people’s specific concepts, such as situations, people, places, objects
2. Use to assess the opinions of respondents about a particular concept.
3. It Collects The Opinion Of People With Various Attitude, Emotions And Feelings Toward A Particular Concept.

Characteristics Of the Likert Scale:

1. Psychologic measurement tool: Is a psychologic measurement tool to assess the attitudes, and feelings of people about a specific concept.
2. Neutral statements: This must contain neutral statements.
3. Bipolar scaling method: This is composed of alternative positive and negative declarative statements so that the respondent’s response and bias can be eliminated. Here positive statement gets a high score with agreement and a negative score gets a high score with the statement.
4. Measurement of a specific number of scaling categories: This scale was originally developed with five scaling categories; but later scale even developed with four, six and seven scaling categories.

Scoring Of The Likert Scale:

• Scoring of the Likert scale is done on the bias of the type of statement and level of respondents’ agreement with the statement. For positive statements, respondents get a higher score if there is agreement with the statement. However, in the case of a negative statement, the respondent gets a higher score if there is a disagreement with the statement.

Advantages Of the Likert Scale:

1. It’s relatively easy to construct this scale.
2. It is considered a more reliable and valid tool to measure the psychosocial variables.
3. It is easy to administer since respondents only have to tick in the space provided against each statement
4. It is less time-consuming during construction and administration.

Disadvantages Of the Likert Scale:

1. In this scale, respondents may feel forced to answer the question against all preplanned items and their categories.
2. The feelings of the respondent were not fully assessed.
3. Difficulty in justifying the selection of the number of categories.
4. Difficult to treat neutral opinions as neither agree or disagree.

“Most common complications of poorly understood data collection tools: FAQs”

Question 5. Rating Scale
Answer:

• Rating is the term used to express the opinion or judgement regarding some performance of a person, object, situation, or character.
• The rating scale involves the qualitative description of a limited number of aspects of a thing or traits of a person.
• When we use rating scales we judge an object in absolute terms against some specified criteria, i.e. we judge properties of objects without reference to other similar objects.

Rating Scale Definitions:

• The Nursing Research and Statistics Rating scale refers to a scale with a set of opinions, that describes the varying degrees of the dimensions of an attitude being observed.
• The rating scale is a device by which judgements may be qualified or an opinion concerning a trait can be systematized.
• The rating scale is a tool in which one person simply checks off another person’s level of performance.
• The rating scale could be a 3-point, 5-point, or 7-point rating scale.

“Why are data collection methods often misunderstood in practice? Questions answered”

Types of the Rating Scales:

1. Graphic rating scale:

• In this scale, the performance is printed horizontally at various points from lowest to highest. It includes the numerical points on the scale.
• It is anchored by two extremes presented to respondents for evaluation of a concept or object. Graphic rating scale

For example: How much are you satisfied with noise control in your ward?

2. Descriptive rating scales: This type of rating scale does not use number, but divide the assessment into a series of verbal phrases to indicate the level of performance.

For example Q. Judge the level of performance of nursing personnel in the Medical ICU

3. Numerical rating scale: It divides the evaluation criteria into a fixed number of points, but defines only numbers at the extremes.

In these scales, each statement is generally assigned a numerical score ranging from 1 to 10 or even more.

For example: Pain assessment numerical scale.

“Cost of ignoring proper data collection vs benefits of accurate data: Q&A”

4. Comparative rating scale:

• In this type of rating scale, the researcher makes a judgement about an attribute of a person by comparing it with that of a similar person(s).
• For example, Mr. Ram’s decision-making abilities closely resemble those of Mr. Shyam and Mr. Gopal.
• In this type of rating scale, a researcher must have hands-on knowledge about the selected attributes of the people with whom the subjects are supposed to be compared.

Rating Scale Definitions

• Rating scale refers to a scale with a set of opinions, which describes varying degrees of the dimensions of an attitude being observed.
• Rating scale is a device by which judgements may be qualified or an opinion concerning a trait can be systematized.

Types of the Rating Scale:

1. Graphic rating scale.

• In this scale, the performance is printed horizontally at various points from lowest to highest.
• It includes the numerical points on the scale. It is anchored by two extremes presented to respondents for evaluation of a concept or object.

2. Descriptive rating scales:

This type of rating scale do not use number, but divide the assessment into a series of verbal phrases to indicate the level of performance.

3. Numerical rating scale:

• It divides the evaluation criteria into a fixed number of points but defines only numbers at the extremes.
• In these scales, each statement is generally assigned a numerical score ranging from 1 to 10, or even more.

4. Comparative rating scale.

• In this type of rating scale, the researcher makes a judgement about an attribute of a person by comparing it with that of a similar person(s) For example, Mr Ram’s decision-making abilities closely resemble those of Mr. Shyam and Mr. Gopal.
• In this type of rating scale, the researcher must have hands-on knowledge about the selected attributes of the people with whom the subjects are supposed to be compared

Characteristics of Rating Scale:

1. The rating scale is the judgement of one person by another
2. It is a directed observation
3. It is the opinion of some object, situation or character
4. It Systemizes The Expression Of Opinion Concerning A Trait.
5. A Systematic Procedure For Obtaining, Recording And Reporting Observer’s Judgements.
6. It Is A Subjective Method Through Which We Can Find Out Opinions About A Particular Person.
7. Similar To A Check List But With Finer Discriminations.
8. It may be filled during or immediately after or much later after the observation is made.
9. Ratings Are Done By Parents, Guardians, Teachers, Students Themselves, Friends, A Board Of Interviewers, Judges, And The Self As Well.
10. Each Statement Constructed In the Rating Scale Must Be Unique In Itself So That Attributes Can Be Judged Appropriately.

“Is data collection-related risk reversible if addressed promptly? Answer provided”

Advantages Of Rating Scale:

1. Easy To Administer And Score The Measured Attributes.
2. Rating Scales Have A Wide Range Of Applications in Nursing Research
3. Graphic Rating Scale Is Easier To Make And Requires Less Time.
4. Rating Scales Can Be Easily Used For A Large Group.
5. It Is Also Used For Quantitative Methods.
6. It May Also Be Used For The Assessment Of Interests, Attitudes, And Personnel Characteristics.
7. Used To Evaluate Performance, Skills, And Product Outcomes.
8. Rating scales are adaptable and flexible research instruments.

“Success rate of interventions using modern data collection techniques: FAQ”

Disadvantages Of Rating Scale:

1. It Is Difficult Or Dangerous To Fix Up Rating About Many Aspects Of An Individual.
2. Misuse Can Result In a Decrease In Objectivity.
3. There Are Chances Of Subjective Evaluation, and the Scales May Become Unscientific And Unreliable.
4. Limited Reliability
5. May Be Substantial Variations Among Informants
6. Do Not Assess Sources Of Behavior Problems
7. Ease Of Use – Unqualified Users May Use And Interpret These Scales.
8. Not suitable for sophisticated treatment planning

The post BSc Nursing 3rd Year Nursing Research And Statistics Chapter 12 Tools And Method Of Data Collection Question And Answers appeared first on BDS Notes.

Question 1. Research tool
Answer:

Research tool Definition

• A research tool may be defined as Anything that becomes a means of collecting information for your study is called a research tool or a research instrument. For example, observation forms, interview schedules, questionnaires, and interview guides are all classified as research tools.
• Measurements are the process of assigning numbers to variables. Measurements, as used in research, imply the qualification of information which is the assigning of some type of numbers to the data.

“Understanding research methods through FAQs: Q&A explained”

Read And Learn More: BSc Nursing 3rd Year Nursing Research And Statistics Previous year Question And Answers 

Meaning Of Tools And Methods:

• There are many alternatives to choose from when selecting a data-collection method. These methods include questionnaires, interviews, physiological measures, attitude scales, psychological tests, and observational measures.

Question 2. Enumerate the principles for research tool creation.
Answer:

“Importance of studying research tools for BSc Nursing students: Questions explained”

Question 3. Define the reliability of the measuring Instrument.
Answer:

Measuring Instrument Definition:

1. According to Chris Jordan, Reliability is the consistency of a set of measurements or measuring instruments often used to describe a test.
2. According to Houghton, Reliability is an attribute of any system that consistently produces the same results, preferably meeting or exceeding its specifications.
3. According to Livingstone, reliability is the ability of an item to perform a required function under stated conditions for a specified period of time.

Question 4. What are the factors affecting the reliability?
Answer:

1. Administrative factors:

• Poor or unclear directions given during administration or inaccurate scoring can affect reliability.
• For example: it says you were told that your scores on being social determined your promotion.

2. Number of items on the instrument:

• The larger the number of items, the greater the chance of high reliability. For example, it makes sense when you ponder that 20 questions on your leadership style are more likely to get a consistent result than four questions.

“Common challenges in understanding research methods effectively: FAQs provided”

3. The instrument taker:

• The response portrayed by the individual respondent will vary with considerable stress and much interference is faced by him during the test.
• For example, if you took an instrument in August when you had a terrible flu and then in December when you were feeling quite good we might see a difference in your response consistency.
• If you were under considerable stress of some sort or if you were interpreted, while answering the instrument questions, you might give different responses.

4. Heterogeneity of the:

• The greater the heterogeneity (differences in the kind of questions of difficulty of the questions) of the times, the greater the chance of high-reliability correlation coefficients.
• For example: if you throw a variety of questions, it is difficult to give an answer and also from various areas. Length of time between test and retest frame

5. Heterogeneity of the group members:

• The greater the heterogeneity of the group members in the preferences, skills, or behaviors being tested, the greater the chance for high-reliability correlation coefficients.
• For example, if the participants are from various professions the views will be different from one another.

6. Length of time between test and reset.

• The shorter the time, the greater the chance for high-reliability correlation coefficients. As we have experienced, we tend to adjust our views a little from time to time.
• Therefore, the time interval between experience intervals.
• Experience happens and it influences how we see things.
• Because internal consistency has no time-lapse, one can expect it to have the highest reliability correlation coefficient.
• For example, when less time is given between the test and retest regarding a particular topic the reliability will be High.

“Why is early learning of research tools critical for evidence-based practice? Answered”

Outside factors that affect reliability

1. Fatigue, illness, emotions, motivational level, attitudes, and values, all affect reliability.
2. Motivation is probably a more important factor, especially the motivation to study before taking the test.
3. A student’s personality characteristics may affect his or her test scores. Sociable persons who enjoy communicating, and who are able to express themselves fluently will also often score higher than persons who lack such characteristics.
4. Students‘ past experience with tests will influence their performance on subsequent tests.
5. The quality of students’ past experiences with tests, if they have often achieved success.
6. Students’ memory, fluency, and the like also enter into the reliability of a test. The higher these factors, the more reliable the test.
7. Lapses of inattention, boredom, distractions, poor ventilation, and the like may serve to reduce a test’s reliability to some extent.
8. In most cases, a teacher can increase reliability simply by ensuring that all students clearly understand test directions, are marking answer sheets correctly, and are not interrupted during the testing process.
9. Elimination of distractions and maximization of students‘ comfort through good lighting, ventilation, and heating can also contribute to higher reliability.

“Steps to explain types of research tools: Questionnaires vs interviews: Q&A guide”

Question 5. Types of reliability
Answer:

Test-retest reliability or stability:

1. It refers to the degree to which research participants’ responses change over time.
2. The test-retest method is used to test the stability of the tool. In this method, an instrument is given to the same individuals on two occasions within a relatively short duration of time
3. A correlation coefficient is calculated to determine how closely the participant’s responses on the second occasion matched their responses on the first occasion.

Half-split reliability or internal consistency:

1. It is a measure of reliability that is frequently used with scales designed to assess psychosocial characteristics.
2. Instruments can be assessed for internal consistency using the half-split-half technique (i.e. answers to one-half of the items are compared with answers to the other half of the items) or by calculating the alpha coefficient or using the Kuder-Richardson formula.
3. In the case alpha coefficient and the Kuder-Richarcison formula, a coefficient that ranges from 0 to 1.00 usually results.

Inter-rater reliability equivalence or the notion of equivalence

1. It is often a concern when different observers are using the same instrument to collect data at the same time.
2. A coefficient can be calculated or other statistical or nonstatistical procedures can be used to see the correlation of values.

“Role of surveys in collecting data for nursing research: Questions answered”

Question 6. Describe the methods for assessing the reliability of measuring instruments.
Answer:

Methods of determining reliability

• The most common method of determining an examination’s reliability is the test-retest method.

Kuder Richardson Approaches:

1. As explained previously, the same test is given to the same group on two separate occasions, usually a few days apart.
2. The correlation between the two tests becomes the coefficient of reliability.
3. Since rankings rather than raw scores are used to compute the correlation, the fact that most students usually do slightly better on the second test will not greatly affect the final results.
4. The real problem with this method stems from students looking up answers after the first test.
5. Some students may not look up any answers, while others may look up as many as they can remember.
6. This would reflect intervening learning and would cause their scores to rise disproportionately. likely students talk about
7. The same problem may occur when they test among themselves. We could expect that those who discuss the test would improve their scores, whereas those who do not discuss it will not improve or will improve only slightly.
8. Another way to determine reliability is split-half correlation. Each student’s test is divided in half, usually with odd-numbered items making up one half and even-numbered items making up the other half. This produces two tests for each student that is then correlated with each other.
9.  A third method is to correlate some students‘ scores on equivalent forms or two tests that cover the same material although the specific questions are different.
10. For example, the first test might contain the math problem 47 x 93, while the second contains 93 x 47.
11. It is impossible to develop two tests with different questions that are exactly equal in difficulty, but we can make them fairly close.
12. These two tests are then administered within a fairly short time period and, as before the results are correlated.
13. The problem with this method is that it costs time and money, and it is hard to ensure that the two tests are equal but different.

“How do observational methods enhance research accuracy? FAQ explained”

Question 7. How the Researcher can increase reliability.
Answer:

• The various methods of determining reliability can never yield precise measures of a test’s unreliability.
• Correlation coefficients are only estimates or, more precisely, averages. For a teacher, the most important concern is how to increase reliability.

The following causes of unreliability should be avoided:

1. The sample of test items is skewed:

• That is, Some material only briefly mentioned in class is made an important part of the test, while other material extensively covered in class is only briefly touched on.
• If a test is not well balanced and does not test material in proportion to the emphasis it was given in class or in the text, reliability will be lowered.
• To remedy this situation, a table of specifications should be used to balance the test.

2. The wording of instructions and questions is ambiguous.

• This common problem may cause students to misinterpret the intent of a question or a portion of the test.
• Ambiguity may be reduced by revising test questions in accordance with feedback 3 from other teachers or based on the results of previous use, either in a practice test or with other classes.

3. Too many difficult items can lead students to do a great deal of guessing.

• Chance then plays an important role in the final scores. The more difficult a test is, the more its final scores are affected by chance.
• To solve this problem, the test should be made easier. The average difficulty level for a non-mastery test should be about 0.50.

4 . A short test usually has lower reliability.

• Probably the easiest way to increase a test’s reliability is by lengthening it.
• An average test that contains only 10 questions often has very little reliability, whereas a test of 70 questions of equal quality is likely to have greater reliability.
• The Larger the sample, all other things being equal, the more accurate the assessment of student’s level of achievement usually is.

“Early warning signs of gaps in understanding research tools: Common questions”

1. Restrictive time limits can force hasty reading and responding, which affect students’ final scores.
2. Students should generally be given plenty of time to finish a test. This ensures that they answer thoughtfully and minimizes quick guesses.

Question 8. Describe the pilot study in terms of its definition and importance.
Answer:

Pilot study in terms of Meaning:

• It is a small preliminary investigation of the same general character as the major study which is designed to acquaint the researcher with problems that can be corrected in proportion to the larger research project or is done to provide the researcher with an opportunity to try out the procedures for collecting data.
• The pilot study is a miniature trial run of the methodology planned for the major project.
• It is a time for detecting errors and flaws being made in the instrument for gathering data. Then when the actual study is carried out the researcher can profit from the mistakes made in the pilot study.

A pilot study in terms of Definition:

1. According to Julie Stachowiak, A pilot study is a smaller version of a larger study that is conducted to prepare for that study.
2. According to Short and Pigeon, Pilot studies are small-> scale rehearsals of larger data collections.
3. According to Gordon Marshall, A Pilot study is any small-scale test of a research instrument (such as a questionnaire experiment or interview schedule), run in advance of the main fieldwork and used to test the utility of the research design.

“Success rate of interventions using modern research tools: FAQ”

A pilot study in terms of Importance:

1. It tells us about the completeness, accuracy, and convenience of the sampling frame from which it is proposed to select the sample.
2. It unfolds the variability within the population to be surveyed. It is important for determining the sample size.
3. It helps in bringing out the inadequacies of the draft questionnaire.
4. Throws light on several difficulties in the study.
5. Shows the effectiveness of the training of the personnel or collecting data.
6. Allays the interviewer’s fear of overtly sensitive questions and builds their self-confidence.
7. Tests the interviewer’s stamina to work under conditions of personal discomfort, stress, and fatigue.
8. Tests the efficiency of the survey organization in the field.
9. Helps in identifying the needs for different kinds of equipment, and vehicles necessary during the project.
10. Provides data for making estimates of time and costs for completing various phases of the project and shows ways to effect savings.
11. The scale of the pilot study will depend on the availability of resources like time, money, and personnel.
12. Provides for initial analysis of the adequacy of the questionnaire and the training instructions and supervision under field conditions.
13. Aids to identify parts of the instrument package that are difficult for pretest subjects.
14. Helps to identify objectionable or offensive questions.
15. Assesses the sequencing of an instrument to be sensible.
16. Determines the need for training of data collection staff.
17. Determines whether the measurement yields data with sufficient variability.

“Asymptomatic vs symptomatic effects of ignoring proper research tools: Q&A”

Question 9. Define validity and types of validity.
Answer:

• The validity of an instrument refers to the degree to which an instrument measures what it is supposed to be measuring.
• For example, a temperature-measuring instrument is supposed to measure only the temperature; it cannot be considered a valid instrument if it measures an attribute other than temperature.
• Similarly, if a researcher developed a tool to measure the pain, and if it also includes the items to measure anxiety, it cannot be considered a valid tool.
• Therefore, a valid tool should only measure what it is supposed to be measuring.

Validity Definitions:

• According to Treece and Treece, ‘Validity refers to an instrument or test actually testing what it suppose to be testing’.
• According to Polit and Hungler, ‘Validity refers to the degree to which an instrument measures what it is supposed to measure’.
• According to the American Psychological Foundation, ‘Validity is the appropriateness, meaning, fullness, and usefulness of the interference made from the scoring of the instrument’. Validity is the appropriateness, completeness, and usefulness of an attribute-measuring research instrument.

“Why are research tools often misunderstood in practice? Questions answered”

Types of Validity

Basically, validity is classified into the following four categories:

Face validity:

• Face validity involves an overall look of an instrument regarding its appropriateness to measure a particular attribute or phenomenon.
• Though face validity is not considered a very important and essential type of validity for an instrument.
• However, it may be taken into consideration while assessing other aspects of the validity of a research instrument. In simple words, this aspect of validity refers to the face value or the outlook of an instrument.
• For example, a Likert scale is designed to measure the attitude of the nurses towards the patients admitted with HIV/AIDS; a researcher may judge the face value of this instrument by its appearance, that is it looks good or not; but it provides no guarantee about the appropriateness and completeness of a research instrument with regards to its content, construct, and measurement score.

Content validity:

• It is concerned with the scope of coverage of the content area to be measured.
• More often it is applied in tests of knowledge measurement. It is mostly used in measuring complex psychological tests of a person.
• It is a case of expert judgment about the content area included in the research instrument to measure a particular phenomenon.
• Judgment of the content viability may be subjective and is based on previous researcher’s and experts’ opinions about the adequacy, appropriateness, and completeness of the content of the instrument.
• Generally, this viability is ensured through the judgments of experts about the content.

“Most common complications of poorly understood research methods: FAQs”

“Can targeted interventions improve outcomes using research tools? FAQs provided”

Criterion validity: This type of validity is a relationship between measurements of the instrument with some other external criteria.

• For example, a tool was developed to measure the professionalism among nurses; to assess the criterion validity nurses were separately asked about the number of research papers they published and the number of professional conferences they have attended.
• Later a correlation coefficient is calculated to assess the criterion validity.
• This tool is considered strong with criterion validity if a positive correlation exists between a score of the tool measuring professionalism and the number of research articles published and professional conferences attended by the nurses.
• The instrument is valid if its measurements strongly respond to the score of some other valid criteria.
• The problem with criterion-related validity is finding a reliable and valid external criterion.
• Mostly we are to rely on a less-than-perfect criterion because the rating found by empirical and supervisory methods may be computed mathematically,
• which can correlate the score of the instrument with the scores of the criterion variable. Here the range of coefficient &62;70 is desirable.
• Criterion-related validity may be differentiated by predictive and concurrent validity.

Predictive validity: It is the degree of forecasting judgment; for example, some personality tests on the academic futures of students can be predictive of behavior patterns.

• It is the differentiation between performances on some future criterion and instruments’ ability. An instrument may have predictive validity when its score significantly correlates with some future criteria.

“Difference between qualitative and quantitative research tools: Q&A explained”

Concurrent validity: It is the degree of the measures at present. It relates to the present specific behavior and characteristics; hence the difference between predictive and concurrent validity refers to the timing pattern of obtaining measurements of a criterion.

Construct validity:

• A construct is founded in this type of validity, such as a nurse may have designed an instrument to measure the concept of pain in amputated patients.
• The pain pattern may be due to anxiety; hence the results may be misleading. Construct validity is a key criterion for assessing the quality of a study, and construct validity has most often been addressed in terms of measurement issues.
• The key construct validity questions with regard to measurements are: What is this instrument really measuring? Does it adequately measure the abstract concept of interest?
• Construct validity gives more importance to testing relationships predicted on theoretical measurements.
• The researcher can make predictions in relation to another such types of constructs. One method of construct validation is known group technique.

The post BSc Nursing 3rd Year Nursing Research And Statistics Chapter 11 Research Tool And Methods Question And Answers appeared first on BDS Notes.

Question 1. Advantages And Disadvantages Of Sampling.
Answer:

The advantages of sampling are as follows:

The economy in expenditure:

If the data are collected for the entire population, the cost will be very high. It is economical of cost when the data are collected from a sample that is only a fraction of the population i.e., sampling helps to reduce the cost of the research.

The economy in time:

• The use of sampling is economical for a time. Sampling is less time-consuming than the census technique.
• Tabulation, analysis, etc. also takes much less time in the case of a sample than in the case of a population. That means sampling helps greater speed in the project.

Read And Learn More: BSc Nursing 3rd Year Nursing Research And Statistics Previous year Question And Answers 

“Understanding sampling methods through FAQs: Q&A explained”

Greater scope:

• In many fields of inquiry (For example, quality control tests) where the complete destruction of the product is involved, a cent percent test production is impossible, and often, impracticable, but they require highly trained personnel and sophisticated equipment.
• Sample simplifies things and personnel with little training can collect and handle data. There is a greater scope and flexibility of studies when a sample is used.

Greater accuracy:

• Sampling ensures completeness and a high degree of accuracy due to a limited area of operation.
• In dealing with a sample, the volume of work is reduced, therefore careful execution of fieldwork is possible.
• This processing of the data is also done more accurately, which in turn produces better results.

“Importance of studying sampling for BSc Nursing students: Questions explained”

Organization of convenience:

• Sampling involves very few organizational problems. Due to small numbers, it does not require vast facilities. It is economical with respect to resources.
• The space and equipment required for this study are very small.

Intensive and exhaustive data:

• As the number is limited, it is possible to collect intensive and exhaustive data.

Suitable in limited resources:

• In every society, there are more problems and fewer resources, particularly when the people are poor and problems are uncountable. This is the method which enables the researcher to work even with limited resources.

“Common challenges in understanding sampling techniques effectively: FAQs provided”

Better rapport:

• It is very difficult to develop a rapport with a large number of people, but it is possible to develop a better rapport with the respondents/subjects.

Disadvantages of Sampling Chances of bias:

• Sampling may involve biased selection and thereby lead to drawing erroneous conclusions, which may be due to various reasons.

Difficulty in getting representative supply:

• Selection of a truly representative sample is very difficult particularly when the phenomena under study are of a complex nature.

“Steps to explain types of sampling: Probability vs non-probability: Q&A guide”

Need for specialized knowledge:

• In the absence of specialized knowledge, the investigator may commit serious mistakes.
• So it requires specialized knowledge of sampling techniques, statistical analysis, and calculation of probable error.

The changeability of units:

• The cases of the sample may be widely dispersed since many refuse to cooperate some may be inaccessible and sometimes the selected cases may have to be replaced by others. All these introduce a change in the stipulated subjects to be studied.

Impossibility of sampling:

• Sometimes the universe is too small, or too heterogeneous, it is not possible to drive a representative sample. In such cases, supply is not required.

“Why is early learning of sampling critical for evidence-based practice? Answered”

Question 2. Characteristics of a Good Sample
Answer:

1. A good sample is one that, within restrictions imposed by its size, will reproduce the characteristics of the population with the greatest possible accuracy.
2. It should be free from error due to bias or due to deliberate selection of the unit of the sample.
3. It should be free from random sampling errors.
4. There should not be any substitution of the originally selected unit for some other more convenient in any way.
5. It should not suffer from incomplete coverage of the units selected for the study i.e., it should not ignore the failure in the sample in responding to the study.
6. Relatively small samples properly selected may be much more reliable than large samples poorly selected.
7. But at the same time, it is essential that the sample is adequate in size so that it can become more reliable.
8. In the sample, only such units should be included, which as far as possible, are independent
9. While constructing a sample, it is important that measurable or known probability sample techniques are used. This will substantially reduce the likely discrepancies.

“Role of random sampling in ensuring unbiased results: Questions answered”

Question 3. Types of Sampling
Answer:

• There are two main sampling methods for quantitative research: probability and nonprobability sampling.

Probability sampling:

• A theory of probability is used to filter individuals from a population and create samples in probability sampling.
• Participants of a sample are chosen by random selection processes. Each member of the target audience has an equal opportunity to be selected in the sample.

There are four main types of probability sampling

1. Simple random sampling:

• As the name indicates, simple random sampling is nothing but a random selection of elements for a sample.
• This sampling technique is implemented where the target population is considerably large.

2. Stratified random sampling:

• In the stratified random sampling method, a large population is divided into groups (strata) and members of a sample are chosen randomly from these strata. The various segregated strata should ideally not overlap one another.

3. Cluster sampling:

• Cluster sampling is a probability sampling method using which the main segment is divided into clusters, usually using geographic and demographic segmentation parameters.

4. Systematic sampling:

• Systematic sampling is a technique where the starting point of the sample is chosen randomly and all the other elements are chosen using a fixed interval. This interval is calculated by dividing the population size by the target sample size.

“Early warning signs of gaps in understanding sampling basics: Common questions”

“How does stratified sampling improve representativeness? FAQ explained”

Non-probability sampling:

• Non-probability sampling is where the researcher’s knowledge and experience are used to create samples.
• Because of the involvement of the researcher, not all the members of a target population have an equal probability of being selected to be a part of a sample.

There are five non-probability sampling models:

1. Convenience Sampling:
   • In convenience sampling, elements of a sample are chosen only due to one prime reason: their proximity to the researcher. These samples are quick and easy to implement as there is no other parameter of selection involved.
2. Consecutive Sampling:
   • Consecutive sampling is quite similar to convenience sampling, except for the fact that researchers can choose a single element or a group of samples and conduct research consecutively over a significant time period and then perform the same process with other samples.
3. Quota Sampling:
   • Using quata sampling researchers can select elements using their knowledge of target traits and personalities to form strata. Members of various strata can then be chosen to be a part of the sample as per the researcher’s understanding. Snowball Sampling
4. Snowball Sampling:
   • Snowball sampling is conducted with target audiences who are difficult to contact and get information. It is popular in cases where the target audience for research is rare to put together.
5. Judgmental Sampling:
   • Judgmental sampling is a non-probability sampling method where samples are created only on the basis of the researcher’s experience and skill.

“Asymptomatic vs symptomatic effects of ignoring proper sampling methods: Q&A”

Question 4. Explain the Simple random sampling
Answer:

• It is a probability sampling procedure in which the required number of sampling units are selected at random from the population in such a manner that each population element has an equal chance (probability) of being selected for the sample.
• Each choice of a sampling unit must be independent of all other choices. One of the most acceptable methods for selecting a simple random sample is to use a table of random numbers, which can be either computer-generated or found in a statistics textbook.
• The numbers in a random-number table have been generated in such a way that there is no pattern.
• The same probability exists that any digit will follow any other digit, and each selection is an independent choice.
• To obtain a simple random sample, first list each of the population elements, then assign consecutive numbers to each of these elements.
• Then, referring to the table of random numbers, arbitrarily start at any point in the table and proceed in any direction to identify enough tabled numbers to associate with the population elements until the desired sample has been selected.

Simple random sampling Merits:

1. This method requires minimum knowledge about the population in advance which is needed in the case of purposive sampling
2. The method is free from classification errors
3. Sampling errors can be easily computed and the accuracy of the estimate easily be assessed.

Simple random sampling Demerits:

1. This method does not make use of the knowledge about the population which the researcher may have.
2. The size of the sample required to ensure statistical reliability is usually large under stratified sampling.
3. From the point of view of field surveys, it has been claimed that cases selected by random sampling tend to be too widely dispersed geographically and that the time and cost of collecting data becomes too large.
4. The use of simple random sampling necessitates a completely cataloged universe from which to draw the sample.

However, it is often difficult for the investigator to have up-to-date lists of all the elements of the population to be sampled.

“Can targeted interventions improve outcomes using sampling knowledge? FAQs provided”

Question 5. Explain the Stratified Random Sampling
Answer:

• This is an improved type of random or probability sampling. In this method, the population is subdivided into homogeneous groups or strata, and from each stratum, a random sample is drawn.
• For example, university students may be divided on the basis of discipline, and each discipline group may again be divided into juniors and seniors; the employees of a business undertaking may be divided into managers and nonmanagers and each of those two groups may be sub-divided into salary-grade-wise strata.

Stratification is necessary for

1. Increasing a sample’s statistical efficiency,
2. Providing adequate data for analyzing the various sub-populations, and
3. Applying different methods to different strata.

• Stratification ensures the representation of all relevant sub-groups of the population. It is thus more efficient statistically than simple random sampling.
• Stratification is essential when the researcher wants to study the characteristics of population sub-groups, e.g., male and female employees of an organization.
• Stratification is also useful when different methods of data collection, etc. are used for different parts of the population e.g., interviewing for workers and self-administered questionnaires for executives.

“Differential applications of systematic vs cluster sampling: Questions answered”

Question 6. Explain Snowball Sampling
Answer:

• It is also known as nominated sampling, which is a non-probability sampling procedure Study subjects are asked to provide referrals to other study subjects.
• In this method of sampling, investigators identify individual respondents whom they believe to have pertinent information related to their study.
• They then ask these individuals to name (nominate) others who might be able to provide further information; these respondents, in turn, are then asked to name other potential respondents.
• This sampling technique is also termed network sampling or link-tracing sampling.
• This is the colorful name for a technique of building up a list or a sample of a special population by using an initial set of its members as informants.
• For example, if a researcher wants to study the problem faced by Indians through some source like the Indian Embassy.
• Then he can ask each one of them to supply names of other Indians known to them and continue this procedure until he gets an exhaustive list from which he can draw a sample or make a census survey.
• This sampling technique may also be used in socio-metric studies. For example, the members of a social group may be asked to name the persons with whom they have social contacts, each one of the persons so named may also be asked to do so, and so on.
• The researcher may thus get a constellation of associates and analyze it.

Snowball Sampling Advantages:

• It is very useful in studying social groups, informal groups in a formal organization, and the diffusion of information among professionals of various kinds
• It is useful for smaller populations for which no frames are readily available.

Snowball Sampling Disadvantages:

• It does not allow the use of probability statistical methods. The elements included are dependent on the subjective choice of the originally selected respondents.
• It is difficult to apply this method when the population is large.
• It does not ensure the inclusion of all elements in the list.

“Difference between probability and non-probability sampling in nursing: Q&A explained”

Question 7. Define Sampling and Explain probability sampling ratified random sampling techniques.
Answer: 

Sampling Definition:

• Sampling is a process of selecting representative units from an entire population.

Probability Sampling Introduction:

• It is one technique of sampling, it is based on the theory of probability.
• It involves random selection of the elements from the population.

“Success rate of interventions using modern sampling techniques: FAQ”

Techniques of probability sampling:

1. Simple random sampling
2. Stratified random sampling
3. Systemic random sampling
4. Cluster/multistage sampling
5. Sequential sampling

1. Simple Random Sampling:

In this, every member of the population has an equal chance of getting selected. In this, the sampling used a random sampling technique. Due to the random selection, there is an absence of systemic bias. Random selection either by a lottery, random table, or computer.

Simple Random Sampling Advantages:

• The most reliable and unbiased method
• Requires minimum knowledge of the study population
• Free from sampling errors

“Cost of ignoring proper sampling vs benefits of accurate sampling: Q&A”

Simple Random Sampling Disadvantages:

• Need to date complete list of all members of the population
• Expensive and time-consuming

2. Stratified Random Sampling:

Dividing heterogeneous populations into strata based on selected traits such as age, gender, and habitat, and then random selection of samples from each stratum.

Stratified Random Sampling Advantages:

• Ensures a representative sample in a heterogeneous population.
• Comparison is possible in two groups.

“Why are sampling methods often misunderstood in practice? Questions answered”

Stratified Random Sampling Disadvantages:

• Requires complete information on the population
• A large population is required
• Chances of faulty classification of strata

Systematic Random Sampling:

Selecting every Kth case from the group, such as every 10th person on a patient list or 100th person

Systematic Random Sampling Advantages:

• Convenient and simple to carry out
• Distribution sample over the entire population

Systematic Random Sampling Disadvantages:

• Less representative sample if subjects are non-randomly distributed
• Sometimes may result in a biased sample

4. Cluster Or Multistage Sampling:

In very large populations random selection of geographic cluster and then random selection of subjects from the cluster. When the population is very large such as “In Asia” random selection of geographic cluster

“Most common complications of poorly understood sampling concepts: FAQs”

Cluster Or Multistage Sampling Advantages:

• Cheap, quick, and easy for a large population
• Population parameters of population can be estimated for sample size

Cluster Or Multistage Sampling Disadvantages:

• Possibility of high sampling error
• Chances of least presentative sample due to over-represented or under-represented cluster

5. Sequential Sampling:

• The investigator initially selects a small sample and tries to make inferences;if not able to draw results, he/she then adds subjects until clear-cut inferences can be drawn. Sample size is not fixed continue till inferences are drawn

Sequential Sampling Advantages:

Study on the best possible smallest sample Facilitates inferences of study

Sequential Sampling Disadvantages:

Not possible to study a phenomenon that needs to be studied to one point in time Requires is repeated entry into the field to collect the sample

The post BSc Nursing 3rd Year Nursing Research And Statistics Chapter 10 Sampling Question And Answers appeared first on BDS Notes.

Question 1. Definition research design
Answer:

• Research design is a blueprint for conducting the study that maximizes control over factors that could interfere with the validity of findings.
• The research design guides the researcher in planning and implementing the study in a way that is most likely to achieve the intended goal.
• The control provided by the design increases the probability that the study results are accurate reflections of reality.
• Skill in selecting and implementing research design is important to improving the quality of the study and thus the usefulness of findings.

Research Design Definition:

• Research design is a blueprint for conducting the study that maximizes control over factors that could interfere with the validity of the finding.

Read And Learn More: BSc Nursing 3rd Year Nursing Research And Statistics Previous year Question And Answers 

“Understanding research design through FAQs: Q&A explained”

Question 2. List the type of research design. Explain nonexperimental research design in detail.
Answer:

Quantitative Research Design

• In Quantitative Research Design, a researcher examines the various variables while including numbers as well as statistics in a project to analyze its findings.
• The use of graphics, figures, and pie charts is the main form of data collection measurement and meta-analysis (it is information about the data by the data).

Qualitative Research Design

• This type of research is quite contrary to quantitative research design.
• It is explanatory in nature and always seeks answers to “What’s” and “How’s”. It mainly focuses on why a specific theory exists and what would be the respondent’s answer to it.
• This allows a researcher to draw a conclusion with proper findings.
• Case studies are mainly used in Qualitative Research Design in order to understand various social complexities.

Nonexperimental Research Design:

• Nonexperimental research design is one of the broad categories of research designs in which the researcher observes the phenomena as they occur naturally and no external variables are introduced.
• It is a research design in which neither the variables are deliberately manipulated nor is the setting controlled.
• In nonexperimental research, researchers collect data without making changes or introducing treatments.
• Data obtained are analyzed and the results may lead to the formation of a hypothesis that can then be tested experimentally.
• Within a quantitative framework, the observations are represented by numbers that can be statistically analyzed.
• Areas of research interest that have not been examined in depth may be best understood by conducting a descriptive study.
• Data in nonexperimental research are generally collected through the use of questionnaires, interviews, observations, literature reviews, and critical incident techniques.

“Importance of studying research design for BSc Nursing students: Questions explained”

Need of Nonexperimental Design:

• Nonexperimental research design is frequently used by nurse researchers.

Some of the study situations where only nonexperimental designs can be used to conduct a study are as follows:

• The studies in which the independent variables cannot be manipulated.
• The studies in which it is unethical to manipulate the independent variable, i.e., manipulation may cause physical or psychological harm to subjects.

The studies or research situations where it is not practically possible to conduct experiments.

• Descriptive-type studies that do not require any experimental approaches. most suitable to improve the understanding of the nonexperimental research designs.
• Descriptive design Univariant descriptive design
• Exploratory descriptive design
• Comparative descriptive design
• Correlational/Ex-post facto design
• Prospective design

Retrospective design Developmental research design

• Cross-sectional design Longitudinal design Epidemiological designs.

Case-control studies:

• Cohort studies
• Survey research design

“How does research design guide nursing studies? FAQ answered”

Question 3. Factors Affecting Research Design
Answer:

Factors Affecting Research Design:

• When a researcher has formulated a research problem, he/she has to focus on developing a good design for solving the problem.
• A good design is one that minimizes bias and maximizes the reliability of the data.
• It also yields maximum information, gives a minimum experimental error, and provides different aspects of a single problem.
• A research design depends on the purpose and nature of the research problem.
• Thus, one single design cannot be used to solve all types of research problems, i.e., a particular design is suitable for a particular problem.

A research design usually consists of the following factors:

1. Availability of scientific information
2. Availability of sufficient data
3. Time availability
4. Availability of the money
5. Manpower availability
6. The magnitude of the management problem
7. Degree of Top management’s support
8. Ability, knowledge, skill, technical understanding and technical background of the Researcher
9. Controllable variables
10. Un-controllable variables
11. Internal variables
12. External variables.

“Common challenges in understanding research design effectively: FAQs provided”

Question 4. Explain the Experimental research design
Answer:

Experimental Research Designs:

• Experimentation is the most scientifically sophisticated research method.
• It is defined as ‘observation under controlled conditions’ Experimental research differs from nonexperimental design in one important aspect.
• The researcher using an experimental design is an active agent rather than a passive observer.)
• Experimental research designs are concerned with the examination of the effect of the independent variable on the dependent variable, where the independent variable is manipulated through treatment or intervention(s), and the effect of these interventions is observed on the dependent variable.
• All the experimental researches have a common characteristic, that is, manipulation of independent variables, but a true experiment also consists of the principles of randomization and control.
• The application of control is difficult when studies are conducted in natural settings on human subjects.
• Therefore, in nursing, experimental research design is not feasible to use, and quasi-experimental, pre-experimental, or nonexperimental studies are more frequently conducted.
• According to Riley, Experimental research design is a powerful design for testing hypotheses of causal relationships among variables.
• Ideally in the experimental design, the investigator throws in a sharp relief of explanatory variables in which he is interested, controlling and manipulating the independent variable and observing its effect on the dependent variable, and minimizing the effect of extraneous variables, which might confound his results.
• Experimental research design is further classified into true experimental designs, quasi-experimental designs, and preexample research designs.

“Why is early learning of research design critical for evidence-based practice? Answered”

Question 5. Elements of Experimental Designs
Answer:

True Experiment Research Design:

• The true experimental design is one in which the researcher has a great deal of control over the research situation only with the use of true experimental design can causality interfere with any degree of confidence with these types of design, the researcher can have some confidence that the independent variables were the cause of the change in the dependent variable.

Criteria for true experimental research design:

1. Manipulation:

• The term manipulation means that the independent or experimental variable is controlled by the researcher.
• The researcher has control over the type of experimental treatment that is administered and who will receive the intervention.

2. Control:

• The second criterion for the true experimental design is the use of a comparison or control group.
• A control group usually indicates a group in an experimental study that doesn’t receive the intervention /treatment.

3. Randomization:

• The third criterion for true experimental studies is the random assignment of subjects to groups.
• Random assignment ensures that each subject has an equal chance of being selected into any of the groups in the experimental study.

Question 6. Give the characteristics of true research design.
Answer:

Characteristics Of True Experimental Design:

• True experimental research designs are those where researchers have complete control over the extraneous variables and can predict confidently that the observed effect on the dependable variable is only due to the manipulation of the independent variable.

A true experimental research design must essentially consist of the following three characteristics:

1. Manipulation,
2. Control, and
3. Randomization.

Manipulation:

1. Manipulation refers to conscious control of the independent variable by the researcher through treatment or intervention(s) to observe its effect on the dependent variable.
2. It is a conscious act by the researcher, where he or she varies the independent variable and observes the effect that manipulation has on the dependent variable of interest.
3. For example, a researcher is conducting a study on the efficacy of chlorhexidine mouthwash in the prevention of ventilator-associated pneumonia (VAP) among patients admitted to ICUs.
4. In this example, chlorhexidine mouthwash is the independent variable, which is manipulated by the researcher and is used as an intervention for the experimental group, while the control group is kept deprived of it to observe its effect on the incidence of VAP.

Control:

1. Control is another essential element of true experimental design.
2. Control refers to the use of a control group and controlling the effects of extraneous variables on the dependent variable in which the researcher is interested.
3. The subjects in the control and experimental groups are similar in number and characteristics, but the subjects in the control group receive no experimental treatment or intervention at all.
4. The experimental group receives the planned treatment or intervention, and a comparison is made with the control group to observe the effect of this treatment or intervention.
5. However, generally in health care and nursing research, it is not ethically feasible to keep a control group deprived of interventions; however, existing conventional methods of interventions may be compared with experimental interventions.
6. The control of the effects of extraneous variables on the dependent variables can be ensured by using several conventional measures, but none of them is very convenient to use.

“Factors influencing success with research design knowledge: Q&A”

Some of these measures can, however, control extraneous variables, which include:

1. Matching
2. Counterbalancing
3. Homogeneity by statistical test

Randomization:

1. Randomization means that every subject has an equal chance of being assigned to an experimental or control group.
2. This is also known as random assignment of subjects, which involves the placement of study subjects on a random basis.
3. Through random assignment of the subject under experimental or control group, the chances of systemic bias are eliminated.
4. Randomization is used in true experimental research designs to minimize the threat to the internal validity of the study and to eliminate the effect of extraneous variables on dependent variables.
5. Through randomization, on average the characteristics of the subjects in the experimental and control groups are similar; thus influence of extraneous variables on the dependent variable is eliminated by dispersing the variability of the subject characteristics equally in both groups.

Question 7. Discuss the Strengths and Limitations ( Advantages and disadvantages) of Experimental research design.
Answer:

Strength Advantages:

• Experimental research designs are considered the most powerful designs to establish the causal relationship between independent and dependent variables.
• Where the purpose of research is an explanation, causal relationships may be established among the variables by experimentation, especially in studies involving physical objects, where the variables are more easily controlled than in human studies.
• In these studies, the controlled environment in which the study is conducted can yield a greater degree of purity in observation.
• Conditions not found in a natural setting can be created in an experimental setting, where the independent variable is manipulated by the investigator.
• In the experimental approach, we can often create conditions in a short period of time that may take years to occur naturally.
• For example, in genetic studies, we can breed strains in a very short time, which would take a long time in nature to occur.
• When the experiment is conducted in a laboratory, experimental unit, or other specialized research setting, it is removed from the pressure and problems of real-life situations and the researcher can pursue his or her studies in a more leisurely, careful, and concentrated way.

Strength Disadvantages:

• Most of the time, the results of experimental research designs cannot be replicated in studies conducted on human beings due to ethical problems.
• For certain research problems, because of the danger to the physical and psychological health of the human subjects, it is not possible to conduct experiments on human beings.
• Many of the human variables neither have valid measurable criteria nor instruments to measure them.
• For example, patient welfare or level of wellness cannot be measured on any scale or by any instrument.
• In these situations, if a refined experimental design is used, there may be a mismatch between the research design and the variable-measuring instruments.
• In experimental studies conducted in natural settings like hospitals or communities, it is not possible to impose control over extraneous variables.
• Experiments are often impractical when the effect of an independent variable may require a lengthy period of time before it can emerge as a response to the criterion measures.
• This situation exists for many of the variables in nursing.
• One of the main drawbacks to conducting experiments on the effects of nursing care on acutely ill, hospitalized patients is that the patients are discharged from hospitals in such short periods of time that there is little opportunity to study the effects as they occur.
• Only by a difficult and costly procedure, these discharged patients can be followed in their homes to observe the experimental results. However, it is not always possible because of several constraints.

“Steps to explain types of research design: Experimental vs non-experimental: Q&A guide”

Question 8. Steps in Experimental Design
Answer:

The major Steps in Experimental Design are as follows,

1. Delineate the population or universe to be studied (i.e., the set of subjects or objects that share a common observable characteristic).
2. Select a sample from the population by random sampling.
3. By random assignment, subdivide the sample into two sub-samples.
4. Specify one sub-sample, the experimental group, and another the control group.
5. Before introducing the independent variable, observe and record all important characteristics of the two groups.
6. Introduce the independent variable into the experimental group but withhold it from the control group.
7. After introducing the independent variable, observe the dependent variable in both the experimental and control groups.
8. Compare the changes that occur in the experimental group with those that may have occurred in the experimental group with those that may have occurred in the control group.
9. Record the difference
10. Compare these values with statistically computed values that judge the significance of the difference, and indicate whether or not the observed differences could have occurred by chance.

Question 9. Types of Experimental Design
Answer:

The investigator planning an experiment has many experimental design options to choose from.

Experimental designs fall into three major categories which are:

1. True or classical experimental design.
2. Quasi-experimental design.
3. Pre-experimental design.

The most commonly used in nursing studies are discussed as nomenclature used and definitions developed by Campbell and Stanley (1963) as follows.

True or classical experimental design: There are three major designs in the true experimental design, viz.

1. Pre-test and post-test control group design
2. Solomon Four-group design
3. After/Post-test-only experimental design.

“Role of research design in ensuring validity and reliability: Questions answered”

Question 10. Characteristics Of Research Design
Answer:

Characteristics Of Research Design:

• Design research investigates the process of designing in all its many fields. It is thus related to Design methods in general or for particular disciplines.
• A primary interpretation of design research is that it is concerned with undertaking research into the design process.
• Secondary interpretations would refer to undertaking research within the process of design.

Characteristics of research design:

1. Objectivity: The findings obtained by the research should be objective. It is possible by allowing more than one person to agree between the final scores/ conclusion of the research.

2. Reliability: If similar research is carried out time and again in a similar setting it must give similar results. So the researcher must frame the research questions to make it reliable and provide similar outcomes.

3. Validity: Any measuring device can be said to be valid if it measures what it is expected to measure and nothing else. To make a research valid the questionnaire framed before research must be framed accordingly.

4. Generalization: The information collected from a given sample must be utilized for providing a general application to the large group from which the sample is drawn.

Question 11. Types of the Quantitative Research Design.
Answer:

“How does research design impact data collection methods? FAQ explained”

Question 12. Steps of the quantitative research process.
Answer:

• Quantitative research is a formal, objective, and systemic process for generating information about the world. Quantitative research is conducted to describe new situations, events, or concepts.
• The research process starts with the identification and formulation of the research problem and ends with the dissemination of research findings.

1. Formulation of the research problem
2. Determining study objectives
3. Review of literature
4. Developing conceptual framework
5. Formulating hypothesis/assumptions
6. Selecting research approach /design
7. Specifying the population
8. Developing tool (s) for data collection
9. Establishing ethical consideration
10. Conducting pilot study/ tryout of tool
11. Sample selection
12. Data collection
13. Preparing data for analysis
14. Analysis and interpretation of data
15. Disseminating the research findings

“Early warning signs of gaps in understanding research design basics: Common questions”

1. Formulation of research problem

• The foremost step of a research process is to discover an interesting and Research-worthy problem.
• It is such an important step that the whole process can get wasted if the research problem is not clearly understood.
• Therefore any good research needs a good research problem.

The research problem must ensure features of originality, Solvability, and feasibility of research problem as well as the need to take into account its following dimensions.

• Substantive dimension: Is the research problem clinically or theoretically important?
• Methodological dimension: What is the best possible way to study his problem?
• Practice dimension: Are adequate resources available to conduct the study?
• Ethical dimension: Would this problem respect the rights of the study subject?

2. Determining study objectives:

• There must be a clear direction to every research prom and objectives certainly serve this purpose.
• Therefore, researchers must have a list of objectives for the study, which provides guidelines for the researcher as to what exactly must be done during the course of a particular research study.
• There may be general and specific objectives for a research project.
• This step of the research process also includes writing operational definitions of variables under study.
• These objectives and operational definitions not only help delimit the research problem but also provide a defined direction to the research process where researchers have clarity about what to study under a particular statement problem.

3. Review of literature:

• A literature review is a summary of previous knowledge generated by the topic of study.
• It is believed by researchers that research cannot be conducted in a vacuum, bereft of the previous knowledge available, but that it requires a foundation of existing knowledge to plan and conduct good research.
• This knowledge base can only be acquired through a literature review from several sources, such as books, journals, research reports unpublished theses, newspapers, magazines, and other current popular electronic information sources.
• A review of the literature helps the researchers to understand what is already known about a topic and what needs to be further investigated.
• An effective literature review needs certain basic skills in researchers, such as critical thinking, brainstorming reading, comprehending, analysis, synthesizing, and summarizing
• Literature review plays an important role in the development of a research project and, moreover, researchers develop greater insight into the research problem and gain information on what has been alread investigated regarding a particular topic under study.

4. Developing conceptual framework:

• The basic aim of research in most disciplines is to develop, refine, or test theories.
• The nursing profession is considered one of the oldest arts. but one among the youngest sciences, where there is a great need to develop new theories or refine/test existing theories to expand the body of professional knowledge.
• Most of the nursing researchers plan to develop a conceptual framework based on the existing nursing or other non-nursing theories. However, researchers may have their own concepts to develop new nursing-based conceptual frameworks for research studies.
• These conceptual frameworks not only provide meaning to the research problem but also help in developing hypotheses or assumptions for the research studies.

“Asymptomatic vs symptomatic effects of ignoring proper research design: Q&A”

5. Formulating hypothesis/assumptions:

• The hypothesis is an assumed statement suggesting an answer to a question, which may or may not be true.
• In simple words, it translates the statement of problem prediction of what is clearly understandable and easy to comprehend is expected to be the outcome of the study, which is either accepted or rejected based on the empirical data generated at the end of the research process.
• For example, consider this research statement, ‘A correlational study on alcohol intake and incidence of liver disease among people of an urban community at Ludhiana’ Here, the hypothesis will be ‘alcoholics have higher incidence of liver disease”.
• Assumptions are statements that are to be tested to be considered true before they have been scientifically tested.
• In other words, assumptions are the general beliefs of the people at large that are held to be true but have not necessarily been proven.
• For example, satisfaction is largely influenced by the preconceived expectations of the customers.
• This is a general belief of the people, but a researcher may use it as an assumption in a customer satisfaction study. Generally, in nonexperimental research, investigators use assumptions or research questions to have defined directions for the research study.

6. Selecting research approach/design:

A research design is a systematic plan to obtain answers to research questions. In a broader sense, the research design is the blueprint of a research study, which enables the researcher to know whom, what, when, where, and how the study will be conducted.

• Who: Specifies the subject(s) under study, eg. patients, families, groups, etc
• What: Specifies the variables that are to be focused upon and measured.
• When: Specifies the time of the study, duration, and frequency of the observations.
• Where: Specifies the setting of the study; that is where the researcher will be conducting the study.
• How: Specifies how the data will be collected.
  • For example, through manipulation of the variables in experimental study under random assignment or simple questioning, interview, or observation of a phenomenon without manipulation and control over variables.
  • Basically, there are three approaches/designs for quantitative research: experimental, quasi-experimental, and nonexperimental. However, these designs can be further subclassified into several subtypes of each design.
• Experimental: In experimental research, there is a random assignment of subjects, and availability of a control group to compare with the experimental group, and manipulation of the independent variable to observe the effect on the dependent variable in an experimental group
• Quasiexperimental: Quasiexperimental studies involve the manipulation of independent variables to observe the effect on dependent variables,
  • but usually, they do not exert complete control over extraneous variables in manipulation and randomization.
  • Research studies on humans, in either field or clinical settings, make it impossible to strictly imply the random assignment of subjects or control of manipulation and extraneous variables.
  • Therefore, healthcare professionals mainly conduct quasi-experimental research.
• Nonexperimental: In this design, research variables are studied without manipulating them in a natural setting for the purpose of description, exploration, explanation, or identification of correlation between two or more variables.

7. Specifying the population:

• In quantitative research studies, it is essential to learn about the characteristics the subjects possess and clearly define the group of subjects or objects under study.
• The research population is an aggregate of all the subjects or objects with specific characteristics.
• For example, in a study on ’ the prevalence of hospital-acquired infection among patients admitted to intensive care units, the population is patients admitted to intensive care units, where all the patients are living in similar situations, Le. they are all admitted to intensive care units.
• Population specification helps the researcher to plan and develop an effective methodology and tools for the e development of empirical evidence

“Can targeted interventions improve outcomes using research design knowledge? FAQs provided”

8. Developing tool(s) for data collection:

• This is the most important and crucial step of the t is essential to devise methods and tools to measure the research vari research process It is able as objectively and accurately as possible.
• The plan and design of the tools for data collection depends on several factors, such as the type of research design, variables, subjects, researcher’s expertise, available resources, and time for study.
• The researcher could use existing standardized research tools or develop new tools, which must be used after establishing their validity and reliability.
• In quantitative research, usually, structured or semistructured tools are usually used for data collection, which requires a lot of constructive effort, such as reviewing relevant content, brainstorming, expert suggestions, and so on.

9. Establishing ethical considerations:

In nursing science, most of the research is conducted on humans, where it becomes more important to establish ethical considerations.

During research studies, a researcher can address the ethical issues and may establish ethical considerations by taking the following measures:

• Taking informed consent from participants
• Avoiding errors in data collection.
• Obtaining permission from the competent authority to conduct the study.
• Doing justice to participants in analyzing data.
• Maintaining confidentiality of the information and anonymity of subjects.

“Success rate of interventions using modern research design techniques: FAQ”

10. Conducting pilot study/tryout of tool:

• A pilot study is a kind of small-scale rehearsal on the subjects, but these subjects are not a part of the actual study.
• A pilot study is conducted to ensure the feasibility of the study and revise the methodology and tool(s) of the study in case of any shortcomings found.

11. Sample selection:

• It is not practically possible to conduct a study on the entire population. Therefore, researchers must select a representative part of the population.
• A sample can be selected by using either probability or nonprobability sampling technique, where the choice of sampling technique depends on several factors, like the nature of the study; availability of time, money, resources, and researchers‘ knowledge about the population, etc.
• However, researchers always strive to select a representative sample that adequately reflects the population’s traits.

12. Data collection:

• It is the most time-consuming step of the research process, which involves direct or indirect interaction with respondents to gather information pertaining to the topic under study.
• It must be carefully planned and implemented to collect relevant information by using preplanned methods, techniques, and tools of data collection.
• Data collection requires adequate planning, patience, communication, interpersonal relationships, and recording skills.
• Data could be collected through questioning, interviewing, or observation methods.

“Differential applications of cross-sectional vs longitudinal designs: Questions answered”

13. Preparing data for analysis:

• In quantitative studies, careful checking of every tool for its completeness and coding is the main activity during this step of the research process.
• must be ensured that one code specifies only one piece of information, and it should be maintained carefully to avoid any errors.
• Coding can be carried out manually on a paper sheet or on a computer grading sheet (eg. MS Excel sheet) or directly in statistical software.

14. Analysis and interpretation of data:

• In quantitative research studies, numerical data must be organized in an orderly and sequential manner, and processing is required because research questions cannot be answered in numerical form.
• In quantitative research, data may be analyzed by using descriptive or inferential statistics.
• Furthermore, data may be analyzed either by manual calculations or by using statistical software programs, like Statistical Program for Social Sciences (SPSS). Data is presented through tables, graphs, and charts to facilitate the interpretation of data.

15. Disseminating the research findings:

• Research may fail in the achievement of its objectives, if findings are not disseminated to its users.
• Research findings may be communicated through the writing of a research thesis, or article, or presenting an oral research report at scientific professional conferences.
• Research findings must be communicated in a standardized format according to international, national, or institutional guidelines

“Difference between experimental and non-experimental research designs in nursing: Q&A explained”

Question 13. Define Experimental Research Designs and nonexperimental Research Design
Answer:

• Experimentation is the most scientifically sophisticated research method) It is defined as ’observation under controlled conditions’ Experimental research differs from nonexperimental design in one important aspect.
• The researcher using an experimental design is an active agent rather than a passive observer.)
• Experimental research designs are concerned with the examination of the effect of the independent variable on the dependent variable, where the independent variable is manipulated through treatment or intervention(s), and the effect of these interventions is observed on the dependent variable.
• All the experimental researches have a common characteristic, that is, manipulation of independent variables, but a true experiment also consists of the principles of randomization and control.

Nonexperimental Research Design

• Nonexperimental research design is one of the broad categories of research designs, in which the researcher observes the phenomena as they occur naturally, and no external variables are introduced.
• It is a research design in which variables are not deliberately manipulated, nor is the setting controlled.

Question 14. Historical research design
Answer:

Introduction:

• The systematic collection and criteria evaluation of data relating to past occurrences of particular phenomena also relies primarily on quantitative data.
• Historical research is undertaken to answer questions concerning causes, effects, or trends relating to past events that may shed light on present behavior or practices.

“Cost of ignoring proper research design vs benefits of systematic approaches: Q&A”

Characteristics of Historical Research Design

• It is important not to confuse Historical research with a review of literature about Historical events.
• Historical research involves the careful study and analysis of data about past events.
• Historical research is a critical investigation of events, Their development, and experience of the past evidence.
• The purpose is to gain a clearer understanding of the impact of the past on present and future events related to the life process.
• It involves a detailed analysis of what has been written.
• Is used to describe, explain, or interpret events.
• Generally, Historical research involves the review of written materials but may include oral documentation as well.
• Historical research typically relies on available data.
• The historical method of research also covers categories such as Historical, legal, documentary, institutional, or organizational.
• Important existing data sources for nurse researchers are hospital records, nursing charts, physician order sheets, and care plan statements.

Steps of Historical Research

1. First step: Data Collection
2. Second step: Criticism of the data.
3. Third Step: Presentation of the facts

“Most common complications of poorly understood research designs: FAQs”

1. First Step:
   • Comprehensive gathering of data is undertaken. Historical sources of data are usually classified into two main categories: –
   • Primary Sources – Primary sources are First-hand information that includes –
     • Oral or written records.
     • Remains or realic associated with person, events, groups.
     • Documents classified as laws, office records certificates, newspapers, magazines, maps, diagrams, etc.
2. Secondary source:
   • These are the reports of people who related the actual witness or actual participants in the same
   • For example- Most of the history books.
     • Second Step: Criticism of the data.
       • The second step necessitates a comprehensive review of gathered materials. Two types of criticism
       • External criticism – The establishment of the validity of the source.
       • Internal criticism – the determination of reliability by correctly interpreting the contents of the document
3. Third Step:
   • Presentation of the facts
   • After evaluating the Historical data, the researcher must bring the material together to analyze it and the research hypotheses

“Why are research design steps often misunderstood in practice? Questions answered”

Areas of Historical Study

• Periods- Historical studies often focus on events and developments that occurred during particular blocks of time in the past.
• Geographical locations- particular Geographical locations e.g. countries, cities
• Military history- is concentrates on the study of conflicts that have happened in human society.
• Methods and tools used in Historical research
• Contemporaneous corroboration
• Photography
• Historical revisionism
• Change log
• Human evolution
• Social change

The post BSc Nursing 3rd Year Nursing Research And Statistics Chapter 9 Research Design Question And Answers appeared first on BDS Notes.

Question 1. Define Theory
Answer:

Definitions Of Theory

• “A theory” is a statement that purports to account for or characterize some phenomenon” and that it “Pulls out the salient parts of a phenomenon so that one can separate the critical and necessary factors or relationships from the accidental or unessential factors or relationship” (Barnum 1990).
• “A theory is a set of concepts interrelated to form propositions that are useful for prediction and control.” “A theory is a conceptual system or framework invented for some purposes” (Dickoff and James 1968).
• “A theory is a set of interrelated constructs (concepts adapted for a scientific purpose), definitions, and propositions that present a systematic view of phenomena by specifying relations among variables, with the purpose of explaining and predicting the phenomena” (Kerlinger, 1986).

Read And Learn More: BSc Nursing 3rd Year Nursing Research And Statistics Previous year Question And Answers 

“Understanding theoretical frameworks through FAQs: Q&A explained”

Question 2. Explain the Purposes of Theory
Answer: 

Nursing theory serves many purposes Among these purposes are:

1. To develop research propositions
2. To serve as a reservoir for knowledge and research findings
3. To explain observations and predict outcomes
4. To stimulate new directions in practice and in research.

“How do theories guide nursing research studies? FAQ answered”

Question 3. Discuss the importance of nursing theories in the formulation of conceptual framework with any one example each.
Answer:

• There are many theories which are reported in the nursing literature.
• For example, learning theory, developmental theory, theories of adaptation, stress and homeostasis theory, systems theory, social theories, and cultural theories.
• Learning theory includes theories of conditioning, social learning, and cognitive theory. Developmental theory examines changes that occur through time, in the physical, psychological, and social structures.
• Theories of adaptations, stress, and homeostasis examine how individuals or groups survive and function in a particular environment.
• System theory is diverse, focussing at times on behavioral systems and at other times on systems of interaction and communication or adaptation modes.
• Social theories examine factors that are external to individuals, such as social class, but that affect their life chances and lifestyles.
• Theories of symbolic interaction, such as Role Theory seek to explain how symbols and meanings establish the rules, roles, roles, and images of self and others in daily life.
• Cultural theories examine how traditional ways of life affect the behavior, values, beliefs, and perceptions of individuals and groups.
• The investigator uses theory in research to provide a framework constructed from past ideas, understanding, and research findings, and to provide foundations for the proposed research project, and he/she also uses theory to generate a hypothesis.
• Nursing theory is a source of professional autonomy and power. Theory provides nurses with a sound basis to describe explain, and predict factors that, influence nursing care.
• The recurrent theme in nursing theory guides nursing education, research, and practice and differentiates nursing practice from other disciplines.
  • In short, the theory provides a firm basis for planning and considering our actions and for challenging nursing practice and theory itself.
• Nursing theory enables nurses to predict the outcomes of what they are doing and to explain their selection of patient care approaches.

“Importance of studying theory for BSc Nursing students: Questions explained”

Question 4. Purpose Of Conceptual Framework
Answer:

• Its overall purpose is to make research findings meaningful and generalizable.
• Frameworks are efficient mechanisms for drawing together and summarizing accumulated facts, sometimes from separate and isolated investigations.
• Theories and models can guide the researcher’s understanding of not only the “what” of natural phenomena but also the “why” of its occurrence.
• Models help to stimulate research and the extension of knowledge by providing direction and importance (drawing force).

Question 5. Difference Between Theoretical and conceptual framework.
Answer:

“Common challenges in understanding theoretical frameworks effectively: FAQs provided”

Question 6. Enlist the stapes for the development of a conceptual framework.
Answer:

The framework of the study may be developed by using the following steps

• Identification of the general concepts:
  • Initially, the researcher identifies the general concepts of the study; these concepts may be based on study variables and previous research findings, existing theories, and models. Some concepts may be identified from a real-life observation or experience.
• Gathering relevant information:
  • Once the researcher identifies the concepts, the next step involves gathering the relevant information about the concepts from the relevant existing theories, previous research findings, etc.
  • This information helps the researcher to understand the concepts more empirically to establish the relationship between concepts for the development of the framework.
  • A framework is based on a specific theory or theoretical model; the researcher must read about it from primary sources.
  • Gathering relevant information about the concept enables the researcher to judge the amount of empirical support the theory has received and perhaps also the way the theory must be adapted to the framework.
• Formulation of the general scheme of relevant concepts:
  • After learning in-depth about the concepts, the researcher starts establishing the general relationship between the different related and relevant concepts.
  • This schematic relationship is established through brainstorming and logical reasoning. If a researcher has identified the problem statement and later develops a conceptual framework, it requires an iterative approach.

“Role of theoretical frameworks in guiding nursing studies: Questions answered”

“Why is early learning of theory critical for evidence-based practice? Answered”

Development of a logical construct:

• After establishing the logical relationship between two more variables, the researcher develops a final construct.
• Construct is a highly abstract, complex description of a phenomenon (concept), and is denoted by a made-up or construed term.
• Construct term is used to indicate a phenomenon that cannot be directly observed but must be inferred by certain concrete or less abstract indicators of the phenomenon.
• For example, wellness, mental health, and self-esteem are constructs but they are only measured through indefinable and measurable concepts; for example, wellness can only be assessed with laboratory data.

“Steps to explain types of theories: Grand vs middle-range: Q&A guide”

Evaluation and revision:

• Concepts and constructs act as the building blocks for the framework, which are later evaluated for their relevance and relationship to conclude or generalize the facts. After the evaluation, revisions may be made before the development of a framework.

Establishment of the congruity:

• Once a researcher develops a framework, it is important to establish the congruity between the conceptual model and its components, the research problem, the hypothesis, the description of the operationalization of concepts, and the selection of the research design.
• In the real sense, congruity of framework may only be established, if most of the research decisions and interpretations of the study findings are based on the framework.

The post BSc Nursing 3rd Year Nursing Research And Statistics Chapter 8 Theory And Theoretical Framework Question And Answers appeared first on BDS Notes.

Question 1. Definition Of Review Of Literature
Answer:

The review of literature is defined as a broad, comprehensive in-depth, systematic, and critical review of scholarly publications, unpublished scholarly print materials, audiovisual material, and personal communications.

According To Virginia Cano:

• A review of literature is a critical summary and an assessment of the current state of knowledge or current state of the art in a particular field.

According To Chris Jordan:

• A review of literature is the selection of available documents (both published and unpublished) on the topic, which contains information, ideas, data, and evidence written from a particular standpoint to fulfill certain aims or to express certain views on the nature of the topic and how it is to be investigated, and the effective evaluation of these documents in relation to the research being proposed.

Read And Learn More: BSc Nursing 3rd Year Nursing Research And Statistics Previous year Question And Answers 

“Understanding literature review through FAQs: Q&A explained”

Question 2. Purposes Of Literature Review
Answer:

• The review of literature is defined as a broad, comprehensive in-depth, systematic, and critical review of scholarly publications, unpublished scholarly print materials, audiovisual material, and personal communications.

According To Virginia Cano:

• A review of literature is a critical summary and an assessment of the current state of knowledge or current state of the art in a particular field.
• Research is an ongoing process that builds on previous knowledge. There are very few topics so rare that have never been investigated.

“Importance of studying literature review for BSc Nursing students: Questions explained”

There are many purposes for reviewing the literature before conducting a research study.

1. Identification of a research problem and development or refinement of research question or hypothesis
2. To demonstrate familiarity with a body of knowledge and to establish credibility.
3. To show the path of prior research and how a current project is linked to it.
4. To integrate and summarize what is known in an area.
5. To learn from others and stimulate new ideas.
6. Orientation to what is known and not known about an area of inquiry, to ascertain what research can best make a contribution to the existing base of evidence.
7. Determination of any gap or inconsistencies in a body of research.
8. Determination of a need to replicate a prior study in a different setting or with a different study population.
9. Identification or development of new or refined clinical interventions to test through empirical research.
10. Identification of relevant theoretical or conceptual for a research problem.
11. Identification of suitable designs and data collection methods for a study.
12. For those developing research proposals for funding, identification of experts in the field who could be used as consultants.
13. Assistance in interpreting study findings and in developing implications and recommendations.

“Common challenges in understanding literature review effectively: FAQs provided”

“Why is early learning of literature review critical for evidence-based practice? Answered”

Question 3. Explain Sources Of The Literature Review
Answer:

• The types of information sources for a review of literature are conceptual and data-based literature.
• The common sources of both these literature are books, chapters of books, journal articles, abstracts, critique reviews, abstracts published in conference proceedings, professional and governmental reports, and unpublished doctoral dissertations and theses.

The Kinds Of Information Available In Written Documents Can Be Categorized Into Five Broad Classes:

1. Facts, findings, or results.
2. Theory.
3. Research procedure or methods.
4. Opinions, points of view, or personal commentaries.
5. Anecdotes or impressions on a particular event or situation.

The references can be categorized as being either primary or secondary sources.

Primary Sources:

• A primary source is written by a person. Who developed the theory or conducted the research or is the description of an investigation written by the person who conducted it
• Most primary sources are found in published literature, e.g. nursing research articles. A credible literature review reflects the use of mainly primary sources.

“Differential applications of primary vs secondary sources: Questions answered”

“Steps to explain types of literature review: Narrative vs systematic: Q&A guide”

Secondary Sources:

• A secondary source is written by persons other than the individual who developed the theory or conducted research.
• Otherwise, known as it is a description of a study or studies prepared by someone other than the original researcher.
• Often secondary source represents a response to, or a summary and critique of a theorist or researcher’s work.
• Secondary sources may be used when unavailability of primary sources, and if we want to know different ways of looking at an issue or problem.

Question 4. Steps in Review of Literature
Answer:

The preliminary review generally includes three steps:

1. Identification of important publications-earlier works, reports, recent publications-Journals books Medlars, Medline Cat-line.
2. Summarize and record the contents of the publication contents-such as theoretical perspective, definitions, research design
3. Comparison of content such as theoretical perspectives, definitions, research, design, methods, instruments, and findings

“Asymptomatic vs symptomatic effects of ignoring proper literature review practices: Q&A”

Way To Review:

1. To select pertinent books or articles from a general review of the literature, examine the book or article quickly.

• For a book, examine the title page that gives the author credentials then scan the table of contents, the index, the bibliography, and the charts and table.
• Read the preface rapidly to determine the author’s purpose; then thumb through the chapters quickly to assess substance.
• If the book is promising, keep it for a critical review. For the article, examine the author’s credentials, quickly scan the problem statement, and the hypotheses, and then focus on the methods of research used, especially how the sample was selected and the data analyzed.
• Read the conclusion and summaries and note the use of theory and other research studies. Retain sound and pertinent literature for summary and critique.

2. To summarise and record information, first note the author, title of the article, and year of publication. If further information is needed, this immediately refers to the reader to the bibliography card.

“Role of literature review in identifying research gaps: Questions answered”

3. Record information from a research report in the following order(s):

• Problem statement
• Definition of concepts
• Hypothesis if any
• Theories or assumptions used
• Method of research including how samples were drawn
• Instruments and scales used
• Type of research (description or explanation)
• Methods and findings of data analysis
• Interpretations of data especially whether the hypothesis is supported or rejected
• Recommendations and suggestions for further research if any
• Make special note of implications for nursing practice or theory.

“Early warning signs of gaps in understanding literature review basics: Common questions”

4. Note that data was not included, such as limitations that were not noted, means of establishing the validity and reliability of instruments that were not given, theory that was not used, or former studies that were not examined.

• Comparison of the recorded summaries of literature enables the researcher to contrast definitions of concepts, uncover competing theories used to explain the same phenomenon,
• Consider the various designs that have been utilized to study the same problem, examine different methods of data collection, and find valid instruments already developed.

The post BSc Nursing 3rd Year Nursing Research And Statistics Chapter 7 Review Of Literature Question And Answers appeared first on BDS Notes.

Question 1. Define Hypothesis
Answer:

• The word hypothesis (plural is hypotheses) is derived from the Greek word ‘hypothenar’ meaning ’to put under‘ or ’to suppose’ For a hypothesis to be put forward as a scientific hypothesis, the scientific method requires that one can test it.

Hypothesis Definition:

• According to Lundberg, “A hypothesis is a tentative generalization, the validity of which remains to be tested. In its most elementary stage, the hypothesis may be any hunch, guess, or imaginative idea, which becomes the basis for action or investigation.
• A hypothesis is a tentative assumption drawn from knowledge and theory which is used as a guide in the investigation of other facts and theories that are yet unknown.

“Understanding hypothesis assumptions through FAQs: Q&A explained”

Read And Learn More: BSc Nursing 3rd Year Nursing Research And Statistics Previous year Question And Answers 

Question 2. Types of Hypothesis
Answer:

• According to Lundberg, “A hypothesis is a tentative generalization, the validity of which remains to be tested. In its most elementary stage, the hypothesis may be any hunch, guess, or imaginative idea, which becomes the basis for action or investigation.

Types Of Hypotheses

• Hypotheses are classified in several ways. With reference to their function.

Hypotheses are of two types:

1. Descriptive hypotheses and
2. Relational hypotheses.

“Importance of studying hypothesis assumptions for BSc Nursing students: Questions explained”

Another approach is to classify them into:

1. Working hypotheses,
2. Null hypotheses and
3. Statistical hypotheses.
4. The third approach is to divide them on the basis of the level of abstraction.

Three broad levels may be distinguished:

1. Simple Description
2. Logical Derivation
3. Abstraction.

Accordingly, there are three types of hypotheses:

1. Common-Sense Hypotheses,
2. Complex Hypotheses And
3. Analytical hypotheses.

Descriptive hypotheses:

• These are propositions that describe the characteristics (such as size, and form of distribution) of a variable. The variable may be an object, person, organization, situation, or event.

“Common challenges in understanding hypotheses effectively: FAQs provided”

Some examples are:

• “Patients who attend pre-operative education classes have less post-operative emotional stress than patients who do not.”
• “The rate of unemployment among Nursing graduates is lesser than that of medical graduates.”
• Relational hypotheses: These are propositions, which describe the relationship between two variables. The relationship suggested may be a positive or negative correlation or causal relationship.

Some examples are:

• “Families with higher incomes spend more for recreation.”
• “Upper-class people have fewer children than lower-class people.”
• “Labour productivity decreases as the working duration increases.”

Causal hypotheses:

• State that the existence of, or a change in, one variable causes or leads to an effect on another variable.
• The first variable is called the independent variable, and the latter is the dependent variable. When dealing with causal relationships between variables the researcher must consider the direction in which such relationships flow, i.e. which is cause and which is effect e.g., smoking causes lung cancer.

Working hypotheses:

• While planning the study of a problem, hypotheses the formed.
• Initially, they may not be very specific. In such cases, they are referred to as “Working Hypotheses” which are subject to modification as the investigation proceeds.

“Factors influencing success with hypothesis knowledge: Q&A”

Null hypotheses:

These are hypothetical statements denying what is explicitly indicated in working hypotheses.

• They do not, nor were ever intended to exist in reality. They state that no difference exists between the parameter and the statistic being compared to it.
• For example, even though there is a relationship between a family’s income and expenditure on recreation

A null hypothesis may state:

• “There is no relationship between families’ income level and expenditure on recreation.”
• Null hypotheses are formulated for testing statistical significance, since, this form is a convenient approach to statistical analysis. As the test would nullify the null hypotheses, they are so-called.

Statistical hypotheses:

• These are statements about a statistical population. These are derived from a sample.
• These are quantitative in nature in that they are numerically measurable, e.g., “Group A is older than Group B.”

Common sense hypotheses:

• These represent common sense ideas. They state the existence of empirical uniformities perceived through day-to-day observations.
• Many empirical uniformities may be observed in business establishments, the social background of workers, and the behavior patterns of specific groups like students e.g., “shop assistants in small shops lack motivation.”
• “Soldiers from upper class are less adjusted in the army than lower class men”; “fresh students conform to the conventions set up by seniors.”

Complex hypotheses:

• These aim to test the existence of logically derived relationships between empirical uniformities.
• For example, in the early stage, Human ecology described empirical uniformities in the distribution of land values, industrial concentrations, types of business, and other phenomena.
• Further study and logical analysis of these and other related findings led to the formulation of complex hypotheses such as “The concentric growth circles characterize a city,”
• “Members of minority groups suffer from oppression psychosis,” etc.
• Such hypotheses are purposeful distortions of empirical exactness. Analytical Hypotheses: These are concerned with the relationship of analytic variables.
• These hypotheses occur at the highest level of abstraction. These specify the relationship between changes in one property and changes in another.
• For example, the study of human fertility might show empirical regularities by wealth, education, region, and religion.
• If these were raised to the level of ideal type formulation, one result might be the hypothesis: “There are two high-fertility population segments in India viz., low-income urban Muslims and low-income rural low-caste Hindus.”
• At a still higher level of abstraction, the effects of region, education, and religion or fertility might be held constant.
• This would allow a better measurement of the relationship between the variables of wealth and fertility.

“Steps to explain types of hypotheses: Null vs alternative: Q&A guide”

Question 3. Importance of Hypothesis
Answer:

According to Lundberg, “A hypothesis is a tentative generalization, the validity of which remains to be tested.

In its most elementary stage, the hypothesis may be any hunch, guess, or imaginative idea, which becomes the basis for action or investigation.

It is A researcher’s eye,

• It focuses a research.
• It formulates clear and specific goals
• It links related facts
• It prevents blind research
• It works as a beacon light

1. Hypotheses facilitate the extension of knowledge in an area.
2. Hypotheses provide the researcher with rational statements,
3. Hypotheses provide direction to the research.
4. Hypotheses provide the basis for reporting the conclusions of the study.
5. The hypothesis has a very important place in research although it occupies a very small place in the body of a thesis.

“Role of assumptions in supporting research hypotheses: Questions answered”

Question 4. Assumptions
Answer:

• Assumptions are things that are accepted as true, or at least plausible, by researchers and peers who will read your dissertation or thesis.
• In other words, any scholar reading your paper will assume that certain aspects of your study are true given your population, statistical test, research design, or other delimitations.
• An assumption is a belief that forms one of the bases for the research. This belief is not to be tested or supported with empirical data. Very often belief is not stated in a research proposal.

Definition:

1. According to Charles: An assumption is a proposition that is taken for granted, as if it were true based upon presupposition without preponderance of the facts.
2. According to Chris Jordan: An assumption is an accepted cause-and-effect relationship or estimate of the existence of a fact from the known existence of another fact (s).
3. According to Frederick: An assumption is an ascertain about some characteristic of the future that underlies the current operations or plans of an organization.

Need of assumptions:

• This is important because both assumptions and limitations affect the inferences you can draw from your study.
• One of the more common assumptions made in survey research is the assumption of honesty and truthful responses.
• However, for certain sensitive questions, this assumption may be more difficult to accept, in which case it would be described as a limitation of the study
• For example, asking people to report they are criminal behavior in a survey may not be as reliable as asking people to report their eating habits.

“How does hypothesis testing impact nursing research outcomes? FAQ explained”

Types of assumptions:

1. Explicit assumptions:

• Explicit assumptions are assumptions of which the intention is fully revealed and expressed without vagueness, implication, or ambiguity.

2. Implicit assumption:

• Implicit assumptions are assumptions that are not expressed and may go undetected. If implicit assumptions prove to be wrong, this can damage projects.

“Early warning signs of gaps in understanding hypothesis basics: Common questions”

3. Primary assumption:

• Primary assumptions are assumptions about which the respondent identifies what they want, how dense the customer population is, what they need, and what the customer sees as the alternative to your project, The primary assumptions define the foundation of an organization.

4. Derivative assumptions:

• Derivative assumptions come from the primary assumptions. The numbers in a sales forecast are based on assumptions about customer demand, the number of clients; a salesperson can visit in a week, and the availability of the project.

Steps involved in assumption-based planning:

1. Identify assumptions: Collect all assumptions implicit, explicit, and primary and derivatives, out of the plan.
2. Determine criticality: Try to quantify the assumptions as much as possible in order to determine which assumptions have the greatest impact.
3. Design tests: Design a test for every critical assumption.
4. Schedule test: Every critical assumption needs to be tested, but not all assumptions can be tested in the present, so future assumptions tests are scheduled in a test schedule,
   Some possible reasons to schedule a test in the future are a lack of information in the present or dependency on the test outcomes of other tests.
5. Test assumptions: When an assumption is tested this results in a test outcome, which proves the assumption right or wrong.
6. Reassess venture plan: Based on the test outcomes and the test schedule one might decide to reassess the venture plan and update the plan with the new insights gathered into the assumption-based planning process.
7. Plan retesting of assumption: The assumptions need to be re-tested regularly if not constantly. There should be a retest scheduled for every critical assumption.
8. Create or update the assumption plan: The assumption plan holds all data gathered during the assumption planning process.

“Difference between null and alternative hypotheses in nursing research: Q&A explained”

“Asymptomatic vs symptomatic effects of ignoring hypothesis assumptions: Q&A”

Question 5. Types of Error.
Answer: 

In the context of testing the hypothesis, there are two types of errors. They are type-1 and type-2 errors.

• Type-1: We may reject H0 when HO is true and it is known as a type-1 error.
• Type-2: We may accept H0, when in fact HO is not true and it is known as a type-2 error.
• Types-1 error is also known as alpha error and type-ll error is also known as beta error.

Question 6. Characteristics of A Hypothesis
Answer:

Characteristics Of A Good Hypothesis:

An acceptable hypothesis should fulfill the conditions given below:

1. Conceptual clarity:
   • A hypothesis should be conceptually clear. It should consist of clearly defined and understandable concepts. Clarity is obtained by means of defining operationally the concepts in the hypothesis.
2. Specificity:
   • A hypothesis should be specific and explain the expected relations between variables and the conditions under which these relations will hold e.g., “when there is dissatisfaction and no care is taken, deprivation will engender violence.”
3. Testability:
   • A hypothesis should be testable and should not be a moral judgment. It should be possible to collect empirical evidence to test the hypothesis.
   • Statements like “Bad partners produce bad children” are commonplace generalizations and cannot be tested, as they merely express sentiments and their concepts are vague.
4. Availability of techniques:
   • Hypotheses should be related to available techniques. Otherwise, they will not be researchable; therefore, the researcher must make sure that methods are available for testing his proposed hypotheses.
5. Theoretical relevance:
   • A hypothesis should be related to a body of theory. A science can be cumulative only by building on an existing body of facts and theory. It cannot develop if each study is an isolated investigation.

“Differential applications of directional vs non-directional hypotheses: Questions answered”

1. • When research is systematically based upon a body of existing theory, a genuine contribution to knowledge is more likely to result. Therefore, a hypothesis should possess theoretical relevance.
2. Consistency:
   • Hypotheses should be logically consistent. Two or more propositions logically derived from the same theory must not be mutually contradictory.
3. Objectivity:
   • Scientific hypotheses should be free from value – judgment. In principle, the researcher’s system of values has no place in the scientific method.
   • However, as social phenomena are affected by the milieu in which they take place, the researcher must be aware of his values and state them explicitly.
4. Simplicity:
   • A hypothesis should be a simple one requiring fewer conditions or assumptions. But simple does not mean obvious. Simplicity demands insight. The more insight the researcher has into a problem, the simpler will be his hypothesis about it.

The post BSc Nursing 3rd Year Nursing Research And Statistics Chapter 6 Hypothesis Assumptions Question And Answers appeared first on BDS Notes.

Question 1. Define the Research Problem.
Answer: 

Research Problem Define:

1. A research problem is defined as a situation for which we have no ready and successful response by instinct or by previously acquired habit. We must find out what to do. i.e., the solution can be found out only after an investigation
2. A problem is an interrogative sentence or statement that asks what relation exists between two or more variables. The answers to questions will provide what is being sought in the research.
3. According to Marilynn J. Wood: the research problem is the full exposition of the idea that you want to study.

Read And Learn More: BSc Nursing 3rd Year Nursing Research And Statistics Previous year Question And Answers 

“Understanding research problems through FAQs: Q&A explained”

Question 2. Factors Involve in Selecting the Research Problem
Answer:

1. A research problem is defined as a situation for which we have no ready and successful response by instinct or by previously acquired habit. We must find out what to do. i.e., the solution can be found out only after an investigation
2.  According to Marilynn J. Wood: the research problem is the full exposition of the idea that you want to study.

Interest

• Interest should be the most important consideration in selecting a research problem. A research endeavor is usually time-consuming and involves hard work and possibly unforeseen problems.

Magnitude

• The researcher should have sufficient knowledge about the research process to be able to visualize the work involved in completing the proposed study.
• Narrow the topic down to something manageable, specific, and clear. It is extremely important to select a topic that the research can manage within the time and resources.

“Importance of studying research problems for BSc Nursing students: Questions explained”

Measurement of subjects:

• If the research uses concepts, make sure that the concepts are clear about their indicators and their measurements.
• If the researcher plans to measure the effectiveness of early ambulation after surgery, he must be clear as to what determines effectiveness and how it will be measured.

Level of expertise:

• The researcher should make sure that he has an adequate level of expertise for the task proposed.
• Allow for the fact that the researcher will learn during the study and may receive help from the research supervisors and others.

Relevance:

• Select a topic that is of relevance to the researcher as a professional. Ensure that the research study adds to the existing body of knowledge, bridges current gaps, or is useful in policy formation.

Availability of data:

• If the research topic entails the collection of information from secondary sources (office records, client records, census, or other already-published reports, etc.) before finalizing the topic make sure that these data are available and in the format the researcher wants.

Ethical issues:

• Another important consideration in formatting a research problem is the ethical issues involved. In the course of conducting a research study, the study population may be adversely affected by some of the questions (directly or indirectly) deprived of an intervention, and expected to share sensitive and private information.
• How ethical issues can affect the study population and how ethical problems can be overcome should be thoroughly examined at the problem formulation stage.

“Common challenges in understanding research problems effectively: FAQs provided”

Question 3. Define variables and Explain types of variables
Answer:

Variables Definition:

• A variable is a concept or abstract idea that can be described in measurable terms. In research, this term refers to the measurable characteristics, qualities, traits, or attributes of a particular individual, object, or situation being studied.
• Variables are properties or characteristics of some event, object, or person that can take on different values or amounts.
• Variables are things that we measure, control, or manipulate in research. They differ in many respects, most notably in the role they are given in our research and in the type of measures that can be applied to them.

1. The independent variable:

• Is that phenomenon in the hypothesis that, in the experimental study to test, the hypothesis, is not manipulated by the investigator?
• It is also called the cause, stimulus, experimental variable, or treatment, the variable that is manipulated by the researcher, in order to study the effect upon the dependent variable.

2. The dependent variable:

• Is that phenomenon in the hypothesis that, in the experimental study, to test the hypothesis, is not manipulated, but is accepted as € Disease it occurs?
• It is also called the effect, the response, the criterion measure; behavior, or outcome that is researcher wishes to predict, study, or explain.

1. Extraneous variables:

• Are all variables in a hypothesis testing investigation that are not dependent variable jar criterion measures of the dependent variables
• That means all those variables present in the research environment especially in research involving human subjects may interfere with the research findings, by acting as unwanted independent variables and confusing the results of the research.

“Factors influencing success with research problem identification: Q&A”

4. Intervening variables:

• They intervene between cause and effect. It is difficult to observe, as they are related to individual feelings such as boredom, fatigue excitement At times some of these variables cannot be controlled or measured but have an important effect on the result of the study as it intervene between cause and effect.
• Though difficult, it has to be controlled through appropriate design. E.g. “Effect of immediate reinforcement on learning the parts of speech”.

5. The accounted for variables :

• Are those variables about which, observations are gathered during the study for the sake of additional information they will provide, but which actually are not needed in relation to testing the hypothesis

6. Controlled for variables:

• Are those extraneous variables that are apt to affect the dependent variable in a manner similar to the dependent variable in a manner similar to the effect of the independent variable?

7. Confounding variables:

• Are extraneous variables that influence the dependent variables in the same way the independent variable influences them?
• These are those who would class confounding variables as those of program concern, after the independent and dependent variables.
• It will be noted, however, that, if variables that are apt to confound the findings are controlled for, there will be no confounding variables.
• Their confounding influence will have to be precluded from occurring. When speaking of relationships between variables, the researcher is essentially asking Is X related to Y? What is the effect of X on Y? How are X1 and X2 related to Y? The researcher asks a question about the relationship between one or more independent variables and dependent variables.

“Steps to explain types of research problems: Clinical vs theoretical: Q&A guide”

Question 4. Enlist the Sources of Problem Statements.
Answer:

Previous Research:

• The critical appraisal of research studies that appear in journals may indirectly suggest problem areas by stimulating the reader’s thinking.
• The organized body of nursing knowledge contains information about previous research.
• The investigator may have read something that did not make sense or work as predicted.
• A nurse may have read a variety of studies and wonder which findings are most valid. For example, studies related to cost-effectiveness.

Popular Conceptions:

• While taking care of health, certain traditional practices have been practiced according to their cultural belief or certain old practices of the new world.
• To test these practices, the researcher may get an idea for conducting a study.
• These researchers challenged a popular belief and found evidence that contraindicated the belief. Numerous beliefs guide nursing practice, and they need to be examined through research.

Empirical Interest:

• The sudden insight, which may arise from seeing things in a different way, is another source of research problems.
• Sudden awareness of something that has not been noticed before is another source of nursing research. (Curiosity about everyday clinical practice is a rich source for nursing research).

Practical Needs:

• Clinical practice provides a wealth of experience from which research problems can be derived. The nurse may observe the occurrence of a particular event or pattern and become curious about why it occurs as well as its relationships to other factors, in the client/patient environment.
• For example, OPD patients on treatment getting severe depend (may be due to anxiety). In a clinical area, a nurse may find an ideal answer to any question that may be a practical need for nursing and may be a source for research problems.

“Role of research questions in defining a research problem: Questions answered”

Political Concerns:

• The political climate, which places emphasis on constraining healthcare costs, the cause and effect of treatment of diseases improving the standards of health, and implementation of vertical health programs, has provided many avenues for research.

Priorities:

• There are so many problems emerging in providing health care services to reach the preserved people.
• Various groups, within nursing, have identified priorities for nursing research that if followed could result in the creation of knowledge that nurses need to care for future generations. The list of priorities can serve as a source of research problems for the nurse investigator.
• Priorities for nursing research also have been set for some nursing specialty areas, e.g.
• Nursing Service, Nursing Administration, Nursing Education, Medical-surgical Nursing, Pediatric Nursing, Psychiatric Nursing, Community Health Nursing etc.

Interested in Untested Theory:

• Verification of an untested nursing theory provides a relatively uncharted territory from which research problems can be derived. In as much as theories themselves are not tested, a researcher

The interest of Professional Organization:

• Researchable problems also are generated by professional organization priorities and quality, improvement issues.
• An example, of such an organization will include the Trained Nurses Association Govt. Nurses Association of Karnataka and others.

“How does a well-defined problem impact nursing research outcomes? FAQ explained”

Question 5. Sources of Research Problem.
Answer:

• The common sources from which a researcher may find ideas to identify and formulate a research problem include

Personal experiences:

• The day-to-day personal experience of a researcher may serve as a good source of ideas to formulate a research problem.
• For example, a researcher observed domestic violence suffered by wives of alcoholic husbands. This experience may provide ideas to identify several research problems related to domestic violence against women.
• There may be so many such life experiences of a researcher which could be used to develop a research problem.

Practical experience:

• Nurses get plenty of ideas to formulate research problems from their clinical experiences. Every curious nurse has several questions to be answered that are encountered during the clinical experience.
• For example, a nurse finds that unrestricted visiting hours in surgical wards reduced the analgesic demand among postoperative patients.
• In another instance, a nurse observed that the application of ice at the site of heparin injection reduced the chances of ecchymosis.
• Such clinical experiences could be rich sources of ideas to identify a significant research problem.

Critical appraisal of literature:

• When we critically study books and articles relating to the subject of our interest, including research reports, opinion articles, and summaries of clinical issues, pertinent questions may arise in our minds.
• These may strike the reader’s mind indirectly by stimulating imagination and directly by stating what additional research is needed.
• For example, a nurse reads an article on the prevalence of pin site infection among patients with external fixators.
• While reading this article, she learns that there is a lack of consensus about pin site care. This information may serve as a basis to formulate a research problem.
• Another type of literature that acts as a source of good research ideas is the requests for proposals (RFPs) that are published by government agencies and some companies.
• Typically, the RFPs describe the problems that need addressing, the contexts in which they operate, the approach they would like you to take to investigate and address the problem, and the amount they would be willing to pay for such research.

Previous research:

• A body of knowledge should be developed on a sound foundation of research findings. Usually, at the end of a research, further research problems are suggested, based on the shortcomings of previous research, which can be investigated.
• In the nursing profession, not much research has been yet done; therefore, this profession needs researchers who are willing to replicate or repeat other studies on different samples and settings where all the essential elements of the original study are held intact.
• Further refinements may be made in the experimental treatments, or more appropriate outcome measures may be identified.

“Early warning signs of gaps in understanding research problem basics: Common questions”

Existing theories:

• Research is a process of theory development and theory testing Nurses use many theories from other disciplines in their practices.
• If an existing theory is used in developing a researchable problem, a specific statement from the theory must be isolated.
• Generally, a part or parts of the theory are subjected to testing in the clinical situation. The testing of an existing theory is definitely needed in nursing; therefore, they serve as good sources of research problems.

Consumer feedback:

• Research problems may be generated from the results of activities aimed to solicit patient feedback.
• For example, at the time of discharge of a patient after angiography, a nurse obtained feedback from the patient.
• The patient verbalized that it was a wonderful experience except for the discomfort during the removal of the pressure dressing at the femoral puncture site.
• This feedback provided a concept for nurses to research on efficacy of available alternative means of dressing at the puncture site to minimize the discomfort among patients undergoing angiography.

Performance improvement activities:

• Performance improvement activities, known as quality improvement activities, are used to improve processes and outcomes to meet regulatory requirements.
• In the process of performance activities, several issues in merged that require answers through research.
• Thus performance improvement activity also serve as an important source of research problem.

Social issues:

• Sometimes, topics are suggested by more global contemporary social or political ues of relevance to the healthcare community.
• For example, HIV/AIDS, female sexual harassment, domestic violence, and gender equality in health care and in earth a some of the current social and political issues of concern for healthcare professionals.
• An idea for a study may stem from a familiarity with social concerns or controversial social issues.

Brainstorming:

• Brainstorming sessions are good techniques for finding new questions when an intensified discussion among interested
• people of the profession is conducted to find more ideas to formulate a good research problem.
• For example, ideas for studies may emerge from reviewing research priorities by having brainstorming sessions with other nurses, researchers, or nursing faculty.

Intuition:

• Traditionally, intuitions are considered good sources of knowledge as well as sources to find new research problems.
• It is believed that the reflective mind is a good source of ideas, which may be used to formulate a good research problem.

“Asymptomatic vs symptomatic effects of ignoring proper problem formulation: Q&A”

Folklores:

• Common beliefs could be right or wrong For example, it is generally believed that studying just before the test decreases the score. We believe we should not study before the test to relax our minds.
• Researchers can conduct a research study on whether one should study before the test or not.

Exposure to field situations:

• During held exposure, researchers get a variety of experiences that may provide plenty of ideas to formulate research problems.
• For example, while working in the field, a researcher observed a specific traditional practice for the cure of a decent condition, which can be used as a research problem to investigate its efficacy.
• Consultations with experts Experts are believed to have sound experience in their respective field, which may suggest a significant problem to be studied In addition, expend may help in finding a current problem of discipline to be solved, which may serve as hass for formulation of the research problem.

Question 6. Criteria For Selecting Research Problem?
Answer:

• Criteria for Selecting a Good Research Problem One commonly used acronym to define the criteria for a good research problem or question is FINER, where F stands for feasible, I for interesting, N for novel, E. for ethical, and R for relevant.
• However, there are many factors that should be considered while deciding the credibility of a particular research question for a scientific investigation.

Some of the most essential factors are discussed below:

-Significance to the nursing profession:

• A problem that a researcher is selecting should have significance to the nursing profession or it will not serve any purpose.
• A research problem is significant for the nursing profession when it is directed to develop or refine the body of professional knowledge.
• A research problem could be considered significant for the nursing profession.

if it fulfills the following criteria:

• Benefits nursing profession and patients, nurses, and health care fraternity through the study
• Improves clinical nursing practices through the results.
• Promotes nursing theory development or testing. Provides solutions to current nursing practice needs Generates information to get practical implications for nursing, profession

Original:

• It is fundamentally considered that every research problem should be new and unique in itself.
• Therefore, it is the key responsibility of a researcher that innovative knowledge is used for selecting research problems, so as to extend the growth of the existing body of knowledge in a profession.

“Can targeted interventions improve outcomes using research problem knowledge? FAQs provided”

Feasibility:

• Feasibility is an essential consideration of any research project.
• Regardless of how significant or researchable a problem may be, the feasibility of the research problem in reference to time, availability of subjects, facilities, equipment, money, and ethical considerations should be checked.
• It will help the researcher to decide whether the selected problem is appropriate or inappropriate and study can be carried out or not.

Time:

• A nurse might be interested in studying sibling relationships among quintuplets Knowledge of the incidence of quintuplet births would certainly discourage anyone from considering research on this particular population unless the researcher plans to make this a lifetime project.
• So, time is always a factor to be considered. It is wise to allow more time than seems to be needed because unexpected delays frequently occur.

Cost: All research projects cost money, some studies are much more expensive than others. The researcher must consider realistically the financial resources available.

Equipment and supplies: All research projects require some type of resources. Therefore, before making the final decision to conduct a study, an accurate determination of the needed equipment and supplies should be ensured.

• Some questions that should be answered before beginning a research project include: What is the equipment that will be needed? Is equipment available and in proper working order?
• Is there a qualified operator of the equipment? Are the necessary supplies available or can they be obtained?
• If the researcher takes into consideration equipment and supplies in the early phases of a research project, there are fewer chances of the project being revised or discarded later because of equipment or supply problems.

“Most common complications of poorly understood research problems: FAQs”

Administrative support:

• Many research projects require administrative support. The nurse researcher may find it very difficult to conduct research independently.
• Financial as well as psychological support from the administration is very helpful. Knowing that your superiors support your research efforts can be a very powerful motivating force.

Peer support:

• Many research ideas have never been developed because potential researchers received no support from their peers.
• One of the best ways to determine a researchable is through interactions and discussions with other nurses.
• A climate of shared interest in nursing research is essential among the members of the nursing profession.

Availability of subjects:

• A researcher may believe that study subjects are readily available for the study. But this may not be the case. Potential subjects may not Solicit.
• Research Problem meets the study criteria, may be unwilling to participate, or may already be participating in other studies. Therefore, the availability of subjects must be ensured well in advance.

“Differential applications of exploratory vs descriptive research problems: Questions answered”

Researcher’s competence:

• A research problem can only be feasible if it is in accordance with the researcher’s competence, where a researcher is capable of handling a given research problem.

Ethical considerations:

• A researcher must ensure that the research problem can be considered by the ethical committee without undue hurdles.
• A very important topic of research cannot be considered a feasible unit unless it is in accordance with ethical guidelines.

Solvable/Researchable:

• The problem selected is considered good only if it is solvable so the chances of unsolvability of a problem should be minimized. Thus, it will enhance relevant results.
• For example, a researcher selects a research problem to know the existence of God in this universe.
• These sorts of problems are ambiguous and impossible to solve. Therefore, the researcher must ensure that the research problem selected for the study is solvable.

“Difference between clinical and theoretical research problems in nursing: Q&A explained”

Current:

• A good research problem must be based on the current problems and needs of a profession so that the results generated will be of more use. Furthermore, more professionals will be interested in the research conducted on the current issues of their profession.

Interesting:

• A research problem can only be considered good if it is in accordance with the researcher’s field of interest.
• A research problem must be as per the motivation of the researcher and should be fascinating to the researcher so that research is conducted with full enthusiasm and not merely for its accomplishment.

The post BSc Nursing 3rd Year Nursing Research And Statistics Chapter 5 Research Problem Question And Answers appeared first on BDS Notes.