The document outlines the concept and structure of research design, emphasizing its importance in minimizing bias and maximizing data reliability. It details various types of research designs, including exploratory, descriptive, diagnostic, and hypothesis-testing designs, as well as the importance of formulating precise hypotheses and understanding key variables. Additionally, the document explains the procedures involved in hypothesis testing, including types of errors and the significance of one-tailed versus two-tailed tests.

1. Research Design

2. Meaning of Research Design
• Task of defining the research problem is the preparation of the research
project, popularly known as the “research design".
• Decisions regarding what, where, when, how much, by what means
concerning an inquiry or a research study constitute a research design.
• A plan or strategy for conducting the research.
• A research design is one that minimizes bias and maximizes the reliability
of the data.
• It also yields maximum information, gives 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 problem, i.e., a particular design is suitable for a particular
problem.

3. Features of a good research
design
The means of obtaining information
 The availability and skills of the researcher and his
staff, if any;
The availability of time and money for the research
work.
It should be flexible enough to consider different
aspects of the study in case of exploratory.
The design should be accurate with minimum bias in
case of accurate description
Control of extraneous variables
Statistical correctness for testing hypothesis

4. Research design have following parts
• Sampling design
• Observational design
• Statistical design
• Operational design

5. ..cont..
Sampling Design
• Which deals with the methods of selecting items to be
observed for the study.
Observational design
• Which relates to the condition under which the
observation are to be create.
Statistical design
• Which concern the question of the of How the
information and data gathered are to be analyzed ?
Operational design
• Which deals with techniques by which the procedures
satisfied in sampling .

6. Different Research Designs
1) Exploratory research design
2) Descriptive research design
3) Diagnostic research design
4) Hypothesis-testing research design

7. Exploratory research design
• Termed as formulative research because its main purpose is formulating a
problem for more precise investigation.
• It is a type of research conducted for a problem, but the problem itself has
not been clearly understood.
• In other words, exploratory research is a process of gathering facts and
doing research that later allows for the team to create the best research
design or data collection method available for specific subjects.
• This process will draw definitive conclusions only with caution due to the
nature of the process. In many cases, this process leads to the
understanding that no problem actually exists.
Exploratory research is guided by a set of hypotheses
1) Operational definition
2) Statistical testing

8. Descriptive research design
• Descriptive research design is a type of research
method that is used when one wants to get
information on the current status of a person or
an object.
• It is used to describe what is in existence in
respect to conditions or variables that are found
in a given situation.
• Concerned with describing the characteristics of a
particular individual or group .

9. Diagnostic research design
• Diagnostic research studies determine the frequency of occurrence
of something or its association with something else. From the
research design point of view, the design of such studies should be
rigid and should focus on the following:
1) Objective of the study (what the study is about and why is it being
made?)
2) Methods of data collection (what techniques of data collection
will be adopted?)
3) Sample selection (how much material will be needed?)
4) Data collection (where the required data can be found and with
what time frequency should the data be related?)
5) Data processing and analysis
6) Reporting the findings

10. Hypothesis-testing research
design
Hypothesis-testing research studies, also known as experimental studies, an
experiment is a study in which a treatment, procedure, or program is intentionally
introduced and a result or outcome is observed. The American Heritage Dictionary
of the English Language defines an experiment as “A test under controlled
conditions that is made to demonstrate a known truth, to examine the validity of a
hypothesis, or to determine the efficacy of something previously untried.”
True experiments have four elements:
1) manipulation
2) control
3) random assignment and
4) random selection.
The most important of these elements are manipulation and control. Manipulation
means that something is purposefully changed by the researcher in the
environment. Control is used to prevent outside factors from influencing the study
outcome

11. Important Concepts relating to
research design
• Dependent and independent variables
• Extraneous variable
• Control
• Experimental and non-experimental
hypothesis- testing research
• Experimental and control groups
• Treatments

12. Variables
• Any characteristic which is subject to change and
can have more than one value such as age,
intelligence, motivation, gender, etc.
• Types of variables
• Independent vs. Dependent vs. Controlled
Variables
• Categorical vs. Continuous Variables
• Quantitative vs. Qualitative Variables

13. …cont..
Dependent Variable
• Variable affected by the independent variable
• It responds to the independent variable.
• In an experiment that which is supposed to be changed by
the independent.
Independent Variable
• Variable that is presumed to influence other variable
• It is the presumed cause, whereas the dependent variable
is the presumed effect.
• In an experiment that which is supposed to be manipulated
by you.
• The variable manipulated by the experimenter.

14. Examples
(1)You are interested in “How stress affects mental state of
human beings?”
Independent variable ----- Stress
Dependent variable ---- mental state of human beings
You can directly manipulate stress levels in your human subjects
and measure how those stress levels change mental state.
(2) Promotion affects employees’ motivation
Independent variable ----- Promotion
Dependent variable ----Employees motivation

15. Other Names for Dependent and
Independent Variables
• Dependent Variable
• Explained
• Predictand
• Regressand
• Response
• Outcome
• Controlled
• Independent Variable
• Explanatory
• Predictor
• Regressor
• Stimulus
• Covariate
• Control

16. Terms related to Research Design
Extraneous variable
• Independent variable that are not related to the purpose of the study, but
may affect the dependent variable are termed as extraneous variables.
• Whatever effect is notices on dependent variable as a result of extraneous
variable is technically described as an ‘experimental error’.
Control
• One of the important characteristics of a good research design is to
minimize the influence or effect of extraneous variable.
Experimental and non experimental hypothesis testing
• Research in which the independent variable is manipulated is termed
experimental hypothesis –testing research.
• Research in which an independent variable is not manipulated is called
non-experimental hypothesis.

17. ..cont..
Example of experimental and non-experimental
• Suppose a researcher wants to study whether intelligence affects reading ability
for a group of students and for this purpose he randomly selects 50 students and
tests their intelligence and reading ability by calculating the coefficient of
correlation between the two sets of scores . This is an example of non-
experimental hypothesis –testing research because herein the independent
variable (intelligence)is not manipulated.
• But now suppose that our researcher randomly selects 50 students from a group
of students who are to take a course in statistics and then divides them into two
groups by randomly assigning 25 to Group A , the usual studies programme, and
25 to group B , the special studies programme. At the end of the course , he
administers a test to each group in order to judge the effectiveness of the training
programme on the student’s performance level. This is an example of
experimental hypothesis testing.

18. ..cont..
Experimental and control group
• In an experimental hypothesis –testing research when a group
is exposed to usual conditions, it is termed as control group.
• When a group is exposed to some novel or special condition it
is termed as experimental group.
Treatments
• The different conditions under which experimental and
control groups are put are usually referred to as treatments.

19. Categorical vs. Continuous Variables
• Categorical variables are variables that can take on specific values only within a defined range of
values like gender, marital status
• consisting of discrete, mutually exclusive categories, such as “male/female,” “White/Black,” etc
• Continuous variables are variables that can theoretically take on any value along a continuum like
age, income weight, height etc..
• When compared with categorical variables, continuous variables can be measured with a greater
degree of precision.
• The choice of which statistical tests will be used to analyze the data is partially dependent on
whether the researcher uses categorical or continuous variables.
• Certain statistical tests are appropriate for categorical variables, while other statistical tests are
appropriate for continuous variables.
• As with many decisions in the research-planning process, the choice of which type of variable to
use is partially dependent on the question that the researcher is attempting to answer.

20. Quantitative vs. Qualitative Variables
• Qualitative variables are variables that vary in kind, like
“attractive” or “not attractive,” “helpful” or “not helpful,” or
“consistent” or “not consistent”
• Quantitative variables are those that vary in amount like
height, weight, salary etc

21. Hypothesis
• The research hypothesis is a predictive statement
that relates an independent variable to
dependent variable.
• A hypothesis may be defined as a proposition or
set of proposition set forth as an explanation for
occurrence of some specified group of
phenomena either asserted merely as a
provisional conjucture to guide some
investigation or accepted as highly probable in
the light of established facts

22. Purpose
• Guides/gives direction to the study/investigation
• Defines Facts that are relevant and not relevant
• Suggests which form of research design is likely to be the most appropriate
• Provides a framework for organizing the conclusions of the findings
• Limits the research to specific area
• Offers explanations for the relationships between those variables that can be
empirically tested
• Furnishes proof that the researcher has sufficient background knowledge to
enable her/him to make suggestions in order to extend existing knowledge
• Structures the next phase in the investigation and therefore furnishes continuity to
the examination of the problem

23. Experimental and non-experimental
hypothesis testing
• When a group is exposed to usual conditions,
it is termed as a control group.
• But when the group is exposed to be some
special condition, it is termed as Experimental
group

24. A Hypothesis
• must make a prediction
• must identify at least two variables
• should have an elucidating power
• should strive to furnish an acceptable explanation or
accounting of a fact
• must be falsifiable meaning hypotheses must be capable
of being refuted based on the results of the study
• must be formulated in simple, understandable terms
• should correspond with existing knowledge
• In general, a hypothesis needs to be unambiguous,
specific, quantifiable, testable and generalizable.

25. Categorizing Hypotheses
Can be categorized in different ways
1. Based on their formulation
• Null Hypotheses and Alternate Hypotheses
2. Based on direction
• Directional and Non-directional Hypothesis
3. Based on their derivation
• Inductive and Deductive Hypotheses

26. ..cont
1. Null Hypotheses and Alternate Hypotheses
• Null hypothesis always predicts that
– no differences between the groups being studied (e.g.,
experimental vs. control group) or
– no relationship between the variables being studied
• By contrast, the alternate hypothesis always predicts
that there will be a difference between the groups
being studied (or a relationship between the
variables being studied)

27. Example of Null and alternative
hypothesis
• If we are to compare method A with method B
about its superiority and if we proceed on the
assumption that both methods are equally
good, then this assumption is termed as the
null hypothesis.
• As against this, we may think that the method
A is superior or the method B is inferior , we
are then stating what is termed as alternative
hypothesis.

28. ..cont..
• Alternate Hypothesis can further be classified
as
2. Directional Hypothesis and Non-directional
Hypothesis

29. ..cont..
2. Directional Hypothesis and Non-directional Hypothesis
• Simply based on the wording of the hypotheses we can tell
the difference between directional and non-directional
– If the hypothesis simply predicts that there will be a difference
between the two groups, then it is a non-directional hypothesis. It is
non-directional because it predicts that there will be a difference but
does not specify how the groups will differ.
– If, however, the hypothesis uses so-called comparison terms, such as
“greater,”“less,”“better,” or “worse,” then it is a directional hypothesis.
It is directional because it predicts that there will be a difference
between the two groups and it specifies how the two groups will differ

30. The level of significance
• This is very important concept in the context of hypothesis testing.
• It is usually 5% which should be chosen with great care, thought and reason. In
case we take the significance level at 5% , then this implies that H0 will be rejected
when the sampling result has a less that .05 probability of occurring if H0 is true.
• In other words , the 5% level of significance means that researcher is willing to
take as much as 5% risk of rejecting the null hypothesis when H0 happens to be
true. Thus the significance level is the maximum value of the probability of
rejecting H0 when it is true and is usually determined in advance before testing
the hypothesis.

31. Type 1 and type 2 errors
• We may reject H0 when H0 is true , is type 1 error and is denoted by
Alpha.
• We may accept H0 when in fact H0 is not true, is type 2 error and is
denoted by beta.
• The probability of type 1 error is usually determined in advance and is
understood as the level of significance of testing the hypothesis.
• If type 1 error is fixed at 5% , it means that there are about 5 chances in
100 that we will reject H0 when H0 is true. We can control type 1 error
just by fixing it at a lower level.
• With a fixed sample size , n, when we try to reduce Type 1 error , the
probability of committing type 2 error increases. Both types of errors can
not be reduced simultaneously.
• To deal with this trade off in business situations , decision makers decide
the appropriate level of Type 1 by examining the cost or penalties
attached to both types of errors.

32. Two-tailed and one-tailed tests
• A two-tailed test, also known as a non directional
hypothesis, is the standard test of significance to
determine if there is a relationship between variables in
either direction. Two-tailed tests do this by dividing the .05
in two and putting half on each side of the bell curve.
• A two-tailed test rejects the null-hypothesis if sample mean
is significantly higher or lower than the hypothesised value
of the mean of the population.
• Such test is appropriate when the null hypothesis is some
specified value and alternative hypothesis is a value not
equal to the specified value of the null hypothesis

33. ..cont..
• Let's say I do a simple test called a t-test, which compares two
averages. I have the average stress levels from last year compared
to the average stress levels of this year.
• After some probability calculations, I learn that there is no
significant difference between last year's and this year's stress
levels. This tells me that this year's stress levels are neither higher
nor lower than last years.
• What if I jump in my time machine again and go back 15 years. The
average age of my subjects is currently 26, so I will talk to them
when they are about 11. I collect their stress levels and then jump
back to the present and do another t-test, and I find out that their
stress levels are lower now than when they were younger. The
beauty of the two-tailed test is that when you run your numbers,
the math will tell you if it's significantly higher or lower.

34. graph

35. One-Tailed Test
• A one-tailed test, also known as a directional
hypothesis, is a test of significance to determine
if there is a relationship between the variables in
one direction. A one-tailed test is useful if you
have a good idea, usually based on your
knowledge of the subject, that there is going to
be a directional difference between the variables.
• It is used when we are to test whether the
population mean is either lower than or higher
than some hypothesised value.

36. graph

37. Example for one tailed and two tailed
tests
• Because the one-tailed test provides more power to detect an effect, you may be
tempted to use a one-tailed test whenever you have a hypothesis about the
direction of an effect.
• Before doing so, consider the consequences of missing an effect in the other
direction.
• Imagine you have developed a new drug that you believe is an improvement over
an existing drug. You wish to maximize your ability to detect the improvement, so
you opt for a one-tailed test. In doing so, you fail to test for the possibility that the
new drug is less effective than the existing drug. The consequences in this
example are extreme, but they illustrate a danger of inappropriate use of a one-
tailed test.
• So when is a one-tailed test appropriate? If you consider the consequences of
missing an effect in the untested direction and conclude that they are negligible
and in no way irresponsible or unethical, then you can proceed with a one-tailed
test. For example, imagine again that you have developed a new drug. It is cheaper
than the existing drug and, you believe, no less effective. In testing this drug, you
are only interested in testing if it less effective than the existing drug. You do not
care if it is significantly more effective. You only wish to show that it is not less
effective. In this scenario, a one-tailed test would be appropriate.

38. Forming/Developing a Hypothesis
• Articulating the hypotheses that will be tested
is one of the steps in the planning phase of a
research study
• A hypothesis is formulated after
– the problem has been stated and
– the literature study has been conducted
• It is formulated when the researcher is totally
aware of the theoretical and empirical
background to the problem

39. The Initial Idea
• The initial idea is the starting point
– Often vague or general, it requires refining before
research hypotheses can be generated
• Refinement of the initial idea is based on
(1) a search of relevant research literature
(2) initial observations of the phenomenon
• Narrow and formalize the initial idea into a
statement of the problem

40. Statement of the Problem
• In the form of a question that clearly indicates
an expected relationship
– The nature of the question will dictate the required
level of constraint of a study
• Causal questions will require experimental research
• Questions about relationships can be answered with lower
constraint research
• Convert into research hypothesis by
operationally defining the variables

41. Hypothesis Testing
• All hypothesis tests are conducted the same way.
• The researcher
1. states a hypothesis to be tested,
2. formulates an analysis plan,
3. analyzes sample data according to the plan, and
4. accepts or rejects the null hypothesis, based on
results of the analysis.

42. Hypothesis Testing (Cont..)
1. State the hypotheses.
Every hypothesis test requires the analyst to state a null and an alternative
hypothesis. The hypotheses are stated in such a way that they are mutually
exclusive. That is, if one is true, the other must be false; and vice versa.
2. Formulate an analysis plan.
The analysis plan describes how to use sample data to accept or reject the null
hypothesis. It should specify the following elements.
– Significance level.
Often, researchers choose significance levels equal to 0.01, 0.05, or 0.10; but any value between
0 and 1 can be used.
– Test method.
Typically, the test method involves a test statistic and a sampling distribution. Computed from
sample data, the test statistic might be a mean score, proportion, difference between means,
difference between proportions, z-score, t-score, chi-square, etc. Given a test statistic and its
sampling distribution, a researcher can assess probabilities associated with the test statistic. If
the test statistic probability is less than the significance level, the null hypothesis is rejected.

43. Hypothesis Testing (Cont..)
• Analyze sample data.
Using sample data perform computations called for in the analysis plan.
Test statistic.
When the null hypothesis involves a mean or proportion, use either of the
following equations to compute the test statistic.
• Test statistic = (Statistic - Parameter) / (Standard deviation of statistic)
Test statistic = (Statistic - Parameter) / (Standard error of statistic)
• where Parameter is the value appearing in the null hypothesis, and Statistic is
the point estimate of Parameter. As part of the analysis, you may need to compute
the standard deviation or standard error of the statistic. Previously, we presented
common formulas for the standard deviation and standard error.
When the parameter in the null hypothesis involves categorical data, you may use
a chi-square statistic as the test statistic. Instructions for computing a chi-square
test statistic are presented in the lesson on the chi-square goodness of fit test.
– P-value. The P-value is the probability of observing a sample statistic as extreme as the test
statistic, assuming the null hypothesis is true.

44. Flow Diagram for Hypothesis testing

45. Tests Of Hypotheses
• Hypothesis testing determines the validity of the
assumption with a view to choose between two conflicting
hypotheses about the value of a population parameter.
• Hypothesis testing helps to decide on the basis of a sample
data , whether a hypothesis about the population is likely
to be true or false.
• Statisticians have developed several tests of hypotheses
(also known as tests of significance) for the purpose of
testing of hypothesis which can be classified as
a. Parametric tests or standard tests of hypotheses
b. Non-Parametric tests or distribution –free tests of
hypothesis.

46. Tests Of significance

47. Parametric tests
• If the information about the population is
completely known by means of its parameters
then statistical test is called parametric test
• Eg: t- test, f-test, z-test, ANOVA

48. Nonparametric test
• If there is no knowledge about the population or
parameters, but still it is required to test the
hypothesis of the population. Then it is called
non-parametric test
• Sample distribution is unknown.
• When the population distribution is abnormal i.e.
too many variables involved.
• Eg: mann-Whitney, rank sum test, Kruskal-Wallis
test

49. scale of measurement
• define an attribute
• e.g. gender, marital statusNominal
•rank or order the observations as scores
or categories from low to high in terms of
«more or less»
•e.g. education, attitude/opinion scales
Ordinal
• interval between observations in
terms of fixed unit of measurement
• e.g. measures of temperature
Interval
• The scale has a fundamental zero
point
• e.g. age, income
Ratio
Nonparametric
*Parametric

50. 1. Nominal data synonymous with categorical data, assigned names/ categories based on
characters with out ranking between categories. ex. male/female, yes/no, death /survival.
2. Ordinal data ordered or graded data, expressed as Scores or ranks. ex. pain graded as
mild, moderate and severe
3. Interval data an equal and definite interval between two measurements it can be
continuous or discrete .ex. weight expressed as 20, 21,22,23,24 here,interval between 20 &
21 is same as 23 &24

51. In addition to scale of measurement, we should
look at the population distribution.
• Population is normally distributed-
Parametric (may be used)
• Not normally distributed population or no
assumption can be made about the
population distribution -Nonparametric
(have to be used)

53. SAMPLE SIZE:
• Large Sample : sample of size is more than 30
• Small Sample: sample of size less than or equal
to 30
• Many statistical test are based upon the
assumption that the data are sampled from a
Gaussian distribution.
• Procedures for testing hypotheses about
parameters in a population described by a
specified distributional form, (normal
distribution) are called parametric tests.

54. Types of Parametric tests
1. Large sample tests
 Z-test
2. Small sample tests
 t-test
* Independent/ unpaired t-test
* Paired t-test
ANOVA (Analysis of variance)
* One way ANOVA
* Two way ANOVA

55. Z- Test:
• A z-test is used for testing the mean of a population
versus a standard, or comparing the means of two
populations, with large (n ≥ 30) samples whether you
know the population standard deviation or not.
• It is also used for testing the proportion of some
characteristic versus a standard proportion, or
comparing the proportions of two populations.
Ex. Comparing the average engineering salaries
of men versus women.
Ex. Comparing the fraction defectives from two
production lines.

56. T- test:
• Properties of t distribution:
i. It has mean 0
ii. It has variance greater than one
iii. It is bell shaped symmetrical distribution about mean
• Assumption for t test:
i. Sample must be random, observations independent
ii. Standard deviation is not known
iii. Normal distribution of population
Uses of t test:
i. The mean of the sample
ii. The difference between means or to compare two samples
iii. Correlation coefficient
Types of t test:
a. Paired t test
b. Unpaired t test

57. Paired t test:
• Consists of a sample of matched pairs of
similar units, or one group of units that has been
tested twice (a "repeated measures" t-test).
• Ex. where subjects are tested prior to a
treatment, say for high blood pressure, and the
same subjects are tested again after treatment
with a blood-pressure lowering medication.

58. Unpaired t test:
• When two separate sets of independent and
identically distributed samples are obtained, one from
each of the two populations being compared.
• Ex: 1. compare the height of girls and boys.
2. compare 2 stress reduction interventions
when one group practiced mindfulness meditation
while the other learned progressive muscle relaxation.

59. ANALYSIS OF VARIANCE(ANOVA):
• Analysis of variance (ANOVA) is a collection
of statistical models used to analyze the differences
between group means and their associated procedures
(such as "variation" among and between groups),
• Compares multiple groups at one time
• Developed by R.A. Fisher.
• Two types: i. One way ANOVA
ii. Two way ANOVA

60. One Way ANOVA:
It compares three or more unmatched groups when
data are categorized in one way
Ex.
1. Compare control group with three different
doses of aspirin in rats
2. Effect of supplementation of vit C in each
subject before , during and after the treatment.

61. Two way ANOVA:
• Used to determine the effect of two nominal
predictor variables on a continuous outcome
variable.
• A two-way ANOVA test analyzes the effect of the
independent variables on the expected outcome
along with their relationship to the outcome
itself.

62. Difference between one & two way
ANOVA
• An example of when a one-way ANOVA could be used is if we want to determine if there is a
difference in the mean height of stalks of three different types of seeds. Since there is more than
one mean, we can use a one-way ANOVA since there is only one factor that could be making the
heights different.
• Now, if we take these three different types of seeds, and then add the possibility that three
different types of fertilizer is used, then we would want to use a two-way ANOVA.
• The mean height of the stalks could be different for a combination of several reasons:
• The types of seed could cause the change,
the types of fertilizer could cause the change, and/or there is an interaction between the type of
seed and the type of fertilizer.
• There are two factors here (type of seed and type of fertilizer), so, if the assumptions hold, then we
can use a two-way ANOVA.

63. Parametric tests applied for different
type of data

64. Types of Non-parametric test
1. One sample test
• Chi-square test
• One sample sign test
2. Two samples test
• Median test
• Two samples sign test
3. K-samples test
• Median tets
• Kruskal Wallis test

65. Types of Non-parametric test
• Chi-square test (χ2):
– Used to compare between observed and expected data.
1. Test of goodness of fit
2. Test of independence
3. Test of homogeneity
• Kruskal-Wallis test-
– for testing whether samples originate from the same distribution.
– used for comparing more than two samples that are independent, or
not related
– Alternative to ANOVA.
• Wilcoxon signed-rank-
– used when comparing two related samples or repeated
measurements on a single sample to assess whether their population
mean ranks differ.

66. • Median test-
– Use to test the null hypothesis that the medians of the
populations from which two samples are drawn are identical.
– The data in sample is assigned to two groups, one consisting of
data whose values are higher than the median value in the two
groups combined, and the other consisting of data whose values
are at the median or below
• Sign test:
– can be used to test the hypothesis that there is "no difference in
medians" between the continuous distributions of two random
variables X and Y,
• Fisher's exact test:
– test used in the analysis of contingency where sample sizes are
small

67. Difference between parametric and
non-parametric tests

70. References
1. Dr J V Dixit’s Principles and practice of
biostatistics 5th edition.
2. Rao & Murthy’s applied statistics in health
sciences 2nd edition.
3. Sarmukaddam’s fundamentals of biostatistics
1st edition.
4. Internet sources…….