The document provides an in-depth overview of qualitative and quantitative research methods, particularly focusing on various research designs such as descriptive, observational, exploratory, and experimental studies. It outlines the merits and demerits of different study types, including case reports, case series, and cohort studies, as well as the importance of statistical analysis and reporting standards in health research. The presentation emphasizes the necessity of careful study design and execution to ensure valid and reliable research outcomes.

1. Qualitative and Quantitative
Research Methods: Statistical
Software Based Training
Prof. (Dr.) Asir John Samuel, BSc (Psych), BPT, MPT, PhD (Physiotherapy)
Professor
Maharishi Markandeshwar Institute of Physiotherapy and Rehabilitation
Maharishi Markandeshwar (Deemed-to-be University)
Accredited by NAAC with Grade A++
Mullana-Ambala, Haryana, India
NORMALIZING LIFE POST COVID 19
27th & 28th March 2022
Prof. Asir John Samuel, Professor, MMIPR 1

2. Prof. Asir John Samuel, Professor, MMIPR 2
Good Morning

3. Why do research?
• One might want to get degree
• One might want to become famous
• One might want to get rich
• One might want to get a free trip
• One might want to help patients
• One might want to discover something new
Asir John Samuel, Assoc. Prof, MMIPR
http://orcid.org/0000-0003-1747-0415

4. First research?
• Adam and eve
Asir John Samuel, Assoc. Prof, MMIPR
http://orcid.org/0000-0003-1747-0415

5. Asir John Samuel, Assoc. Prof, MMIPR
http://orcid.org/0000-0003-1747-0415

6. Prof. Asir John Samuel, Professor, MMIPR 6

7. Research Design
Prof. Asir John Samuel, Professor, MMIPR 7

8. Different research designs
Study designs
Observational
Descriptive
Case report Case series
Analytical
Cross-
sectional
study
Case control
study
Cohort
Experimental
RCT Non-RCT
Prof. Asir John Samuel, Professor, MMIPR 8

9. Research Designs by
Portney & Watkins
Describe
population
Descriptive
Research Find
Relationship
Exploratory
Research
Cause
and
Effect
Experimental
Research
Portney LG, Watkins MP. Foundations of Clinical Research: Applications to Practice.
3rd ed. Upper Saddle River, NJ: Prentice Hall; 2015
Prof. Asir John Samuel, Professor, MMIPR 9

10. 10
Portney LG, Watkins MP. Foundations of Clinical Research: Applications to Practice.
3rd ed. Upper Saddle River, NJ: Prentice Hall; 2015. p.21
Prof. Asir John Samuel, Professor, MMIPR

11. Prof. Asir John Samuel, Professor, MMIPR 11

12. Descriptive research designs
• Developmental research
• Normative research
• Qualitative research
• Descriptive survey
• Case study
• Case series
Prof. Asir John Samuel, Professor, MMIPR 12

13. Descriptive research
Portney LG, Watkins MP. Foundations of Clinical Research: Applications to Practice.
3rd ed. Upper Saddle River, NJ: Prentice Hall; 2015. p.301
Prof. Asir John Samuel, Professor, MMIPR 13

14. Case report
• Studies describing the characteristics of a
single patient
• Collection of cases may be up to 4
Prof. Asir John Samuel, Professor, MMIPR 14

15. Purpose of case studies
• Understanding unusual patient conditions
• Providing examples of innovative or creative
therapies
• Generating and testing theory
• Providing future research directives
Prof. Asir John Samuel, Professor, MMIPR 15

16. Case report-merits
• Record unusual medical occurrences and can
give the first clues in identification of a new
disease or adverse effects of an exposure
• Only means of surveillance for rare clinical
events
• Serve to elucidate the mechanism of disease
and treatmentProf. Asir John Samuel, Professor, MMIPR 16

17. Case report-Demerits
• Cannot be used to test for the presence of
valid statistical association because it is based
on the experience of one person
Prof. Asir John Samuel, Professor, MMIPR 17

18. Case series
• Studies describing the characteristics of a
group of patients with similar diagnosis
• Collection of 5 & more cases
Prof. Asir John Samuel, Professor, MMIPR 18

19. Case series-merits
• Helps in formulating a useful hypothesis
regarding risk factors of disease or identifying
a new disease or outcome of new treatment
• Informative for very rare disease with few
established risk factors
• May suggest the emergence of a new disease
or epidemic
Prof. Asir John Samuel, Professor, MMIPR 19

20. Case series-Demerits
Cannot be used to test for the presence of valid
statistical association due to absence of a
comparison group
Prof. Asir John Samuel, Professor, MMIPR 20

21. Exploratory research designs
• Case-Control studies
• Cohort studies
• Cross-sectional studies
• Correlational research
• Methodological research reliability and validity
• Historical research
Prof. Asir John Samuel, Professor, MMIPR 21

22. Exploratory research
Portney LG, Watkins MP. Foundations of Clinical Research: Applications to Practice.
3rd ed. Upper Saddle River, NJ: Prentice Hall; 2015
Prof. Asir John Samuel, Professor, MMIPR 22

23. Exploratory research
Portney LG, Watkins MP. Foundations of Clinical Research: Applications to Practice.
3rd ed. Upper Saddle River, NJ: Prentice Hall; 2015
Prof. Asir John Samuel, Professor, MMIPR 23

24. Cross-sectional studies
• Single examination of a cross section of
population at one point of time
• Prevalence study
• Used to investigate non fatal diseases
• Both exposure and outcome (disease) are
determined simultaneously for each subject
• Provide information about the frequency or
characteristic of disease
Prof. Asir John Samuel, Professor, MMIPR 24

25. Cross-sectional studies-Merits
• Provide information about the frequency of an
attribute and potential risk factors
• Helps to generate a hypothesis
• Can give a good picture about the health care
needs of the population at the point of time
• Can be used to investigate multiple exposure and
multiple outcome
• Suitable for chronic cases
Prof. Asir John Samuel, Professor, MMIPR 25

26. Cross-sectional studies-Demerits
• Difficult to establish the time of sequence of
events
• They are not suitable to investigate rare diseases,
rare exposure or disease of short duration
• Being based on prevalent rather than incident
cases
• Limited value to investigate etiological
relationship Prof. Asir John Samuel, Professor, MMIPR 26

27. Case control study
• Type of analytical study
• By observation and analysis
• Retrospective evaluation to determine who
was exposed and who was not exposed –
retrospective study
Prof. Asir John Samuel, Professor, MMIPR 27

28. Case control study
• To examine the possible relation of an
exposure to certain disease
- Identify the individual having the disease –
case
- Individual don’t have the disease –
comparison purpose
Prof. Asir John Samuel, Professor, MMIPR 28

29. Case control study
Factor (s)
Present individuals with disease
TIME
Direction of enquiry
Absent individuals w/o disease
Prof. Asir John Samuel, Professor, MMIPR 29

30. Case control study
4 basic steps in conducting a case control study,
1. Selection of cases and controls
2. Matching
3. Measurement of exposure
4. Analysis and interpretation
Prof. Asir John Samuel, Professor, MMIPR 30

31. Case control study – Merits
• Quick, less expensive
• Well suited for disease with long latent period
• Optimal for evaluation of rare diseases
• Can study etiological factors for a single
disease
• Requires small sample than a cohort study
• No attrition (drop outs) problem
• Ethical problems are minimal, no risk to
subjects
Prof. Asir John Samuel, Professor, MMIPR 31

32. Case control study- Demerits
• More prone to bias
• Selection of appropriate control group may be
difficult
• Inefficient for evaluation of rare exposure
• Cannot directly measure incidence, can only
estimate relative risk
Prof. Asir John Samuel, Professor, MMIPR 32

33. Cohort study
• Forward looking study
• Prospective study
• Incidence study
• Longitudinal study
• There is regular follow up over a period of
time
Prof. Asir John Samuel, Professor, MMIPR 33

34. Cohort study
Factor (s)
Individuals exposed Present
TIME
Direction of enquiry
individuals unexposed Absent
Prof. Asir John Samuel, Professor, MMIPR 34

35. Cohort study
• Proceeds from cause to effect
• Exposure has occurred when the study is
initiated, but the disease has not occurred
Prof. Asir John Samuel, Professor, MMIPR 35

36. Cohort
• A group of people who share a common
characteristic or experience within a defined
time period
• Cohort must be free from disease under study
• Both groups (study cohort & control cohort)
should be equally susceptible for the disease
under study
• Should be comparable in all possible variables
Prof. Asir John Samuel, Professor, MMIPR 36

37. Cohort study
• Elements of a cohort study,
1. Selection of study subjects
2. Obtaining data on exposure
3. Selection of comparison group
4. Follow-up
5. Analysis
Prof. Asir John Samuel, Professor, MMIPR 37

38. Cohort study - Merits
• Incidence can be calculated
• Examines multiple effects of a single exposure
• Provides direct estimate of relative risk
• Minimizes bias
• Dose-response ratios can be calculated
• Elucidates temporal relationship b/w exposure
& disease Prof. Asir John Samuel, Professor, MMIPR 38

39. Cohort study - Demerits
• Inefficient for rare diseases
• Expensive and time consuming
• Involves large sample size
• Alters people behaviour
• Changes in standard methods or diagnostic
criteria of disease over prolonged follow-up
Prof. Asir John Samuel, Professor, MMIPR 39

40. Exploratory research
Portney LG, Watkins MP. Foundations of Clinical Research: Applications to Practice.
3rd ed. Upper Saddle River, NJ: Prentice Hall; 2015
Prof. Asir John Samuel, Professor, MMIPR 40

41. RCT
• Basic steps in conducting a RCT,
1. Drawing up a protocol
2. Selecting reference & exp. Group
3. Randomization
4. Manipulation or intervention
5. Follow-up
6. Assessment of outcome
Prof. Asir John Samuel, Professor, MMIPR 41

42. Select
suitable
population
Select
suitable
sample
Exclusions
Randomize
Experimental
group
Control group
Intervention
& follow-up
Prof. Asir John Samuel, Professor, MMIPR 42

43. Experimental (RCT) research designs
• Pretest-posttest control group design
• Two-group pretest-posttest design
• Posttest-only control group design
• One-way repeated measures design
• Crossover design
• Two-way design with two repeated measures
• Mixed design Prof. Asir John Samuel, Professor, MMIPR 43

44. Pretest-Posttest Control Group Design
Experimental
Group
Control
Group
Study
Sample
Pretest
Pretest
No
Intervention
or Placebo
Experimental
Intervention
Posttest
Posttest
Random
Assignment
Prof. Asir John Samuel, Professor, MMIPR 44

45. Two-Group Pretest-Posttest Design
Experimental
Group
Control
Group
Study
Sample
Pretest
Pretest
Experimental
Intervention
2 or
Standard care
Experimental
Intervention
1
Posttest
Posttest
Random
Assignment
Prof. Asir John Samuel, Professor, MMIPR 45

46. MultiGroup Pretest-Posttest Design
Experimental
Group 2
Control
Group
Study
Sample
Pretest
Pretest
No
Intervention or
Placebo
Experimental
Intervention
2
Posttest
Posttest
Experimental
Group 1
Pretest
Experimental
Intervention
1
Posttest
Random
Assignment
Random
Assignment
Prof. Asir John Samuel, Professor, MMIPR 46

47. Posttest-only Control Group Design
Experimental
Group
Control
Group
Study
Sample
No
Intervention
or Placebo
Experimental
Intervention
Posttest
Posttest
Random
Assignment
Prof. Asir John Samuel, Professor, MMIPR 47

48. Crossover design
Portney LG, Watkins MP. Foundations of Clinical Research: Applications to Practice.
3rd ed. Upper Saddle River, NJ: Prentice Hall; 2015
Prof. Asir John Samuel, Professor, MMIPR 48

49. Quasi-Experimental Design
Portney LG, Watkins MP. Foundations of Clinical Research: Applications to Practice.
3rd ed. Upper Saddle River, NJ: Prentice Hall; 2015
Prof. Asir John Samuel, Professor, MMIPR 49

50. Quasi-experimental study design
• An empirical study used to estimate the causal
impact of an intervention on its target
population
• Type of evaluation which aims to determine
whether a program or intervention has the
intended effect or not
Prof. Asir John Samuel, Professor, MMIPR 50

51. Founder
• Donald Thomas Campbell
• November 20, 1916 to May 5, 1996
• American social scientist
Prof. Asir John Samuel, Professor, MMIPR 51

52. Lacking key components
• Pre-post test design
• Treatment and control group
• Random assignment of participants
- Lack one or more of the above
Prof. Asir John Samuel, Professor, MMIPR 52

53. Concern about
• Internal validity
• Confounding variables
• Ethical issues
Prof. Asir John Samuel, Professor, MMIPR 53

54. Advantages
• Randomization is impractical
• Not Unethical
• Minimizes threats to external validity
• Natural experiments
• Continue as longitudinal study
Prof. Asir John Samuel, Professor, MMIPR 54

55. Disadvantages
• Impact of confounding variables
• Threat to internal validity
• Provide weaker evidence because of the lack
of randomness
• Extraneous variable
Prof. Asir John Samuel, Professor, MMIPR 55

56. Forms of quasi-exp. study
• Pre-post test design without a control group
• Pre-post test design with a control group
Prof. Asir John Samuel, Professor, MMIPR 56

57. One-group pretest-posttest design
Prof. Asir John Samuel, Professor, MMIPR 57

58. One-way repeated measures design
Prof. Asir John Samuel, Professor, MMIPR 58

59. Interrupted time-series design
Prof. Asir John Samuel, Professor, MMIPR 59

60. Nonequivalent pretest-posttest
control group design
Prof. Asir John Samuel, Professor, MMIPR 60

61. Nonequivalent posttest-only
control group design
Prof. Asir John Samuel, Professor, MMIPR 61

62. Single-subject designs
Portney LG, Watkins MP. Foundations of Clinical Research: Applications to Practice.
3rd ed. Upper Saddle River, NJ: Prentice Hall; 2015
Prof. Asir John Samuel, Professor, MMIPR 62

63. Reporting of health research studies
• CARE
- CAse Report (13)
• STROBE
- Strengthening the Reporting of Observational
Studies in Epidemiology (22)
63
www.equator-network.org
Prof. Asir John Samuel, Professor, MMIPR

64. Reporting of health research studies
• SPIRIT
- Standard Protocol Items Recommendations
for Interventional Trials (33)
• PRISMA (http://www.prisma-statement.org/)
- Preferred Reporting Items for Systematic
Reviews and Meta-Analyses (27)
www.equator-network.org
Prof. Asir John Samuel, Professor, MMIPR 64

65. Reporting of statistical analysis
• SAMPL
- Statistical Analyses and Methods in the
Published Literature
www.equator-network.org
Prof. Asir John Samuel, Professor, MMIPR 65

66. Reporting of health research studies
• CONSORT
- Consolidated Standards of Reporting Trials
(25)
• TREND
- Transparent Reporting of Evaluations with
Nonrandomized Designs (22)
www.equator-network.org
Prof. Asir John Samuel, Professor, MMIPR 66

67. Prof. Asir John Samuel, Professor, MMIPR 67

68. Choice of Design
–Research Questions
–Research Goals
–Researcher Beliefs and Values
–Researcher Skills
–Time and Funds
–Status of existent knowledge
–Nature and availability of information
–Available resources
Prof. Asir John Samuel, Professor, MMIPR 68

69. SACKETT LEVEL OF EVIDENCE
Prof. Asir John Samuel, Professor, MMIPR 69

70. PEDro scale
Prof. Asir John Samuel, Professor, MMIPR 70

71. Randomization
• Simple randomization
• Block randomization
• Stratified randomization
• Unequal randomization
• http://www.graphpad.com/quickcalcs/index.cfm
• http://www.randomization.com
Prof. Asir John Samuel, Professor, MMIPR 71

72. Simple Randomization
• Single sequence of random assignments
• Maintains complete randomness of the
assignment of a subject to a particular group
• Flipping a coin
• Heads - control, tails – treatment
• Unequal number of participants among groups
• Simple and easy to implement
Prof. Asir John Samuel, Professor, MMIPR 72

73. Block randomization
• Randomize subjects into groups in equal
sample sizes
• Blocks are small and balanced with
predetermined group assignments
• Numbers of subjects in each group similar at
all times
• Subjects are randomly assigned into blocks
Prof. Asir John Samuel, Professor, MMIPR 73

74. Block randomization
Prof. Asir John Samuel, Professor, MMIPR 74

75. Stratified randomization
• Control and balance the influence of covariates
• Generate a separate block for each combination
of covariates
• Subjects are assigned to the appropriate block
of covariates
• Simple randomization is performed within each
block Prof. Asir John Samuel, Professor, MMIPR 75

76. Allocation concealment
• Generation of an unpredictable randomised
allocation sequence
• concealing it at least until patients have been
assigned to their groups
• Sequentially numbered, opaque, sealed
envelopes (SNOSE)
Prof. Asir John Samuel, Professor, MMIPR 76

77. Allocation concealment
Prof. Asir John Samuel, Professor, MMIPR 77

78. Systematic Reviews
and
Meta-analysis
Prof. Asir John Samuel, Professor, MMIPR 78

79. Systematic Reviews and
Meta-Analyses
Portney LG, Watkins MP. Foundations of Clinical Research: Applications to Practice.
3rd ed. Upper Saddle River, NJ: Prentice Hall; 2015
Prof. Asir John Samuel, Professor, MMIPR 79

80. Reasons to synthesize the literature
• To identify experts in a given topic
• More deeper understanding of a given topic
• Identify research methods
• Single conclusion
Prof. Asir John Samuel, Professor, MMIPR 80

81. Ways to synthesize literature
• Narrative reviews
• Systematic reviews without meta-analysis
• Systematic reviews with meta-analysis
Prof. Asir John Samuel, Professor, MMIPR 81

82. Systematic reviews without meta-analysis
• Compiling studies on a focused topic based on
predefined inclusion and exclusion criteria
• Reduces bias in interpreting results
• Consider negative studies
• Come with single unique conclusion
Prof. Asir John Samuel, Professor, MMIPR 82

83. Systematic reviews with meta-analysis
• Meta-analysis is the analysis of analyses
• The size of differences b/w treatment groups
(effect size) is mathematically standardized so
that it can be pooled across studies with
different, conceptually related, dependent
variables
• Include statistical pooling of results
Prof. Asir John Samuel, Professor, MMIPR 83

84. 1. Framing questions for a review
2. Identifying relevant work
3. Assessing the quality of studies
4. Summarizing the evidence
5. Interpreting the findings
Prof. Asir John Samuel, Professor, MMIPR 84

85. Steps in conducting Meta-analysis
1. Framing questions for a review
2. Identifying relevant work
3. Assessing the quality of studies
4. Statistical pooling of results
5. Interpreting the findings
Prof. Asir John Samuel, Professor, MMIPR 85

86. Framing questions for a review
• Specified in a clear form before beginning
• Once protocol is set difficult to change
1. Free-form questions
2. Structured question
• Based on population, intervention, outcome
and study designs
Prof. Asir John Samuel, Professor, MMIPR 86

87. Identifying relevant work
• Extensive search without language restrictions
• Begin with bibliographic databases
• Most researchers will “play around”
• Printed, online, published and unpublished
• Based on selection criteria set in advance
• Follows strict inclusion and exclusion criteria
Prof. Asir John Samuel, Professor, MMIPR 87

88. Assessing the quality of studies
• Based on LoE
• Use critical appraisal guidelines & design-
based quality checklists
• Explore heterogeneity
• PEDro for RCT’s
Prof. Asir John Samuel, Professor, MMIPR 88

89. Summarizing the evidence
• Summarize from various study quality
• Difference b/w study results
• Compare negative
and positive studies
Prof. Asir John Samuel, Professor, MMIPR 89

90. Statistical pooling of results
• Use of confidence intervals
• Pooling of effects across studies
• Mathematical complexities are often elegantly
summarized in a “forest plot”/blobbogram
• Pat Forrest dates back to 1970s
Prof. Asir John Samuel, Professor, MMIPR 90

91. Forrest Plot
Prof. Asir John Samuel, Professor, MMIPR 91

92. Prof. Asir John Samuel, Professor, MMIPR 92

93. Interpreting the findings
• Care is taken in interpreting the results of low
quality studies
Prof. Asir John Samuel, Professor, MMIPR 93

94. Prof. Asir John Samuel, Professor, MMIPR 94

95. Prof. Asir John Samuel, Professor, MMIPR 95

96. Surveys and
Questionnaires
Prof. Asir John Samuel, Professor, MMIPR 96

97. Surveys and Questionnaires
Portney LG, Watkins MP. Foundations of Clinical Research: Applications to Practice.
3rd ed. Upper Saddle River, NJ: Prentice Hall; 2015
Prof. Asir John Samuel, Professor, MMIPR 97

98. Survey
• System for collecting information from or
about people to describe, compare, or explain
their knowledge, attitudes, and behaviour
- Fink A (2003)
Prof. Asir John Samuel, Professor, MMIPR 98

99. Survey
• Non-experimental research
• Prospective collection of self-reported
information
• 2 methods
- Questionnaires (mailed, internet)
- Interview (in-person or telephone)
Prof. Asir John Samuel, Professor, MMIPR 99

100. Steps in Implementation
• Sampling and sample size
• Interview or questionnaire design
• Implementation
• Follow-up
• Analysis
• Interpretation/inference
Prof. Asir John Samuel, Professor, MMIPR 100

101. Sampling and sample size
N = Z2 (p)(1-p) / error2
Prof. Asir John Samuel, Professor, MMIPR 101

102. Questionnaire development
• Drafting
- Needs based on literature
- Reexamine the purpose of study
- Format and comprehensibility
- More easily digestible parts
- readability
Prof. Asir John Samuel, Professor, MMIPR 102

103. Questionnaires
• Consists of no. of questions printed or typed
in definite order on a form or set of forms
• Respondents have to answer the questions on
their own
• Data is collected through mailing or
distributing the questionnaires
• Used in survey
Prof. Asir John Samuel, Professor, MMIPR 103

104. 3 main aspects
• General form
• Question sequence
• Question formulation and wording
Prof. Asir John Samuel, Professor, MMIPR 104

105. General form
• Structured/unstructured
• Closed (Y/N) or open (inviting free response)
• In pilot study unstructured then standardized
Prof. Asir John Samuel, Professor, MMIPR 105

106. Question sequence
• Clear and smoothly-moving
• Easiest question being put in beginning
• Avoid question that put too great strain on
memory, personal character, wealth, etc
• General to more specific
Prof. Asir John Samuel, Professor, MMIPR 106

107. Question formulation and wording
• Short and Easily understood
• Simple and convey only one thought at a time
• Concrete
• Can be dichotomous, multiple choice or open-
ended
Prof. Asir John Samuel, Professor, MMIPR 107

108. Questionnaires - Merits
• Low cost even when sample size is large
• Free from bias of interviewer
• Respondent who are not easily approachable
can be reached conveniently
• More reliable
• Respondents have adequate time to answer
Prof. Asir John Samuel, Professor, MMIPR 108

109. Questionnaires - Demerits
• Low rate of return of duly filled in
questionnaires
• Can be used only when respondents are
educated and cooperating
• There is inbuilt inflexibility
• Slowest of all
Prof. Asir John Samuel, Professor, MMIPR 109

110. Questionnaires - Demerits
• Interpretation of omissions is difficult
• Control over the questionnaire may be lost
once it is sent
• Difficult to know whether willing respondents
are true representative
Prof. Asir John Samuel, Professor, MMIPR 110

111. Questionnaire development
• Expert review
- Review by a colleague or colleagues
knowledgeable about the topic under study
- All important elements of constructs under
were addressed ?
- Were Q understandable?
Prof. Asir John Samuel, Professor, MMIPR 111

112. Questionnaire development
• First revision
- Accept or reject recommendations for change
- After critical or peer review
• Pilot test
- Return rate/response rate
- Difficulty in reaching
Prof. Asir John Samuel, Professor, MMIPR 112

113. Survey method
• Final revision
• Motivating prospects to respond
• Implementation
• Data analysis
• Reporting
Prof. Asir John Samuel, Professor, MMIPR 113

114. Sampling
Prof. Asir John Samuel, Professor, MMIPR 114

115. Basic definitions
• Population
- Collection of all the units that are of interest
to the investigator
• Sample
- Representative part of population
• Sampling
- Technique of selecting a representative group
from a population
Prof. Asir John Samuel, Professor, MMIPR 115

116. Basic definitions
• Sampling error
- The difference that occurs purely by chance
between the values of sample statistic and
that of the corresponding population
parameter
Prof. Asir John Samuel, Professor, MMIPR 116

117. Why ?
• Only feasible method for collecting information
• Reduces demands on resources (time, finance,.)
• Results obtained more quickly
• Better accuracy of collected data
• Ethically acceptable
Prof. Asir John Samuel, Professor, MMIPR 117

118. Steps in sampling design
Target
population
Study
population
Sample
Study
participation
Prof. Asir John Samuel, Professor, MMIPR 118

119. Characteristic of good sample design
• True representation of population
• May result in small sampling error
• Each member in population should get an
opportunity of being selected
• Systematic bias can be controlled in a better way
• Results should be capable of being extrapolated
Prof. Asir John Samuel, Professor, MMIPR 119

120. Types of sample design
• Probability/Random sampling
- Selection of subjects are according to any
predicted chance of probability
• Non-probability/non-random sampling
- Does not depend on any chance of predecided
probability
Prof. Asir John Samuel, Professor, MMIPR 120

121. Types of sample design
Sample
design
Random
sampling
Simple Stratified Systematic Cluster Multistage
Non-random
sampling
convenience Quota Judgment
Prof. Asir John Samuel, Professor, MMIPR 121

122. Simple random sampling
• Equal and independent chance or probability
of drawing each unit
• Take sampling population
• Need listing of all sampling units (sampling
frame)
• Number all units
• Randomly draw units
Prof. Asir John Samuel, Professor, MMIPR 122

123. How to ensure randomness?
• Lottery method
• Table of random numbers
- e.g. Tippett’s series
- Fisher and Yates series
- Kendall and Smith series
- Rand corporation series
Prof. Asir John Samuel, Professor, MMIPR 123

124. Table of Random Numbers
Prof. Asir John Samuel, Professor, MMIPR 124

125. SRS - Merits
• No personal bias
• Easy to assess the accuracy
Prof. Asir John Samuel, Professor, MMIPR 125

126. SRS - Demerits
• Need a complete catalogue of universe
• Difficult if size sample is large
• Widely dispersed
Prof. Asir John Samuel, Professor, MMIPR 126

127. Stratified Random Sampling
• Used for heterogeneous population
• Population is divided into homogeneous
groups (strata), according to a characteristic of
interest (e.g. sex, religion, location)
• Then a simple random sample is selected from
each stratum
Prof. Asir John Samuel, Professor, MMIPR 127

128. SRs - Merits
• More representative
• Greater accuracy
• Can acquire information about whole
population and individual strata
Prof. Asir John Samuel, Professor, MMIPR 128

129. SRs - Demerits
• Careful stratification
• Random selection in each stratum
• Time consuming
Prof. Asir John Samuel, Professor, MMIPR 129

130. Systematic Sampling
• Sampling units are selected in a systematic
way, that is, every Kth unit in the population is
selected
• First divide the population size by the,
required sample size (sampling fraction). Let
the sampling fraction be K
Prof. Asir John Samuel, Professor, MMIPR 130

131. Systematic Sampling
• Select a unit at random from the first K units
and thereafter every Kth unit is selected
• If, N=1200
• And n=60
• Then, SF=20
Prof. Asir John Samuel, Professor, MMIPR 131

132. SS - Merits
• Simple and convenient
• Less time and work
Prof. Asir John Samuel, Professor, MMIPR 132

133. SS - Demerits
• Need complete list of units
• Periodicity
• Less representation
Prof. Asir John Samuel, Professor, MMIPR 133

134. Cluster Sampling
• The sampling units are groups or clusters
• The population is divided into clusters, and a
sample of clusters are selected randomly
• All the units in the selected clusters are then
examined or studied
Prof. Asir John Samuel, Professor, MMIPR 134

135. Types of cluster sampling
• Single-stage cluster sampling
- All the elements from each of the selected
clusters are used
• Two-stage cluster sampling
- A random sampling technique is applied to the
elements from each of the selected clusters
Prof. Asir John Samuel, Professor, MMIPR 135

136. Cluster Sampling
• It is always assumed that the individual items
within each cluster are representation of
population
• E.g. District, wards, schools, industries
Prof. Asir John Samuel, Professor, MMIPR 136

137. CS - Merits
• Saving of travelling time and consequent
reduction in cost
• Cuts down on the cost of preparing the
sampling frame
Prof. Asir John Samuel, Professor, MMIPR 137

138. CS - Demerits
• Units close to each other may be very similar
and so, less likely to represent the whole
population
• Larger sampling error than simple random
sampling
Prof. Asir John Samuel, Professor, MMIPR 138

139. Multistage Sampling
• Selection is done in stages until final sampling
units are arrived
• At first stage, Random sampling of large sized
sampling units are selected, from the selected
1st stage sampling units another sampling
units of smaller sampling units are selected,
randomly Prof. Asir John Samuel, Professor, MMIPR 139

140. Multistage Sampling
• Continue until the final sampling units are
selected
• E.g. Few states – District – Taulk
Prof. Asir John Samuel, Professor, MMIPR 140

141. MS - Merits
• Cut down the cost of preparing the sampling
frame
Prof. Asir John Samuel, Professor, MMIPR 141

142. MS - Demerits
• Sampling error is increased compared to
simple random sampling
Prof. Asir John Samuel, Professor, MMIPR 142

143. Quota Sampling
• Interviewers are requested to find cases with
particular types of people to interview
Prof. Asir John Samuel, Professor, MMIPR 143

144. Judgment (Purposive Sampling)
• Researcher attempts to obtain sample that
appear to be representative of the population
selected by the researcher subjectively
Prof. Asir John Samuel, Professor, MMIPR 144

145. Convenience Sampling
• Sampling comprises subject who are simply
avail in a convenient way to the researcher
• No randomness and likelihood of bias is high
• Consecutive sampling
Prof. Asir John Samuel, Professor, MMIPR 145

146. Snowball Sampling
• Investigators start with a few subjects and
then recruit more via word of mouth from the
original participants
Prof. Asir John Samuel, Professor, MMIPR 146

147. Merits
• Easy
• Low cost
• Limited time
• Total list population
Prof. Asir John Samuel, Professor, MMIPR 147

148. Demerits
• Selection bias
• Sample is not representation of population
• doesn’t allow generalization
Prof. Asir John Samuel, Professor, MMIPR 148

149. Validity and
Reliability
Prof. Asir John Samuel, Professor, MMIPR 149

150. Validity
• Validity of an assessment is the degree to
which it measures what it is supposed to
measure
Prof. Asir John Samuel, Professor, MMIPR 150

151. Reliability
• Reliability is the extent to which a
measurement gives results that are consistent
• Reliability is used to describe the overall
consistency of a measure
• A measure is said to have a high reliability if it
produces similar results under consistent
conditions
Prof. Asir John Samuel, Professor, MMIPR 151

152. Prof. Asir John Samuel, Professor, MMIPR 152

153. Prof. Asir John Samuel, Professor, MMIPR 153

154. Prof. Asir John Samuel, Professor, MMIPR 154

155. Prof. Asir John Samuel, Professor, MMIPR 155

156. Prof. Asir John Samuel, Professor, MMIPR 156

157. Validity
• Construct validity
• Content validity
• Face validity
• Criterion-related validity
- Concurrent validity
- Predictive validity
Prof. Asir John Samuel, Professor, MMIPR 157

158. Construct validity
• Construct validity refers to the extent to which
operations of a construct do actually measure
what the theory says they do
• Measure an abstract concept, or construct
Prof. Asir John Samuel, Professor, MMIPR 158

159. Content validity
• Content validity is a non-statistical type of
validity that involves "the systematic
examination of the test content to determine
whether it covers a representative sample of
the behavior domain to be measured“
• E.g. VAS vs McGill Pain Questionnaire
Prof. Asir John Samuel, Professor, MMIPR 159

160. Representation validity
• Representation validity also known as
translation validity, is about the extent to
which an abstract theoretical construct can be
turned into a specific practical test
Prof. Asir John Samuel, Professor, MMIPR 160

161. Face validity
• Face validity is an estimate of whether a test
appears to measure what it is supposed to
• Measures may have high validity, but when
the test does not appear to be measuring
what it is, it has low face validity
Prof. Asir John Samuel, Professor, MMIPR 161

162. Criterion-related validity
• Criterion validity is the correlation between
the test and a criterion variable (or variables)
taken as representative of the construct
• Compares the test with other measures (gold
standard) or outcomes (the criteria) already
held to be valid
Prof. Asir John Samuel, Professor, MMIPR 162

163. Concurrent validity
• Concurrent validity refers to the degree to
which the operations correlates with other
measures of the same construct that are
measured at the same time
Prof. Asir John Samuel, Professor, MMIPR 163

164. Predictive validity
• Predictive validity refers to the degree to
which the operation can predict (or correlate
with) other measures of the same construct
that are measured at some time in the future
• E.g. BBS or TUG predicts risk of fall
Prof. Asir John Samuel, Professor, MMIPR 164

165. Reliability
• Inter-rater reliability
• Test-retest reliability
• Inter-method reliability
Prof. Asir John Samuel, Professor, MMIPR 165

166. Inter-rater reliability
• Degree to which test scores are consistent
when measurements are taken by different
people using the same methods
Prof. Asir John Samuel, Professor, MMIPR 166

167. Test-retest/Intra-rater reliability
• Degree to which test scores are consistent
from one test administration to the next
• Measurements are gathered from a single
rater who uses the same methods or
instruments and the same testing conditions
• Intra-rater reliability
Prof. Asir John Samuel, Professor, MMIPR 167

168. Inter-method reliability
• Degree to which test scores are consistent
when there is a variation in the methods or
instruments used
Prof. Asir John Samuel, Professor, MMIPR 168

169. Bio-statistics for
physiotherapists
Asir John Samuel, BSc (Psy), MPT (Neuro Paed), MAc, DYScEd, FAGE
Assistant Professor, MMIPR
Mullana-Ambala,
Haryana
Prof. Asir John Samuel, Professor, MMIPR 169

170. Statistics in Medical Research
• Documentation of medical history of disease,
their progression, variability b/w patient,
association with age, gender, etc.
• Efficacy of various types of therapy
• Definition of normal range
• Used in Epidemiological studies
Prof. Asir John Samuel, Professor, MMIPR 170

171. Statistics in Medical Research
• Study the effect of environment, socio-
economic and seasonal factors
• Provide assessment of state health in
common, met and unmet needs
• Success/failure of specific treatment
• Promote health legislation
• Evaluate total health programme of action
Prof. Asir John Samuel, Professor, MMIPR 171

172. Statistics in Medical Research - Limitation
• Does not deal with individual fact
• Conclusion are not exact
• Can be misused
• Common men cannot handle properly
Prof. Asir John Samuel, Professor, MMIPR 172

173. Normal distribution
• Represented by a family of infinite curves
defined uniquely by 2 parameter the mean
and the SD of the population
• The curve are always symmetrically bell
shaped. The width of the curve is defined by
population, SD
Prof. Asir John Samuel, Professor, MMIPR 173

174. Normal distribution
• Mean, median and mode coincide
• It extends from - ∞ to + ∞
• Symmetrically about the mean
• Approx 68% of distribution is within 1SD of
mean (68.27%)
- 95% - 2SD (1.96 SD)
- 99% - 3SD (2.58 SD)
Prof. Asir John Samuel, Professor, MMIPR 174

175. Normal distribution
• The total area under the curve is 1
• The value of measure of skewness is zero. It is
not skewed
• The curve is asymptotic. It approaches but
never touches baseline at extremes
• The curve extends on the both sides -3σ
distance on left to +3σ distance on the right
Prof. Asir John Samuel, Professor, MMIPR 175

176. Normal distribution - Uses
• Construct confidence interval
• Many statistical techniques makes an
underlying assumption of normality
• Distribution of sample means is normal
• Normality is important in statistical inference
Prof. Asir John Samuel, Professor, MMIPR 176

177. Normality test
• Shapiro-Wilk test
• Kolmogorov-Smirnov test
Prof. Asir John Samuel, Professor, MMIPR 177

178. Prof. Asir John Samuel, Professor, MMIPR 178

179. Prof. Asir John Samuel, Professor, MMIPR 179

180. Shapiro-Wilk test
• More appropriate for small sample sizes (< 50
samples)
• But can also handle sample sizes as large as
2000
• If the Sig. value of the Shapiro-Wilk Test is
greater the 0.05, the data is normal
• If it is below 0.05, the data significantly
deviate from a normal distribution
Prof. Asir John Samuel, Professor, MMIPR 180

181. Prof. Asir John Samuel, Professor, MMIPR 181

182. Kolmogorov-Smirnov test
• Appropriate for larger samples
• ≥ 50 samples
• If the Sig. value of the Kolmogorov-Smirnov test
is greater the 0.05, the data is normal
Prof. Asir John Samuel, Professor, MMIPR 182

183. Descriptive statistics
• Measures of location
- Central tendency
- Mean, median and mode
• Measures of variation
- Dispersion
- Range, quartile, IQR, variance and SD
Prof. Asir John Samuel, Professor, MMIPR 183

184. Prof. Asir John Samuel, Professor, MMIPR 184

185. Prof. Asir John Samuel, Professor, MMIPR 185

186. Sample Size
Determination
Prof. Asir John Samuel, Professor, MMIPR 186

187. MCID
• ‘the smallest difference in score in the domain
of interest which patients perceive as
beneficial…’, - Jaeschke
• MDC = SEM × √2 × 1.9
• SEM = SD pooled X √(1-r)
• MCID = 1 X MDC
Prof. Asir John Samuel, Professor, MMIPR 187

188. MDC - MCID
Prof. Asir John Samuel, Professor, MMIPR 188

189. Effect size
• Effect size index (ESI)
ESI = Mean Post – Mean Pre / S Pre
• Standard response mean (SRM)
SRM = Mean Post – Mean Pre / S Change
Prof. Asir John Samuel, Professor, MMIPR 189

190. Effect size grading
Prof. Asir John Samuel, Professor, MMIPR 190
Portney LG, Watkins MP. Foundations of Clinical Research: Applications to Practice. 3rd ed.
Upper Saddle River, NJ: Prentice Hall; 2015. p.649

191. p-value
• Probability of getting a minimal difference of
what has observed is due to chance
• Probability that the difference of at least as
large as those found in the data would have
occurred by chance
Prof. Asir John Samuel, Professor, MMIPR 191

192. Hypothesis
• Alternate hypothesis (HA)
- Statement predict that a difference or
relationship b/w groups will be demonstrated
• Null hypothesis (H0)
- Researcher anticipate “no difference” or “no
relationship”
Prof. Asir John Samuel, Professor, MMIPR 192

193. Decision for 5% LOS
• If p-value <0.05, then data is against null
hypothesis
• If p-value ≥0.05, then data favours null
hypothesis
Prof. Asir John Samuel, Professor, MMIPR 193

194. Type I & II errors
Possible states of Null Hypothesis
Possible
actions on
Null
Hypothesis
True False
Accept Correct
Action
Type II
error
Reject Type I
error
Correct
Action
Prob (Type I error) – α (LoS) – False positive
Prob (Type II error) – β – False negative
1-β – power of test
Dr. Asir John Samuel (PT), Ast Prof, MMIPR

195. Type I Error and Type II Error

197. Z values
Z 0.05 – 1.96 – 95%
Z 0.10 – 1.282 – 90%
Z 0.20 – 0.84 – 80%
Prof. Asir John Samuel, Professor, MMIPR 197

198. Comparison of 2 means
n= 2 [(Zα+Zβ)s/d]²
Zα – LoS
Zβ – power of study
s – pooled SD of the two sample
d – clinically significant difference
Prof. Asir John Samuel, Professor, MMIPR 198

199. Eg. for Comparison of 2 means
• A RCT to study the effect of BP reduction. One
group received a no intervention and other-
aerobic exercise. What would be the sample
size in order to provide the study with power
of 90% to detect a difference in sys. BP of 2
mm Hg b/w two groups at 5% LoS? The SD of
sys. BP is observed to be 6 mmHg.
Prof. Asir John Samuel, Professor, MMIPR 199

200. Estimating proportion
n = Z α² P (1-P) / d²
P – proportion of event in population
d – acceptable margin of error in estimating the
true population proportion
Prof. Asir John Samuel, Professor, MMIPR 200

201. Eg. Estimating proportion
• To determine the prevalence of navicular drop
in ACL injured population by anticipating of
15% with acceptable margin of error is 3%
= (1.96)²(0.15)(0.85) / (0.03)²
= 544.2
Prof. Asir John Samuel, Professor, MMIPR 201

202. Estimating mean
n = (Zα σ / d)²
σ – anticipated SD of population
d – acceptable margin of error in estimating true
population mean
Prof. Asir John Samuel, Professor, MMIPR 202

203. Eg. Estimating mean
• To determine the mean no. of days to
ambulate pt undergoing stroke rehabilation
among stroke pts. Where anticipated SD of
days are 60 and acceptable margin of error is
20 days
n = (1.96 x 60/20)²
n = (5.88)² = 34.6
Prof. Asir John Samuel, Professor, MMIPR 203

204. Comparison of 2 proportions
n = (Zα√2PQ + Zβ√P1Q1+P2Q2)²/(P1-P2)²
P = P1+P2/2 Q = 1-P
Prof. Asir John Samuel, Professor, MMIPR 204

205. Eg. Comparison of 2 proportions
• To see whether there is any sig. difference in
percentage of strength increase after 4 wks of
intervention b/w a new technique and
standard one
• Standard one – 70% (P1)
• New technique – 75% (P2)
Prof. Asir John Samuel, Professor, MMIPR 205

206. Multiple regression
N > 50 + 8p
Where,
p - number of predictors
Prof. Asir John Samuel, Professor, MMIPR 206

207. Testing of hypothesis
1. Evaluate data
2. Review assumption
3. State hypothesis
4. Presume null hypothesis
5. Select test statistics
6. Determine distribution of test statistics
7. State decision rule
Prof. Asir John Samuel, Professor, MMIPR 207

208. Testing of hypothesis
8. Calculate test statistics
9. What is the probability that the data conform
10. Make statistical decision
11. If p>0.05, then reject(HA)
12. If p<0.05, then accept (HA)
Prof. Asir John Samuel, Professor, MMIPR 208

209. Testing of Hypothesis
Presume null hypothesis
What is the probability
that data conform to
null hypothesis
Retain H0 reject H0
p>0.05 P<0.05
Prof. Asir John Samuel, Professor, MMIPR 209

210. Test of Hypothesis
• Parametric test
• Non-parametric test
Prof. Asir John Samuel, Professor, MMIPR 210

211. Parametric & non-parametric test
• Paired t-test
• Repeated measure
ANOVA
• Independent t-test
• ANOVA
• Pearson correlation
coefficient
• Wilcoxon Signed Rank T
• Friedman test
• Mann-Whitney U test
• Kruskal Wallis test
• Spearman Rank
correlation coefficient
Prof. Asir John Samuel, Professor, MMIPR 211

212. Paired t-test
• Two measures taken on the same subject or
naturally occurring pairs of observation or two
individually matched samples
• Variable of interest is quantitative
Prof. Asir John Samuel, Professor, MMIPR 212

213. Assumption
• The difference b/w pairs in the population is
independent and normally or approximately
normally distributed
Prof. Asir John Samuel, Professor, MMIPR 213

214. Wilcoxon Signed Rank test
• Used for paired data
• The sample is random
• The variable of interest is continuous
• The measurement scale is at least ordinal
• Based on the rank of difference of each paired
values
Prof. Asir John Samuel, Professor, MMIPR 214

215. Repeated measures ANOVA
• Measurements of the same variable are made
on each subject on more than two different
occasion
• The different occasions may be different point
of time or different conditions or different
treatments
Prof. Asir John Samuel, Professor, MMIPR 215

216. Assumptions
• Observations are independent
• Differences should follow normal distribution
• Sphericity-differences have approximately
same variances
Prof. Asir John Samuel, Professor, MMIPR 216

217. Fried Man test
• Data is measured in ordinal scale
• The subjects are repeatedly observed under 3
or more conditions
• The measurement scale is at least ordinal
(qualitative)
• The variable of interest is continuous
Prof. Asir John Samuel, Professor, MMIPR 217

218. Independent t-test
• Compare the means of two independent
random samples from two population
• Variable of interest is quantitative
Prof. Asir John Samuel, Professor, MMIPR 218

219. Assumptions
• The population from which the sample were
obtained must be normally or approximately
normally distributed
• The variances of the population must be equal
Prof. Asir John Samuel, Professor, MMIPR 219

220. Mann Whitney-U test
• Two independent samples have been drawn
from population with equal medians
• Samples are selected independently and at
random
• Population differ only with respect to their
median
• Variable of interval is continuous
Prof. Asir John Samuel, Professor, MMIPR 220

221. Mann Whitney-U test
• Measurement scale is at least ordinal
(qualitative)
• Based on ranks of the observations
Prof. Asir John Samuel, Professor, MMIPR 221

222. ANOVA
• Extension of independent t-test to compare
the means of more than two groups
• F = b/w group variation/within group variation
• F ratio
• Post hoc test (which mean is different)
Prof. Asir John Samuel, Professor, MMIPR 222

223. Assumptions
• Observations are independent and randomly
selected
• Each group data follows normal distribution
• All groups are equally variable (homogeneity
of variance)
Prof. Asir John Samuel, Professor, MMIPR 223

224. Why not t-test?
• Tedious
• Time consuming
• Confusing
• Potentially misleading – Type I error is more
Prof. Asir John Samuel, Professor, MMIPR 224

225. Kruskal Wallis H test
• Used for comparison of more than 2 groups
• Extension of Mann-Whitney U test
• Used for comparing medians of more than 2
groups
Prof. Asir John Samuel, Professor, MMIPR 225

226. Assumptions
• Samples are independent and randomly
selected
• Measurement scale is at least ordinal
• Variable of interest is continuous
• Population differ only with respect to their
medians
Prof. Asir John Samuel, Professor, MMIPR 226

227. Chi-square Test (x2)
• Variables of interest are categorical
(quantitative)
• To determine whether observed difference in
proportion b/w the study groups are
statistically significant
• To test association of 2 variables
Prof. Asir John Samuel, Professor, MMIPR 227

228. Chi-square Test-Assumption
• Randomly drawn sample
• Data must be reported in number
• Observed frequency should not be too small
• When observed frequency is too small and
corresponding expected frequency is less than
5 (<5) – Fischer Exact test
Prof. Asir John Samuel, Professor, MMIPR 228

229. Relationship
• Correlation (relative reliability)
• Regression (absolute reliability)
Prof. Asir John Samuel, Professor, MMIPR 229

230. Correlation
• Method of analysis to use when studying the
possible association b/w two continuous
variables
• E.g.
- Birth wt and gestational period
- Anatomical dead space and ht
- Plasma volume and body weight
Prof. Asir John Samuel, Professor, MMIPR 230

231. Correlation
• Scatter diagram
• Linear correlation
• Non-linear correlation
Prof. Asir John Samuel, Professor, MMIPR 231

232. Properties
• Scatter diagrams are used to demonstrate the
linear relationship b/w two quantitative
variables
• Pearson’s correlation coefficient is denoted by r
• r measures the strength of linear relationship
b/w two continuous variable (say x and y)
Prof. Asir John Samuel, Professor, MMIPR 232

233. Properties
• The sign of the correlation coefficient tells us
the direction of linear relationship
• The size (magnitude) of the correlation
coefficient r tells us the strength of a linear
relationship
Prof. Asir John Samuel, Professor, MMIPR 233

234. Properties
• Better the points on the scatter diagram
approximate a straight line, the greater is the
magnitude r
• Coefficient ranges from -1 ≤ r ≤ 1
Prof. Asir John Samuel, Professor, MMIPR 234

235. Interpretation
• r = 1, two variables have a perfect +ve linear
relationship
• r = -1, two variables have a perfect -ve linear
relationship
• r = 0, there is no linear relationship b/w two
variables
Prof. Asir John Samuel, Professor, MMIPR 235

236. Assumption
• Observations are independent
• Relationship b/w two variables are linear
• Both variables should be normal distributed
Prof. Asir John Samuel, Professor, MMIPR 236

237. Caution
• Correlation coefficient only gives us an
indication about the strength of a linear
relationship
• Two variables may have a strong curvilinear
relationship but they could have a weak value
for r
Prof. Asir John Samuel, Professor, MMIPR 237

238. Judging the strength – Portney &
Watkins criteria
• 0.00-0.25 – little or no relationship
• 0.26-0.50 – fair degree of relationship
• 0.51-0.75 – moderate to good degree of
relationship
• 0.76-1.00 – good to excellent relationship
Prof. Asir John Samuel, Professor, MMIPR
Portney LG, Watkins MP. Foundations of Clinical Research: Applications to Practice. 3rd ed.
Upper Saddle River, NJ: Prentice Hall; 2015
238

239. Spearman’s Rank correlation
• Non-parametric measure of correlation
between the two variables (at least ordinal)
• Ranges from -1 to +1
Eg:
- Pain score of age
- IQ and TV watched /wk
- Age and EEG output values
Prof. Asir John Samuel, Professor, MMIPR 239

240. Situation
• Relationship b/w two variables is non-linear
• Variables measured are at least ordinal
• One of the variables not following normal
distribution
• Based on the difference in rank between each
variable
Prof. Asir John Samuel, Professor, MMIPR 240

241. Assumption
• Observation are independent
• Samples are randomly selected
• The measurement scale is at least ordinal
Prof. Asir John Samuel, Professor, MMIPR 241

242. Shrout and Fleiss criteria for ICC
• < 0.4 = poor
• < 0.4 to 0.75 = moderate
• 0.75 to < 0.9 = good
• ≥ 0.9 = excellent
Prof. Asir John Samuel, Professor, MMIPR
Shrout PE, Fleiss JL. Intra-class correlations: uses in assessing rater reliability. Psychol Bull.
1979;86:420–428. 242

243. Regression
• Expresses the linear relationship in the form of
an equation
• In other words a prediction equation for
estimating the values of one variable given the
valve of the other,
y = a + bx
Prof. Asir John Samuel, Professor, MMIPR 243

244. Regression - eg
• Wt (y) and ht (x)
• Birth wt (y) and gestation period (x)
• Dead space (y) and height (x)
x and y are continuous
y-dependent variable
x-independent variable
Prof. Asir John Samuel, Professor, MMIPR 244

245. Regression line
• Shows how are variable changes on average
with another
• It can be used to find out what one variable is
likely to be (predict) when we know the other
provided the prediction is within the limits of
data range
Prof. Asir John Samuel, Professor, MMIPR 245

246. Regression analysis
• Derives a prediction equation for estimating
the value of one variable (dependent) given the
value of the second variable (independent)
y = a + bx
Prof. Asir John Samuel, Professor, MMIPR 246

247. Assumption
• Randomly selection
• Linear relationship between variables
• The response variable should have a normal
distribution
• The variability of y should be the same for
each value of the predictor value
Prof. Asir John Samuel, Professor, MMIPR 247

248. Prof. Asir John Samuel, Professor, MMIPR 248

249. Multiple regression
• One dependent variable and multiple
independent variable
• Derives a prediction equation for estimating
the value of one variable (dependent) given
the value of the other variables (independent)
Prof. Asir John Samuel, Professor, MMIPR 249

250. Multiple regression
• The dependent variable is continuous and
follows normal distribution
• Independent variable can be quantitative as
well as qualitative
Prof. Asir John Samuel, Professor, MMIPR 250

251. Prof. Asir John Samuel, Professor, MMIPR 251

252. 252