This document describes various research designs and methods. It discusses descriptive research which aims to describe characteristics of a population without explaining relationships between variables. Descriptive methods include surveys, case studies, and observational studies. Experimental research aims to establish cause-and-effect relationships by manipulating variables and using control groups. True experiments use random assignment while quasi-experiments do not. The document also discusses cohort and case-control studies as well as qualitative research methods like interviews. It provides examples of different research designs and their strengths and limitations.

1. MATERIALS AND
METHODS
Chapter III
Adopted by:
CHARLANE M. DIASNES, LPT

2. MATERIALS AND METHODS
• Research Design
• Sampling Techniques
• Research Instruments
• Collecting the Data
• Statistical Tools
• Descriptive
• Inferential

3. Limitations
• Science is imperfect, especially when it is applied to
human behavior and performance

4. RESEARCH DESIGN
• Refers to the overall plan and scheme for conducting
the study
• Descriptive Comparative Correlation
• Experimental

5. Types of Research
• Descriptive Comparative
Correlation
• Case study
• Cross-sectional study
• Qualitative study
• Experimental
• True experimental designs
• Quasi-experimental designs

6. Descriptive Research
• Descriptive research usually makes some type of
comparison contrasts and correlation and sometimes,
in carefully planned and orchestrated descriptive
researchers, cause-effect relationship may be
established to some extent.

7. Descriptive Research
• The purpose of this design is to describe the status
of events, people or subjects as they exist
• Present oriented
• Not able to explain the cause-effect relationship but
is able to provide clues to such relationships
• Describes and interprets what is currently prevailing

8. Descriptive Research
• Descriptive: investigator attempts to describe a group
of individuals on a set of variables or characteristics.
• Enables classification and understanding
• Methods: survey research, case study, qualitative,
developmental (natural history of something, patterns of
growth and change), normative, evaluation

9. • Aims to establish significant differences between two or
more groups of subjects on the basis of a criterion
measure (ex. Compare the managerial effectiveness of
three groups of managers A,B,C)
• Limitations
• The lack of control variables making them less reliable in
terms of actual hypothesis testing
• Unless the normative survey where the entire population is
considered , conclusions drawn from descriptive designs are at
best tentative
Descriptive Research

10. Descriptive Research
• Case study
• Cross-sectional study
• Qualitative study

11. Case Study Design
• Often a description of a individual case’s condition or response
to an intervention
• can focus on a group, institution, school, community, family, etc.
• data may be qualitative, quantitative, or both
• Case series: observations of several similar cases are reported

12. Case Study
Example
• In 1848, young railroad worker, Phineas Gage, was forcing gun powder into a rock
with a long iron rod when the gun powder exploded. The iron rod shot through his
cheek and out the top of his head, resulting in substantial damage to the frontal lobe
of his brain. Incredibly, he did not appear to be seriously injured. His memory and
mental abilities were intact, and he could speak and work. However, his personality
was markedly changed. Before the accident, he had been a kind and friendly person,
but afterward he became ill-tempered and dishonest.
• Phineas Gage’s injury served as a case study for the effects of frontal lobe damage.
He did not lose a specific mental ability, such as the ability to speak or follow
directions. However, his personality and moral sense were altered. It is now known
that parts of the cortex (called the association areas) are involved in general mental
processes, and damage to those areas can greatly change a person’s personality.

13. Case Study Design
• Strengths
• Enables understanding of the totality of an individual’s (or organization,
community) experience
• The in-depth examination of a situation or ‘case’ can lead to discovery of
relationships that were not obvious before
• Useful for generating new hypotheses or for describing new phenomena
• Weaknesses
• No control group
• Prone to selection bias and confounding
• The interaction of environmental and personal characteristics make it weak in
internal validity
• Limited generalizability

14. Cross-sectional Study
• Researcher studies a stratified group of subjects at one point in
time
• Draws conclusions by comparing the characteristics of the
stratified groups
• Well-suited to describing variables and their distribution
patterns
• Can be used for examining associations; determination of
which variables are predictors and which are outcomes depends
on the hypothesis
• eg. Does lead paint ingestion cause hyperactivity or does hyperactivity
lead to lead paint ingestion?

15. Cross-sectional Study
• Example:
What is the prevalence of chlamydia in women age 18-35 in
Cleveland, and is it associated with the use of oral contraceptives?
• Select a sample of 100 women attending an STD clinic in the
city of Cleveland
• Measure the predictor and outcome variables by taking a history
of oral contraceptive use and sending a cervical swab to the lab
for chlamydia culture
• A questionnaire may be used to gather information abut oral
contraceptive history

16. Cross-sectional Study
• Strengths
• Fast and inexpensive
• No loss to follow-up (no follow-up)
• Ideal for studying prevalence
• Convenient for examining potential networks of causal links
• e.g., in analysis, examine age as a predictor of oral contraceptive use, and then
examine oral contraceptive use as a predictor for chlamydia infection
• Weaknesses:
• Difficult to establish a causal relationship from data collected in a cross-sectional
time-frame (Lack of a temporal relationship between predictor variables and
outcome variables - Does not establish sequence of events)
• Not practical for studying rare phenomena

17. Qualitative Study
• Seeks to describe how individuals perceive their own
experiences within a social context
• Emphasizes in-depth, nuanced understanding of
human experience and interactions
• Methods include in-depth interviews, direct
observations, examining documents, focus groups
• Data are often participants’ own words and narrative
summaries of observed behavior

18. Qualitative Study
Example
A researcher wants to understand how provision of
healthcare to undocumented persons affects the people
and institutions involved
• In 3 communities, information is gathered from
undocumented patients, FQHC primary care clinicians,
specialists, and hospital administrators
• Methods: in-depth interviews, key informant interviews,
participant observations, case studies, focus groups

19. Qualitative Study
Strengths
• Data based on the participants’ own categories of meaning
• Useful for studying a limited number of cases in depth or describing complex phenomena
• Provides understanding and description of people’s personal experiences of phenomena
• Can describe in rich detail phenomena as they are embedded in local contexts
• The researcher can study dynamic processes (i.e., document sequential patterns/change)
Weaknesses
• Knowledge produced might not generalize to other people or other settings
• It is difficult to make quantitative predictions
• It might have lower credibility with some administrators and commissioners of programs
• Takes more time to collect and analyze the data when compared to quantitative research
• The results are more easily influenced by the researcher’s personal biases and idiosyncrasies

20. Quantitative/Qualitative
• Quantitative research involves measurement of outcomes
using numerical data under standardized conditions
• May be used along the continuum of research
• Qualitative research is concerned with narrative
information under less structured conditions that often
takes the research context into account
• Descriptive and exploratory research
• Purposes: describing conditions, exploring associations,
formulating theory, generating hypotheses

21. Exploratory Research
• Cohort study
• Case control study

22. Cohort Study
• A group of individuals who do not yet have the outcome
of interest are followed together over time to see who
develops the condition
• Participants are interviewed or observed to determine the
presence or absence of certain exposures, risks, or
characteristics
• May be simply descriptive
• May identify risk by comparing the incidence of specific
outcomes in exposed and not exposed participants

23. Cohort Study
• Example
To determine whether exercise protects against coronary heart
disease (CHD).
1. Assemble the cohort: 16,936 Harvard alumni were
enrolled
2. Measure predictor variables: Administer a questionnaire
about activity and other potential risk factors , collected
data from college records
3. 10 years later, sent a follow-up questionnaire about CHD
and collected data about CHD from death certificates

24. Cohort Study
• Strengths
• Powerful strategy for defining incidence and investigating
potential causes of an outcome before it occurs
• Time sequence strengthens inference that the factor may cause
the outcome
• Weaknesses
• Expensive – many subjects must be studied to observe
outcome of interest
• Potential confounders: eg, cigarette smoking might confound
the association between exercise and CHD

25. Case-Control Study
• Generally retrospective
• Identify groups with or without the condition
• Look backward in time to find differences in predictor
variables that may explain why the cases got the condition
and the controls did not
• Assumption is that differences in exposure histories
should explain why the cases have the condition
• Data collection via direct interview, mailed questionnaire,
chart review

26. Case-Control Study
• Strengths
• Useful for studying rare conditions
• Short duration & relatively inexpensive
• High yield of information from relatively few participants
• Useful for generating hypotheses
• Weaknesses
• Increased susceptibility to bias:
• Separate sampling of cases and controls
• Retrospective measurement of predictor variables
• No way to estimate the excess risk of exposure
• Only one outcome can be studied

27. Case-Control Study
• Example
Purpose: To determine whether there is an association between
the use of aspirin and the development of Reye’s syndrome in
children.
1. Draw the sample of cases – 30 patients who have had Reye’s
syndrome
2. Draw the sample of controls – 60 patients from the much
larger population who have had minor viral illnesses without
Reye’s syndrome
3. Measure the predictor variable: ask patients in both groups
about their use of aspirin

28. Choosing evaluation methods
28

29. Experimental Research
• Provides a basis for comparing 2 or more conditions
• Controls or accounts for the effects of extraneous factors,
providing the highest degree of confidence in the validity
of outcomes
• Enables the researcher to draw meaningful conclusions
about observed differences
• Randomized controlled trials, single subject designs,
sequential clinical trials, evaluation research, quasi-
experimental research, meta-analysis

30. Experimental Research
• Often regarded as the most rigid and scientific of all
research methods
• When used properly it can provide conclusions that
are beyond questions
• Future oriented
• Characterized by its strict adherence to the scientific
process

31. Features of Experimental Research
1. Presence of dependent and independent variable
2. The presence of control and
3. The measurement of effect of an independent
variable, on the dependent variable neglecting the
effects of all other variables

32. • ID and D
• Control
• The Measurement

33. Experimental Research
• True experimental designs
• Quasi-experimental designs

34. Efficacy vs. Effectiveness
• Efficacy: the benefit of an intervention compared to
a control or standard program under controlled,
randomized conditions
• Randomized controlled trial (RCT) design often used
• Effectiveness: the benefit of an intervention under
less controlled ‘real world’ conditions
• Quasi-experimental design often used

35. Experimental Design
• True experimental design: Subjects are randomly assigned
to at least 2 comparison groups
• Purpose is to compare 2 or more groups that are formed
by random assignment
• The groups differ solely on the basis of what occurs
between measurements (ie, intervention)
• Changes from pretest to posttest can be reasonably attributed
to the intervention
• Most basic is the pretest-posttest control group design
(randomized controlled trial, RCT)

36. Experimental Design
Example:
• Researchers conducted an RCT to study the effect of progressive
resistance exercises in depressed elders. They studied 35 volunteers
who had depression.
• Participants were randomly assigned to an exercise group, which met
three times per week for 10 weeks, or a control group which met 2
times per week for an interactive health education program.
• The outcome variables were: level of depression, functional status, and
quality of life, using standardized instruments.
• Pretest and posttest measures were taken for both groups and
differences were compared.

37. Experimental Design
Strengths
• Controls the influence of confounding variables, providing more conclusive answers
• Randomization eliminates bias due to pre-randomization confounding variables
• Blinding the interventions eliminates bias due to unintended interventions
Weaknesses
• Costly in time and money
• Many research questions are not suitable for experimental designs
• Usually reserved for more mature research questions that have already been examined
by descriptive studies
• Experiments tend to restrict the scope and narrow the study question

38. Quasi-Experimental Design
• Quasi-Experimental designs do not use randomized
assignments for comparisons

39. Quasi-Experimental Design
• Example:
• A study was designed to examine the effect of electrical
stimulation on passive range of motion of wrist extension
in 16 patients who suffered a stroke.
• Outcomes: effects of treatment on sensation, range of
motion, & hand strength.
• Patients were given pretest and posttest measurements
before and after a 4-week intervention program.
• Note: No randomization, and no comparison group

40. Quasi-Experimental Design
Strengths
• Q-E designs are a reasonable alternative to RCT
• Useful where pre-selection and randomization of groups is difficult
• Saves time and resources vs. experimental designs
Weaknesses
• Nonequivalent groups may differ in many ways -- in addition to the differences
between treatment conditions, introducing bias
• Non-blinding allows the possibility of unintended interventions; blinding can be used
in some Q-E studies
• Must document participant characteristics extensively
• Potential biases of the sample must be acknowledged when reporting findings
• Causal inferences are weakened by the potential for biases vs. experimental designs

41. EXPERIMENTAL DESIGN
1. Single Treatment Design
1. One Group Pretest-Posttest Design
2. Two Group Pretest-Posttest Design
3. Solomon Four Group Design
4. Posttest Only Control Group Design

42. 1. One group pretest-post-test design
T1 P T2
T1 = Pretest (treatment group)
T2 = Posttest (treatment group)
P = Program or intervention
42
Types of designs

43. One group pretest-post-test
design
• In experimental conditions where a limited number of
subjects are available
• The group is first given a pretest followed by the usual
treatment and then a posttest is administered
• This design is very delicate because the researcher must
see to it that the situations are equivalent before and
during the experimental factor is introduced
• More open to threats to internal validity such as the
Hawthorne Effects (Test wiseness), maturation and
attrition

44. 2. 2 Group Pretest-Posttest Design
T1 P T2
C1 C2
• T1 = Pretest (treatment group)
• T2 = Posttest (treatment group)
• P = Program or intervention
• C1 = Pretest (comparison group)
• C2 = Posttest (comparison group)
44
Types of designs

45. Pre-Test/Post-Test Control Group
Design
• After the experimental period, both groups are again
given the same posttest
• The researcher may now conduct a comparison of
the posttest results or the gain in scores (posttest-
pretest) between the experimental and control group
• This design is threatened by certain factors,
maturation, test wiseness and natural attribution
(natural death or drop outs)

46. 3. Solomon Four Group Design
- Employ four equivalent groups
- The first two groups obey the pretest-posttest control
group design, the third group is given no pretest with
treatment and a posttest
- The last group is given no pretest, no treatment but with
posttest
- The design can eliminates the Hawthorne effect, effects
of maturation and attrition, but has the main
disadvantages of requiring a large number of respondents

47. • To analyze the data in Solomon four-group design,
one uses a two factor analysis of variance
• Factor A is the effect of treatment while the factor B
is the effect of the pretest
• The interaction affect AB would indicate if the
treatment works well with pretesting or without
pretesting

48. 4. Posttest Control Group Only Design
P T2
P = Program or intervention
T2 = Posttest
48
Types of designs

49. How much of the effect is due to the program?
49
Time (X)
Desired
Outcome
(Y)
T
C
Gross
Effect
Pre Post
Net
Effect

50. EXPERIMENTAL DESIGN
2. Doubt or Multiple Treatment Design
1. RCBD – Randomized Complete Block Design
ex. Block treatment design
RCBD treatment – 2 block design
2. CRD - Complete Randomized Design

51. RCBD
• The RCBD assumes that a population of experimental
units can be divided into a number of relatively
homogenous subpopulations or blocks.
• The treatments are then randomly assigned to
experimental units such that each treatment occurs equally
(usually once) in each block (each block contains all
treatments)
• Block usually represent levels of naturally-occurring
differences or sources of variation that are not related to
the treatments, and the characterization of these
differences is not of interest of to the researcher

52. RCBD
1 B A C
2 A B C
3 A C B
4 A C B
BLOCK NORTH END OF THE FIELD High N
SOUTH END OF THE FIELD Low N

53. CRD
• Is the basic single factor design. In this design the
treatments are assigned completely at random so that
each experiment unit has the same chance of
receiving any one treatment.
• But CRD is appropriate to only when the
experimental material is homogeneous. As there is
generally large variation among experimental plots
due to many factors CRD is not preferred in field
experiments.

54. example
TREATMENT
1 2 1 3
2 1 2 3
3 3 1 2
REPLICATION

55. SAMPLING
• Refers to the design for getting the respondents of
the study with minimum cost and such that the
resulting observation will be representative of the
entire population

56. SAMPLING PLANS AND
SAMPLING DESIGNS
• Sample – is the small that being observed
• Population – is the larger group about which your
generalization is made
• Sampling –is the process of obtaining information
from a proper subset of a population
• The values calculated from this sample will not be
too far from the actual values of the population

57. Example: Population
MALE FEMALE TOTAL
High School 200 350 550
College 150 300 450
Total 350 650 1000
Sampling Plan
MALE FEMALE TOTAL
High School 57 100 157
College 143 86 129
Total 100 186 286

58. If we know that the underlying population is
normally distributed, then if we have some
estimate of the variability of the population
such as variance (s2) then the formula for the
sample size is:
N=4s2/e2 where e=error tolerance (0.05 or 0.01) for
a confidence coefficient of a=.05
For a=.01, then the formula becomes N=9s2/e2

59. How to calculate value of the sample
size?
Slovin’s Formula can be used if the population is more
than 500.
n= N/(1+Ne2)
Where N= population size
e=error of tolerance or desired margin of error
n=a sample size