2. HOW?
 RESEARCH METHODOLOGY
 Research Design
 Research Locale
 Sources of Data
 Sampling
 Sample Profile
 Sampling Process
 Data Collection
 Conditions
 Instruments
 Validity & Reliability
 Analysis of Data
 Proposed Budget
 Project Management

3. DESIGN & PLANNING PHASE
The Research Design
 Refers to a scheme or plan of action for meeting the objectives of
the study. The appropriateness of the research design depends
largely on which method will help the investigator attain research
objectives.
 The purpose of the research design is to provide a plan of action
for answering the research question.
 The major concern within the blueprint or plan is to specify
control mechanisms to be used in the study so that the answer to
the question will be clear and valid.

4. Reliability & Validity
 Reliability refers to the consistency, stability
or dependability of data. A research method
that will give the same results, even if
conducted twice, is reliable; it is unreliable
when, used the second time, the research
yields different from those of the first time.

5. Reliability & Validity
 Validity refers to data that are not just
reliable but also true and accurate. In
another sense, it refers to the extent to
which an instrument is able to actually
assess what it is supposed to measure.

6. Internal Validity
The degree to which changes in the
dependent variable (effect) can be
attributed to the independent variable
(cause)
Concerns the validity of inferences that,
given the existence of an empirical
relationship, it is the independent
variable, rather than other factors, that

7. Threats to Internal Validity
 Bias or Selection Bias occurs when the
study result is attributed to the
experimental treatment when, in fact, the
results may be due to pre-treatment
differences between the subjects in the
experimental and comparison group.

8. Threats to Internal Validity
Example of Selection Bias
 Women with fertility problem were more
likely to be depressed than women who were
mothers, it would be impossible to conclude
that the two groups differed in depression
because of differences in reproductive status;
women in the two groups might have been
different in terms of psychological well-being
from the start.

9. Threats to Internal Validity
 History occurs when the subjects are
exposed to an event. Some events besides
the experimental treatment occurs between
the pre-treatment and post-treatment
measurement of the dependent variable and
this event influences the dependent
variable.

10. Threats to Internal Validity
Example of History
 If the comparison group is different from the
treatment group, then the characteristics of
the members of the comparison group could
lead them to have different intervening
experiences.

11. Threats to Internal Validity
 Maturation occurs when changes take
place within the study subjects as a result of
the passage of treatment and these changes
may affect the study results.

12. Threats to Internal Validity
Example of Maturation
 Such processes include physical growth,
emotional maturity, fatigue, and the like. For
instance, if we wanted to evaluate the effects
of a special sensori-motor development
program for developmentally delayed
children, we would have to consider that
progress does occur in these children even
without special assistance.

13. Threats to Internal Validity
 Testing refers to the effects of taking a
pretest on subjects’ performance on a
posttest.
 It has been documented in several studies,
particularly in those dealing with opinions
and attitudes, that the mere act of collecting
data from people changes them.

14. Threats to Internal Validity
Example of Testing
 We administered to a group of students a
questionnaire about their attitudes toward
assisted suicide. At the end of instruction, we
give them the same attitude measure and
observe whether their attitudes have
changed. The problem is that the first
administration of the questionnaire might
sensitize students resulting in attitude
changes regardless of whether instruction
follows.

15. Threats to Internal Validity
 Instrumentation Change occurs when
there is changes in measuring instruments
or methods of measurement between two
points of data collection.
 Example: If we used one measure of stress at
baseline and a revised measure at follow-up,
any differences might reflect changes in the
measuring tool rather than the effect of an
independent variable.

16. Threats to Internal Validity
 Mortality/Attrition is the drop-out rate
because of the boredom syndrome.
 Example: The most severely ill patients
might drop out of an experimental condition
because it is too demanding, or they might
drop out of the comparison group because
they see no personal advantage to remaining
in the study.

17. External Validity
The validity that inferences about
observed relationships will hold over
variations in persons, setting, time, or
measures of the outcomes
Concerns the generalizability of causal
inferences, and this is a critical concern
for research that aims to yield evidence
for evidence-based nursing practice

18. External Validity Threats
 Hawthorne Effect occurs when the
participants respond in a certain manner
because they are aware that they are
involved in a research study.

19. External Validity Threats
 Experimental Effect wherein the
researcher’s behavior influences the
subjects’ behavior in a way that is not
intended by the researcher.

20. External Validity Threats
 Pretest Effect occurs when the subjects’
responses to the experimental treatment are
influenced by the pretest.

21. Controlling Extraneous or
Confounding Variables
Participant characteristics almost always need to be
controlled for quantitative findings to be
interpretable. This are ways of controlling
confounding subject characteristics to rule out
rival explanations for cause & effect relationships.
1. Removing the variable
2. Matching cases
2. Balancing cases
4. Analysis of Variance (ANOVA)
5. Randomization

22. “The researcher must examine all
possible threats to validity and
eliminate them, recognize their
influence when they are inevitable.”

24. Experimental Design
 In an experiment (or randomized, controlled trial,
RCT), researchers are active agents, not passive
observers.
 The controlled experiment is considered by many to be
the gold standard for yielding reliable evidence about
causes and effects.
 A true experimental or RCT design is characterized by
the following properties:
 Manipulation
 Control
 Randomization

25. Experimental Design
 Manipulation – the experimenter does something to at
least some subjects – that is, there is some type of
intervention.
 Control – the experimenter introduces controls over
the experimental situation, including devising a good
approximation of a counterfactual – usually a control
group that does not receive the intervention.
 Randomization – the experimenter assigns subjects to
a control or experimental condition on a random basis.

26. Experimental Designs
Name of Design Pre-intervention
Data?
Features
Posttest-only
(after only)
No One data collection point after the
intervention; not appropriate for
measuring change
Pretest-posttest
(before & aftrer)
Yes Data collection before and after the
intervention; appropriate for measuring
change; can determine differences
between groups (experimental) and
change within groups (quasi-
experimental)
Factorial Optional Experimental manipulation of more
than one independent variable; permits
a test of main effects for each
manipulated variable and interaction
effects for combinations of manipulated

27. Pre-Experimental Design
 A type of experimental design in which the
researcher has little control over the research
situation
 Types
 One-shot Case Study – a single group or subject is
observed after a treatment to determine the effect of the
treatment (clinical papers or clinical case studies)
 One-group Pretest-Posttest Design – compares one
group of subjects before and after an experimental
treatment.

28. Quasi-Experimental Design
 Quasi-experiments, like true experiments, involve an
intervention.
 However, quasi-experimental designs lack
randomization, the signature of a true experiment
 The signature of a quasi-experimental design then, is
an intervention in the absence of randomization

29. Quasi-Experimental Designs
 Nonequivalent control group design – the most
frequently used quasi-experimental design, which
involves an experimental treatment and two groups of
subjects observed before and after its implementation.
 Example: Gates, Fitzwater, and Succop (2005) evaluated
the effectiveness of a violence-prevention intervention
for nursing assistants working in long-term care. The
intervention was implemented in three nursing homes,
and three other nursing homes served as the
comparison. Data on anxiety, self-efficacy, and violence-
prevention skills were collected before and after the
intervention.

30. Quasi-Experimental Designs
 Time series design – the researchers periodically
observe subjects and administer an experimental
treatment between the observations.
 Example: Edwards and Beck (2002) used a powerful time
series design to assess the effect of animal-assisted
therapy (aquariums) on the nutritional intake of
individuals with Alzheimer’s disease. Weight (one of the
outcomes) was measured on the first of each months for
3 months before the intervention and for 4 months after
it in a sample of residents in specialized units in 3
facilities. The researchers found that, over this 7-month
period, weight declined in the 3 months.

31. Non-Experimental Design
 A. Descriptive Research. The purpose is to
observe, describe and document aspects of a
situation as it naturally occurs.
 Exploratory Design – a preliminary research
project to ascertain some procedures or
possibility for future research.
 Survey Design – collecting data from a group,
usually be questionnaire or interview, to
learn some of the subjects’ characteristics.

32. Non-Experimental Design
 B. Correlational Research
 When researchers study the effect of a
potential cause that they cannot manipulate,
they use designs that examine relationships
between variables – often called correlational
designs. A correlation is an interrelationship
or association between two variables, that is,
a tendency for variation in one variable to be
related to variation in another.

33. Non-Experimental Design
 B. Correlational Research
 Example: In human adults, height and
weight are correlated because there is a
tendency for taller people to weigh more
than shorter people.

34. DESIGN & PLANNING PHASE
The Research Locale
 The setting of the Study
 Types
 Laboratory Studies – designed to be more
highly controlled in relation to both the
environment in which the study is conducted
& the control of extraneous & intervening
variables
 Field Studies

35. Laboratory Studies
 Example: Physiological laboratory experiments,
chemistry & physics experiments, psychological and
microbiological experiments are laboratory
experiment, designed to control the possibility of
extraneous variables influencing the effect of the
independent variable on the dependent variable. In
the laboratory setting, it is possible to control
environmental variables, such as temperature,
humidity, light and sound, as well as physiological
variables such as nutrition and hydration of the
subjects during the experiment in clinical researches.

36. Field Studies
 Simply means they occur somewhere other than in a
controlled laboratory setting. They occur in natural
settings and use a variety of methods such as field
experiments, participant observations in villages or
hospital wards, interviews in home or office,
questionnaire sent to research subjects and anything
at all that does not occur in a controlled laboratory
setting.

37. Timing of Data Collection
 Prospective or Longitudinal Studies – looking at
events that are underway or expected to occur in
the future. This is designed to follow the subjects
for a long period of time.
 Retrospective, Ex Post Facto or Historical Studies –
focusing on events that have occurred in the past.

38. Timing of Data Collection
 Retrospective – for cause-effect study in which the
effect is known.
 Example: Lung Cancer studies
 Ex Post Facto – a phenomenon that occurs in the
present is thought to have a cause that can be found in
the past.
 Example: Alcoholism, Obesity and Diabetes
 Historical – descriptive studies that ask people to
recall events, other people and memories of the past,
or refer to written documents and artifacts to
reconstruct past events.

39. DESIGN & PLANNING PHASE
The Sampling Design
 Sampling – is the process of selecting a portion
of the population to represent the entire
population so that inferences about the
population can be made.
 A sample is a subset of population elements
 A stratum is a mutually exclusive segment of a
population
 An element is the most basic unit about which
information is collected
 In nursing research, the elements are usually
humans

40. Basic Sampling Concepts
 Population – the entire aggregation of cases in which
a researcher is interested
 If you want to study American nurses with doctoral
degrees, the population could be defined as all U.S.
citizens who are RNs and who have acquired a doctoral-
level degree.
 The criteria that specify population characteristics are
referred to as eligibility criteria or inclusion
criteria. Sometimes, a population is defined in terms
of characteristics that people must not possess
(stipulating the exclusion criteria).
 For example, the population may be defined to exclude

41. Study Groups
 Experimental Group
 First group that receives treatment
 Control Group
 Second group without treatment that will serve as the
comparison group

42. To whom do you
wish to generalize
the findings?
The Target Population
To which
population do
you have access?
The Accessible Population
Through what
resource can
you access them?
The Sampling Frame
Who is
participating
in your study?
The Sample

43. Why do sampling?
Reduce cost
Greater speed
Greater scope
Greater accuracy

44. Types of Sampling
 Probability Sampling – the use of random
sampling procedures to select a sample from
elements of a population.
 Non-Probability Sampling – a sampling
process in which a sample is selected from
elements or members of a population
through non-random methods.

45. Methods of Probability Sampling
 Simple Random Sampling – the most basic probability
sampling design wherein there is the establishment of
a sampling frame (the technical name for the list of
elements from which the sample will be chosen).
 Example: Boyington, Jones, & Wilson (2006) explored
the presence of nursing on hospital websites. The
sampling frame was the US News and World Report list
of the top 203 American hospitals in 2003. Fifty
hospitals were selected: all 17 from the magazine’s
“Honor Roll” and 33 randomly selected from the
remaining hospitals via an electronic number generator.

46. Methods of Probability Sampling
 Stratified Random Sampling – wherein the population
is first divided into two or more strata. The aim of this
sampling is to enhance representativeness. This design
subdivides the population into homogeneous subsets
from which an appropriate number of elements are
selected at random
 Example: Stewart & colleagues (2005) studied nursing
practice issues in rural & remote areas of Canada using a
mailed survey. Questionnaires were sent to a stratified
random sample of rural nurses throughout Canada,
using province as the stratifying variable.

47. Methods of Probability Sampling
 Cluster Sampling – there is a successive random
sampling units. Because of the successive stages in
cluster sampling, this approach is often called
multistage sampling. The resulting design can be
described in terms of the number of stages (e.g. three-
stage cluster sampling)
 Example: Thato & colleagues (2003) studied predictors
of condom use among adolescent Thai vocational
students. In the first stage, the researchers randomly
selected 8 private vocational schools in Bangkok, and
then randomly selected students from the schools. A
total of 425 student aged 18-22 were sampled.

48. Methods of Probability Sampling
 Systematic Sampling – involves the selection of every
kth case from a list, such as every 10th person on a
patient list or every 100th person in a directory of ANA
members.
 Application: The desired sample size is established at
some number (n). The size of the population must be
known or estimated (N). By dividing N by n, the
sampling interval width (k) is established. The
sampling interval is the standard distance between
elements chosen for the sample.

49. Methods of Non-Probability
Sampling
 Convenience Sampling – it is sometimes called an
accidental sampling, entails using the most
conveniently available people as study participants.
 A faculty member who distributes questionnaires to
nursing students in a class is using a convenience
sample. The nurse who conducts an observational study
of women delivering twins at the local hospital is also
relying on a convenience sample.

50. Methods of Non-Probability
Sampling
 Snowball Sampling – also called network sampling or
chain sampling is a variant of convenience sampling.
With this approach, early sample members (called
seeds) are asked to refer other people who meet the
eligibility criteria. This sampling method is often used
when the population is people with characteristics
who might otherwise be difficult to identify.
 Snowballing begins with a few eligible study participants
and then continues on the basis of participant referrals.

51. Methods of Non-Probability
Sampling
 Quota Sampling – is one in which the researcher
identifies population strata and determines how many
participants are needed from each stratum. By using
information about population characteristics,
researchers can ensure that diverse segments are
represented in the sample, preferably in the
proportion in which they occur in the population.
 For example: Pieper (2006) used quota sampling in their
study of chronic venous insufficiency (CVI) in HIV-
positive persons and the extent to which neuropathy
increased the risk for CVI. They stratified their sample
on the basis of whether or not the person had a history

52. Methods of Non-Probability
Sampling
 Purposive Sampling – or judgmental sampling, is based
on the belief that researchers’ knowledge about the
population can be used to hand-pick sample members.
Researchers might decide purposely to select subjects
who are judged to be typical of the population or
particularly knowledgeable about the issues under
study.
 For example: Gagnon & Grenier (2004) conducted a
study to identify, validate, and rank-order quality care
indicators relating to empowerment for patients with a
chronic complex illness. One phase of their study
involved gathering quantitative information from a

53. Data Collection Methods
 Interview – a process of asking questions to subjects
verbally in order to collect data.
 Approaches:
 1. Self-report – questioning people directly
 2. Unstructured interviews – typically conversational in
nature
 3. Focused Interviews or semi-structured interview –
used when you want to be sure that a given set of topics
is covered in interviews with respondents (FGD)

54. Data Collection Methods
 Interview – a process of asking questions to subjects
verbally in order to collect data.
 Approaches:
 4. Life histories – narrative self-disclosure about a
person’s life experiences
 5. Critical Incidents Technique – method of gathering
information about people’s behaviors by examining
specific incidents relating to the behavior under
investigation

55. Data Collection Methods
 Questionnaire – a written question-and-answer sheet
which provides data about subject’s attitudes, beliefs,
habits, and socioeconomic background
 Question forms:
 Open-ended questions
 Close-ended questions or fixed alternative

56. Data Collection Methods
 Observation – is a method of collecting descriptive
behavioral data and is extremely useful in health
researches because one can observe behavior as it
occurs. It is systematically planned and recorded.
 Types:
 Participant observer
 Non-participant observer

57. How to Write Questionnaire Items
 1. Define or qualify terms that could easily be
misinterpreted.
 What is the value of your house? X
 What is the present market value of your house?
 How much does your house cost?
 2. Be careful in using descriptive adjectives & adverbs
that have no agreed upon meaning.
 Frequently, occasionally, rarely
 Times per week or times per month

58. How to Write Questionnaire Items
 3. Beware of double negatives. Underline negatives for
clarity.
 Are you opposed to not requiring students to take
shower after gym class?
 Federal aid should not be granted to those states in
which education is not equal regardless of race, creed or
color.
 4. Be careful with inadequate alternatives.
 Married? YES NO

59. How to Write Questionnaire Items
 5. Avoid double-barreled question.
 Do you believe that gifted students should be placed in
separate groups for instructional purposes and assigned
to special schools?
 6. Underline a word if you wish to indicate special
emphasis.
 Should all schools offer a modern foreign language.
 7. When asking for ratings/comparisons, a point of
reference is necessary.
 How would you rate the clinical instructor’s teaching?
Superior _____ Average _____ Below Average _____

60. How to Write Questionnaire Items
 8. Avoid unwanted assumptions.
 Are you satisfied with the salary raise you receive last
year?
 Do you feel that you benefited from the spankings that
you received as a child?
 9. Phrase questions so that they are appropriate for all
respondents.
 What is your monthly teaching salary?
 10. Design questions that will give complete response.
 Do you read the Philippine Star? YES NO

61. How to Write Questionnaire Items
 11. Provide for systematic quantification of responses.
- solutions -
 Ask respondents to rank
 Arrange in order of preference
 Allow specific number of responses
 12. Consider the possibility of classifying the responses
yourself, rather than having the respondent choose
categories.

62. EMPIRICAL & ANALYTIC PHASE
TRIANGULATION
 A compatibility procedure designed to reconcile
the two major methodologies by eclectically using
elements from each of the major methodologies as
these contribute to the solution of the major
problem.
Qualitative Research
(Data: principally verbal)
Quantitative Research
(Data: principally numerical)
Descriptive Studies
Historical Studies
Case Studies
Ethnographic Studies
Survey Studies
Experimental Studies
Quasi-experimental Studies
Statistical Analytical Studies

63. Kinds of Triangulation
 Methodological Triangulation
 The use of two or more methods of data
collection procedures within a single study.
 Data Triangulation
 Attempts to gather observations through the use
of a variety of sampling strategies to ensure that
a theory is tested in more than one way.

64. Kinds of Triangulation
 Theoretical Triangulation
 The use of several frames of reference or
perspectives in the analysis of the same set of
data.
 Investigator Triangulation
 The use of multiple observers, coders,
interviewers, and/or analysis in a particular
study.