MODULE 4

DEVELOPMENT OF THEORETICAL AND

CONCEDPTUAL FRAMEWORK
 Objectives
1. State/describe and discuss theoretical and conceptual
framework and its functions in research.
2. Formulate theoretical and conceptual framework of the
study.
 Theoretical Framework
Theoretical Framework (TF) is a statement or an
elaboration of the theoretical basis of the
research problem.
Shows how a study is normally anchored based
on theoretical frameworks variables.

Steps:
1) Identify a theory that possibly
explains why certain factors
(independent variables) can
contribute to or possibly “cause” a
problem (dependent variable).
• 2) Explain the theoretical proposition in
relation to the problem under investigation.

3) TF is needed only if the research intends

to show association/relationship between two or more
variables (“problem and probable “cause/s”).

4. Findings of related studies may be used to explain

the possible connection between the dependent and
independent variables.
Conceptual Framework (CF)

 Explains the possible connection/ link

between the independent & dependent
variables based on the theoretical framework.

Translates abstract concepts in the

theoretical framework into concrete
and measurable terms .

 Identifies functions of variables and

the proposed analysis plan of the
study.
 Explains how/why one variable possibly affects
another.

 Shows a schematic diagram showing the assumed

connection between or among the study variables,
(including direction)

Independent Variable Dependent Variable

(Assumed Cause of ( Problem or

Problem) (Assumed Effect)
 Operational Definition of Variables
• An operational definition is a description of a
variable in terms of how it is used in the
study and how it is going to be measured.

• The operational definition serves as basis in

identifying/formulating the research
instrument.
• Examples:
1.Educational attainment - The highest grade/year
completed by respondent in the elementary, high
school or college

2.Level of knowledge about dangers of cancer- No. of

correct answers in a knowledge tests about
smoking and its dangers.

3.Attitudes towards smoking. Whether in favor or not

in favor with smoking.

4.Smoking practices. No. of cigarettes consumed in

one day.
 Module 8
SAMPLING

In conducting a study, the researcher may study every

member of a given population or just a representative
sample of that population. For various reasons, a study of
a sample of the population is often done instead of
complete enumeration. The process of choosing a
representative sample of a population is called sampling.
In this chapter the concept of sampling is discussed and
the different sampling techniques are described and
illustrated.
 Objective
• Identify and discuss the different types and techniques of
data collection.
Selecting the Study Population
The information collected in order to answer the objectives of a
study is obtained from cases or individuals or objects. For example, in the
study of “Attitudes of Farmers and Landowners towards Land Reform,”
the attitudes that will be examined are those of farmers and landowners,
and they will most likely be the source of information. These cases or
individuals are commonly called research subjects or respondents.

Once the type of cases to be studied has been determined, the

researcher must decide next how the cases will be selected. For this
purpose, it is important to distinguish between the total population and
the sample from the population from which data will be collected.

The population is composed of elements, each of which is a

potential case in study. Depending on the objective/s of the study, the
available resources and the available time for the study, a researcher may
decide whether to study all the available elements or cases in a population
or just a sample from the entire population. The usual practice is to select
and study a small sample of the total population. This process is called
sampling.
The Nature of Sampling (Fraenkel and Wallen, 1996)
Sampling is the process of choosing a representative portion of a population or some
elements in a population that will represent the entire population. It is assumed that the
characteristics of the chosen elements, called sample, reflect the characteristics of the entire
population. In contrast total enumeration or census requires the study of all elements in the
population.

In the study of sampling, it is important to distinguish the following concepts: population,

target population, sampling population, sampling frame and sample.

Population. This refers to the total number of elements (e.g. items, objects, areas, or
individuals) to be studied. For example, in the study of “Sexual Attitudes and Practices of Students
in Public High Schools in Province A,” all students in public high school, in the province constitute
the study population.

Population Element. This pertains to an item, an object, an area, or an individual on

which data will be taken. It is considered the unit of study. In the example above, a student in a
public high school is a population element.

Target Population. This is the population for which representative information is desired.

Sampling Population. This is the population from which a sample is actually drawn.

Sampling Frame. The frame is a list of all the elements or sampling units (e.g. items,
persons) in a population. The sample is drawn from the sampling frame.

Sample. This refers to an individual, an element or a group of individuals or elements on which

information is obtained. The sample is drawn from a population to which research results are
generalized.
Why Study a Sample?
1. It is cheaper
2. It is faster
3. It is more accurate
4. It can yield more comprehensive information

Basic Types of Sampling

Non-probability Sampling
- Accidental Sampling
- Purposive Sampling
Probability Sampling
- Simple Random Sampling
- Systematic Sampling with a Random Start
- Stratified Random Sampling
- Cluster Sampling
- Multistage Sampling
Non-Probability Sampling
Non-probability sampling is judgmental sampling. It does not provide every
member of the population an equal chance of being selected as part of the sample. Moreover,
the probability or chance of an element to be chosen as a sample is unknown. One
disadvantage of using this sampling technique is the possibility of bias. A bias is the greater
probability of certain type/class of sample units/elements to be included in the selected sample.
The conclusion derived from a non probability sample is limited to the sample itself.

There are two common ways to choose persons or elements to include in a non-
probability sample, accidental and purposive methods.

Accidental Sampling. In accidental sampling, the investigator selects the sample

units as they become available.

For example, an investigator who wants to interview 25 students about their study
habits may stand at the door of a classroom and interview the first 25 students who enter the
room. If better students tend to enter the classroom earlier than the slower students, the bias
in favor of the brighter students is introduced.

Purposive Sampling. In this type of sampling, the investigator uses a specific

purpose in selecting a sample.

For instance, a researcher wants to know how grandparents feel about their
grandchildren. He may select men and women who are 65 years old and above who have
grandchildren to serve as respondents. If there are younger grandfathers in the population,
they cannot be included in the study.
Probability Sampling Techniques
A. Simple Random Sampling
 Drawing of lots
 Using table of random digits

Type of random sampling

 Restricted type
 Unrestricted type

In most cases, however, random sampling is done without

replacement, because in sampling with replacement, the same
element may be drawn several times.

Illustration
For instance, you want to study the extent of participation in school
activities of 24 fourth year high school students in School A, and you want to
draw a random sample of 10 students. A decision has been made to choose the
sample without replacement. It is also assumed that the class is homogeneous in
regard to a number of characteristics. Sampling may be done by “draw lots” or by
using a table of random number.

Population: 24 students
Sampling Frame: A list of the 24 fourth year high school students
Sampling unit: Student
 Steps in selecting the sample by drawing of lots (without
replacement)

Step one: Make a list of the students and give them a corresponding number
from 01 to 25. Assuming that the names of students are the alphabets, List the names
of students and give them their corresponding numbers

A 1 G 7 M 13 S
19
B 2 H 8 N 14 T
20
C 3 I 9 O 15 U
21
D 4 J 10 P 16 V
22
E 5 K 11 Q 17 W
23
F 6 L 12 R 18 X
24

Step Two: Write a number on a small piece of paper and roll it.. After writing all
25 numbers, place the rolled pieces of paper in a container, shake the container and pick
one piece of paper at a time.

For instance if the numbers picked are 3, 7, 13, 25, 17, 21, 5, 6, 12, 20, then the
sample students are C, G, M, Y, Q, U, E, F, R, and T
 B. Systematic Sampling with a Random Start

Description:

Systematic sampling with a random start is a method of selecting a

sample from a population by taking the kth units from an ordered
population, from the first unit which is selected at random. The K is called
the sampling interval. The sampling interval is derived by dividing the total
population by the desired sample size. To use this technique, an ordered
list of the population elements is required.

Illustration:
For instance, you want to study the extent of participation in school activities of 24
fourth year high school students in School A, and you want to draw a random sample
of 10 students. A decision has been made to choose the sample without replacement. It
is also assumed that the class is homogeneous in regard to a number of characteristics.
Sampling may be done by “draw lots” or by using a table of random number.

Population: 24 students
Sampling Frame: A list of the 24 fourth year high school students
Sampling unit: Student
Steps in drawing the sample patients

Step 1: List the 30 eligible patients, in alphabetical order, and number them from
1 to 30.
Step 2. Determine the sampling interval (K) by dividing the size of the population
by the number of units desired: K = 30/15 =2.
Step 3. Select a random start by picking at random any number from 01 to 30.
For example you picked 10, then start at number 10.
Step 4: From number 10, the random start (RS), take every second name in
the list. When you reach number 30, go back to number one and continue drawing your
sample units, until you have drawn 15 numbers.

A 1 G 7 M 13 S
19
B 2 H 8 N 14 T
20
C 3 I 9 O 15 U
21
D 4 J 10 P 16 V
22
E 5 K 11 Q 17 W
23
F 6 L 12 R 18 X
24
C. Stratified Random Sampling

Description:

Stratified random sampling is the process of selecting a random

sample from subgroups or strata into which a population has been
subdivided. A population is made up of groups of elements with varied
characteristics, which can possibly affect observations or responses. The
population is be stratified into more or less homogeneous subgroups or
strata, before sampling is done. From each subgroup, a sub-sample is
drawn using either simple random sampling or systematic sampling with a
random start.

Illustration:
In a study on “Attitudes of Farmers Towards Land Reform,” the study population
consists of 75 farmers: 30 rice farmers, 20 sugar cane farmers 10 vegetable growers,
and 15 cutflower growers. If the attitudes towards land reform of the four groups are
expected to differ, a sample from each group must be drawn.

Population: All the 75 farmers

Sampling Frame: List of farmers by type
Sampling unit: Farmer
C. Cluster Sampling
Description:

Cluster sampling is a method of selecting a sample of groups

or clusters of elements. Clusters are usually exclusive sub-
populations, which together comprise a population. Each
cluster consists of heterogeneous elements and each is typical
of the population. For instance in a school where students in
each grade level are assigned to heterogeneous rather than
homogeneous sections, each section is considered a cluster.
Boy scout troops in public schools and YMCA clubs are
examples of clusters.

The number of clusters in the population represents the size of

the population of clusters, while the number of elements in a
cluster is called cluster size. The sample clusters can be
drawn using simple random sampling or systematic sampling
with a random start.
Illustration:
Consider the study “Attitudes Towards Cheating of College Freshmen In a Private
School.” The students may be divided into 10 heterogeneous groups, or classes/clusters,
each with 10 members, or a total of 100 students. If the desired sample size is 50
students, 5 sample clusters need to be drawn at random.

ABCDEF ABCDEF ABCDEF ABCDEF ABCDEF

HI J K HI J K HI J K HI J K HI J K
1 2 3 4 5
ABCDEF ABCDEF ABCDEF ABCDEF ABCDEF
HI J K HI J K HI J K HI J K HI J K

6 7 8 9 10

Population: All the 10 classes/clusters

Frame: List of 10 clusters/groups
Sampling Unit: One group/cluster with 10 members

Steps in selecting the sample clusters:

Step 1: Number the 10 groups consecutively from 01 to 10.
Step 2: Using simple random sampling, draw five numbers. Suppose numbers 2, 6,5,.9, and 1
are chosen
Step 3: Identify the groups represented by the numbers drawn.
Step 4: Study all the students in the five sample classes.
E. Multistage Sampling

Description:

In multistage sampling, the selection of the sample is

accomplished in two or more stages. The population is first
divided into a number of first-stage units from which a sample
is drawn. Then, the population in the sampled first stage units
are divided into second stage units. More stages may be
added, if desired, by dividing the population into a hierarchy of
sampling units corresponding to the different sampling stages.
This process is usually used when the population can be divided
into hierarchies. The sampling process in each hierarchy is
considered one stage.
Illustration
In the study on “Men’s participation in Child Care,” one may wish to select a
sample of 135 married men with 0 to 6 year-old children from a certain province.
Suppose, you wish to include three of the seven towns of the province and three
barangays in each sample town, and select 15 men with young children from each
barangay.
Population: All the men with 0 to 6 year-old children in the province
Sampling Frame: List of men with 0 to 6 year-old children
Sampling Unit: A man with 0 to 6 year-old children
Steps in drawing the sample
Stage One: Draw sample towns in the province. List the names of all the towns in
the province and using simple random sampling draw the three sample towns.
Stage Two: Draw a sample of barangays in the sample towns. Step Two: Secure
a list of all the barangays in each of the three sample towns and using simple
random sampling draw three sample barangays in each of the three sample
towns.
Stage Three: Draw a sample of married men in the sample barangays. List the
names of the men with 0 to 6 year-old children in each of the three sample
barangays in the three sample towns. Using simple random sampling or systematic
sampling with a random start, select the sample men in each of the sample
barangays.
Determination of Sample Size

When studying a sample of a population, it is important

that the sample size should be large enough: 1) to allow a
reliable analysis, 2) to provide for desired levels of accuracy in
estimates, and 3) to enable the researcher to test for the
significance of differences between proportions. If resources are
inadequate to obtain a sufficient sample size, the researcher must
obtain more resources or revise the plans for data analysis
(Fisher, et al, 1991).
There are two important considerations in determining the
sample size of a study: 1) availability of resources and 2) the
requirements of a proposed plan of analysis.
 The sample size can be determined by using an appropriate sampling
formula. To calculate the sample size required for accuracy in estimating
proportions for example, it is important first to answer the following questions:

1. What are the reasonable estimates of key proportions (p) to be measured

in the study? For instance, if you are studying acceptability of land reform
among lowland farmers, you may need to guess what percent of the
population accept land reform. If you cannot guess, you can safely assume it
to be 0.50 (50 percent) to maximize the expected variance.

2. What degree of accuracy do you: want to have or how far can you allow
your sample estimates of key proportions to deviate from the true proportions
in the population as a whole? Do you want to be confident of your results
within 1 percent or 5 percent (usually referred to as the .01 and .05 levels,
respectively)?

3. What confidence level (Z) do you want to use? How confident do you
want to be that your sample estimate is as accurate as you wish? Usually,
the 95 percent confidence level is specified. This is represented by the Z
value of 1.96.

4. What is the size of the population (N) that the sample is supposed to
represent?
 On the basis of your answers to the four questions, you can compute for the sample size
needed to measure a given proportion, with a given degree of accuracy, at a given level of statistical
significance, by using a simple formula given below. This formula is recommended if the
population is more than 10,000.
Z2pq
n = --------------
d2
Where:
n = the desired sample size
Z = the standard normal deviate, usually set at 1.96, which
corresponds to the 95 percent level of confidence
p = the proportion in the target population estimated to have a
particular characteristic. If there is no reasonable estimate,
then use 50 percent (.50)
q = 1.0 – p
d = degree of accuracy desired, usually set at either .05, 0.2, or .01
For example, if you want to determine the sample size of a study in which the
population is over 10,000, and if the proportion of a target population with a certain characteristic is
.50, and if the level of confidence you want to use is 95 percent or the Z statistic 1.96; and you
desire an accuracy level of .05, then the sample size is computed as:
(1.96)2 (1-.50) (.50)
n = ------------------------------
(.05)2
= 384
In the following formula which is also commonly used in
calculating the sample size. Unlike the first formula, the total
population (N) is considered (Parel, et. al., 1985).
NZ2 (p(1-p))
n =
Nd2+ Z2 (p (1-p)
where :
N = Population
n = the desired sample size
Z = the standard normal deviate, set at 1.96,
corresponding to 95 % level of confidence
p = the proportion in the target population estimated to
have a particular characteristic, 50 percent (.50)
d = degree of accuracy desired, usually set at either .05,
025, or .01
Slovin’s Formula
Shown below are sample sizes (n) required for populations (N) of 50 to 3000 with a desired
reliability (Z) of 0.95, a proportion (p) of 0.50, and a maximum sampling error (d) of 0.025 (1),
0.05 (2), and 0.10 (3), respectively.
N N N n N n
1 2 3 1 2 3 1 2 3

50 48 44 33 1050 634 281 88 2050 899 324 92

100 94 79 50 1100 652 285 88 2100 908 325 92
150 137 108 59 1150 669 288 89 2150 918 326 92
200 178 132 65 1200 686 291 89 2200 927 327 92
250 216 151 69 1250 702 294 89 2250 935 328 92
300 253 168 73 1300 717 297 89 2300 944 329 92
350 287 183 75 1350 732 299 90 2350 952 330 92
400 320 196 77 1400 747 301 90 2400 960 331 92
450 351 207 79 1450 761 304 90 2450 968 332 92
500 381 217 81 1500 774 306 90 2500 976 333 92
550 409 226 82 1550 787 308 90 2550 983 334 93
600 436 234 83 1600 800 310 91 2600 991 335 93
650 462 241 84 1650 812 312 91 2650 998 336 93
700 487 248 84 1700 824 313 91 2700 1005 336 93
750 511 254 85 1750 836 315 91 2750 1012 337 93
800 533 260 86 1800 847 317 91 2800 1018 338 93
850 555 265 86 1850 858 318 91 2850 1025 339 93
900 576 269 87 1900 869 320 91 2900 1031 339 93
950 596 274 87 1950 879 321 92 2950 1038 340 93
1000 615 278 88 2000 889 322 92 3000 1044 341 93
Slovin’s Formula. Slovin's Formula is calculated as the sample size (n), given
the population size (N) and the desired margin of error (e). It is computed as n
= N / (1+Ne2). If a sample is taken from a population, a formula must be used
to take into account confidence levels and margins of error. (Saunders, Lewis,
& Thornhill, 2012)

n=N/(1+Ne²)
Solution
n= sample size
N= CHM students faculty and staff (581)
e= margin of error (0.05)

n=581/(1+581 (0.05)²)
=581/(1+581 (0.0025))
=581/(1+0.14525)
=581/1.14525
n=507.31
 EVALUATION
A. Key Terms to Remember

Sampling Random Sampling

Population Random Start
Sampling Frame Sampling Error
Sampling Interval Confidence Level
Sampling Unit Sample Size

B. Questions for Discussion

1. What is Sampling?
2. Differentiate between a sample and a population?
3. Why should a researcher study a sample rather than the total population?
4. Differentiate between probability sampling from non-probability sampling.
5. Differentiate the different probability sampling techniques and the non
probability techniques
6. What are the advantages and disadvantages of using random sampling?
C. Exercises

1. Determine the size of the sample to be taken from a

population of 500 using the second formula presented
in the text with a desired reliability of 0.95, a proportion
estimate of 0.50, and a maximum sampling error of
0.05. Show your computation.

2. Using the data set in the table in page 110, draw a

sample of 25 students using the following sampling
techniques:
a) Simple random sampling with the use of a table of
random numbers
b) Systematic sampling with a random start
c) Multistage, stratified random sampling in two
stages: First stage by school and second stage, by
sex.
CHAPTER 6

CHOOSING AN APPROPRIATE
STUDY DESIGN

The study design is the plan adopted by the

researcher in the conduct of a study. It is
important that the researcher selects the
appropriate study design to minimize errors
and avoid reaching wrong conclusions. this
chapter defines, describes and illustrates
the most commonly used experimental and
non-experimental research designs.
Objective
Identify,discuss and
formulate research
design appropriate to
the research study.
 Research Frameworks

A problem exists because of certain reasons.

Even if the cause or causes of a problem can
not be pinpointed, its existence may be
discussed by examining certain patterns that
relate to the problem situation. In explaining
the existence of a problem, a researcher may
base the explanation on a theory. The
connection between a theory and the problem
is explained in the theoretical framework.
The theoretical framework may be further
explained and illustrated in concrete terms
using a conceptual framework.
What Is a Research Design?
A research design is the “blue print” of the study. It guides the collection,
measurement and analysis of data (Cooper and Schindler, 2001). It is a plan or course
of action which the research follows in order to answer the research question/s or
solve the research problem (Sanchez, et. al., 1996). The design becomes the basis
for determining what data will be collected, and how they will be analysed and
interpreted.

A good research requires a good design. The use of an appropriate design

minimizes the occurrence of error in the conduct of the study and in the conclusions
drawn from the study.

Before the research is implemented, the researcher must already be able to

determine the research design she intends to use. Will he/she use an experimental
design or a non-experimental design.

A wrong choice of a design puts to risk the validity and the reliability of the
study. When this happens, it is quite difficult to find the real answer to a research
question, because there could be some rival hypotheses that can explain the
occurrence of a problem. The selection of an appropriate study design can minimize
possible errors by maximizing reliability and validity of the data
Reliability
Reliability refers to the consistency, stability and dependability of the data.
A reliable measuring device is one which, if used for the second time, will yield the
same results as it did the first time. If the results are substantially different, the
measurement is unreliable.

Validity
Validity refers to the extent to which a measurement does what it is
supposed to do, which is to measure what it intends to measure. Valid data are not
only reliable, but also true and sound. A researcher must select a research design
that will yield a true and accurate information and avoid factors that can
invalidate study results.

Validity Threats

There are many threats to validity. The most common of them are history, selection,
testing, instrumentation, maturation, and mortality.

History. Sometimes there are events in the life of a research project, but which are
not part of the project, that can increase or decrease the expected project outcomes.
These events are not expected, they just happen and they produce effects that can
invalidate study results.
 For example, in a study of the “Effect of Anti-Smoking Campaign
on Cigarette Consumption Among Young Adults in City A,” an intensive
information campaign against smoking was launched in order to
discourage smoking among young adults. Anti-smoking messages were
disseminated on radio, television, and newspapers daily for one month. In
the course of the campaign, a cigarette company also launched a product
promotion, offering a free trip to Europe for the customer and dealer who
could collect and submit the most number of empty packs of the cigarette
brand being promoted. A month after the launching of the anti-smoking
campaign, an evaluation was conducted, and the results showed an
increase in cigarette consumption in the study area. The researcher might
conclude that the campaign was a failure. The conclusion here would be
invalid because of the high possibility that the cigarette promotion (the
historical event) may have contributed to the increase in cigarette
consumption.
Selection. In an experimental study, a threat to validity occurs
when the elements or subjects selected for the experimental group is very
different from those selected for the control group. For instance, if at the
beginning the experiment, the experimental group already has an
advantage over the control group in terms of the focus variables of the
study, this difference will definitely affect the results of the study.
 For example, in an experiment conducted to determine if using games and
puzzles as instructional aids can improve performance of college freshmen in Basic
Math, the teacher used games and puzzles in the experimental group, but did not use
them in the control group. After the experiment, it was found that the experimental
group got significantly better grades in the subject than the control group. It was
discovered, however, that most of the students in the experimental group had very good
grades in high school math, while most of those in the control group had average grades
only. Attributing the better performance of the experimental group to the use of games
and puzzles can be questioned. To avoid this validity threat, the experimental and the
control group should have similar characteristics at the beginning.

Testing. Whenever a pretest is given, it may make the examinees “test

wise,” and this can therefore affect the posttest results. Research subjects who have
been given a pretest may remember some of the test items/questions for which they may
search answers and get these correct when they take the posttest. Better performance in
the posttest might be due to the effect of the pretest and not necessarily to the
intervention or treatment.

Instrumentation. When a research instrument, such as a

questionnaire or a measuring device, like a weighing scale or a thermometer is
changed during the study period or between the pretest and the posttest, the
change could result in an effect that is independent of the intervention and yet,
may be attributed to it.
 For example, in a survey study, an instrumentation effect may be caused by an
interviewer who after conducting the pretest interview becomes more experienced in
interviewing. The interviewer’s experience will enable him/her to generate better and/or more
complete information during the post test than what was collected during the pretest.

In a biomedical study, the use of an unreliable device, like a scale that badly needs
calibration, a contaminated syringe, or a very old litmus paper may also threaten the validity of
test results.

Maturation. People and things change over time. In other words they become more
mature, and this change can threaten the validity of conclusions. Research subjects can get
tired, hungry, or bored during the duration of the project. If the effect of a project is measured
with a test, their tiredness or boredom can result in scores lower than their “true” scores..

On the other hand, the subjects may become more experienced, more
knowledgeable as they grow older and as a result they may get higher scores than they did in
the pretest. In this regard the change can not be attributed to the intervention.

Mortality. In studies that take a long time to finish, say, one year or more, like
cohort (group) studies, where the subjects (the same people) are followed up over time, some
cases may drop out, thus resulting in a loss of cases. Some cases may have transferred
residence and are difficult to locate during the follow-up interview. Cases which cannot be
contacted cannot be followed-up. This loss, called mortality, may distort findings and
conclusions, if substantial and if it has introduced a bias to the sample.

The loss could result in a big difference between the pretest and the posttest results.
This change may be wrongly attributed to the intervention, thus, threaten the validity of the
conclusions.
 Commonly Used Research Designs
(Campbell and Stanley, 1968, Parel, et. al.,
1985, Fisher, et. al., 1991)

The choice of a research design depends on the

objectives of the study. There are many types of research
designs that can be used in basic and experimental
research. Described here are some of the most frequently
used designs. They are classified into: non- or pre-
experimental designs, true experimental designs, and
quasi-experimental designs.
Non/Pre-Experimental Designs

Non-experimental designs are appropriate for

collecting descriptive information about a population or
subjects of a study. They are appropriate for descriptive
studies, like profile studies, exploratory studies, and for
doing small case studies. They are also ideal for
diagnostic studies or situation analysis. They are not
recommended for evaluation studies intended to
determine the effect or impact of a certain intervention or
treatment. Three non-experimental designs are described
below. They are the posttest only or after only design, the
pretest-posttest design, and the static group comparison.
 Posttest Only Design or After Only Survey
Time
X (Observation / Testing / Survey O

The design is also called as one shot survey because the

data are collected only once (O). This design is used when the study
objective is to describe a situation/condition of a study population as it
exists or to determine/describe the characteristics of a population/
respondents. There is no baseline data.

This design is cheap and easy to conduct, but results cannot be

conclusive(certain) in terms of causality or effect of an intervention. It
is not, however, recommended for evaluation studies that intend to
measure the effect of a program intervention, like training.
 Pretest-Posttest Design or Before-After Survey
Observation / Survey 1 Time Observation / Survey 2
(Before X) O (After X) O
O1 X(Intervention) O2

This design is used when the study wants to know the change in
characteristics (e.g. knowledge, attitude, practices) of the study population
(students, nurses, managers, clients, etc) in a given area. A survey,
observation, or testing is conducted before an intervention is introduced (O1).
After a period of time the survey, observation or testing is repeated (O2) and
the results of the pretest (before) and the posttest (after) are compared to
determine change/s.

For example, if a researcher wants to know if an information campaign

against drug/substance abuse in a certain city has reduced drug use in the
area after the campaign, a survey before and after the campaign can be
conducted. No "control" area (area where no campaign is conducted),
however, is surveyed. With the absence of a control area, this design cannot
be considered an experimental design. Any reduction in drug use overtime,
cannot be solely/conclusively attributed to the intervention (campaign).
Static Group Comparison
Time
Expt’l Group
X O1

Control Group
O2
In the static group design, there are two groups involved, an
experimental group and a control group. The experimental group
receives or is exposed to the intervention/treatment (X). This is
followed by a measurement (O1 ), the result of which is compared to
the result of the measurement/observation from a control group (O2)
that did not receive the intervention. The random process, however,
was not used in the assignment of subjects to the experimental and
control groups (indicated by a broken line). The problem with this
design is the validity threat of selection and mortality. It is possible that
the two groups differ greatly on the basis of the main variables of the
study (selection) or some subjects in the experimental group may drop
out and be lost to follow-up or second observation/testing (mortality).
True Experimental Designs

In true experimental designs, subjects are

randomly to the experimental groups and the control
group to achieve pre-intervention/treatment equality of the
two groups. With well-defined and properly selected
experimental and control groups, validity threats are
avoided. Before a researcher decides on an alternative
design, the feasibility of using true experimental designs
must first be considered. The two most frequently used
true experimental designs are the pretest-posttest control
group design and the the posttest control group design.
Pretest-posttest Control Group Design
Expt’l Group O1 X O3
RA
Control Group O2 O4
Pretest Posttest
In the pretest-posttest control group design, the experimental group is
exposed to or covered by an intervention or treatment (X), for example, training or
a new strategy, while the control group is left alone or given another kind of
treatment. Before the intervention/treatment is introduced to the experimental
group, a survey/observation/testing is conducted for both experimental group (O1)
and control group (O2) using the same device/ instrument. The pre-intervention
survey/observation/test serves as pretest and the data collected serve as
baseline data. After the introduction of the intervention in the experimental group or
area, an evaluation survey/observation/testing is conducted in both experimental
group/area (O3) and the control group/area (O4), using the same instrument used in
both during the pretest. The results serve as the posttest/endline data.

The baseline (pretest) and endline (posttest) data are compared. If the change in
the "impact/effect indicator/s” or dependent variable/s is significantly better in the
experimental area/group than the change in the control area/group, then the
intervention is considered effective. If not, then the intervention is said to have
had no effect.
Posttest Only Control Group Design
Expt’l Group X O1
RA
Control Group O2
Posttest
The Posttest Only Control Group design is also used to determine the effects of an
intervention or treatment introduced to a group of subjects (people/objects). As in the
pretest-posttest control group design, at least two groups or areas (e.g. women groups,
communities, provinces) with virtually same characteristics are chosen and randomly
assigned (RA) to the control and experimental group.
The experimental group or area is exposed to or covered by an intervention/treatment,
while the control group is left alone. No pretest/pre-intervention study is conducted. The
experimental and the control groups are assumed to have similar characteristics at the start of
the study. After the introduction of an intervention in the experimental group or area, an
evaluation survey/observation/testing is conducted in both experimental and the control
groups or areas, using the same “fair” instrument.
The data gathered from the experimental and control groups are compared. If
the experimental group or area shows significantly better results than the control
area/group with respect to the "impact/effect indicator/s” or dependent variable/s,
the intervention or treatment is considered effective. If not, then, the intervention is
not effective.
Quasi-experimental Designs

In field studies, it is very difficult to meet the

random assignment criterion of a true experimental
design. In this situation, a quasi-experimental design is
recommended. Quasi-experimental designs are nearly
the same as the true experimental designs, except that
the former do not have restrictions of random assignment.
The two most commonly used quasi-experimental
designs are the non-equivalent control group design
and the time series design.
Time
Expt’l Group
O1 X O3

Control Group O2 O4
Pretest Postest
 In field research, it is possible to compare an experimental group with a
similar, but not necessarily equivalent group. The two groups need only to have
"collective similarity," which means that they should have more or less the same
characteristics in terms of aspects which are relevant to the study. For example,
if one wants to determine the impact of an educational campaign on school
attendance of children, the experimental and the control areas should have more
or less the socio-economic characteristics, because these factors also affect
school attendance.

As in the pretest-posttest control group design, the intervention or

treatment is introduced to the experimental group, but withheld from the control
group. Before the introduction of the intervention, a survey/observation/testing is
conducted in both the experimental group (O1) and the control group (O2). After
the introduction of the intervention to the experimental group, another
observation/testing (posttest) is conducted to both groups (O3 and O4,
respectively). The pretests can be used to determine whether the two groups
have truly "collective similarity" at the start of the experiment. The two posttests
(O3 and O4) will also be compared. The intervention is effective if the change in
the impact/effect indicators in the experimental group (O3 minus O1) is
significantly higher/better than the change in the impact/effect indicators in the
control group (O4 minus O3). If not, then the intervention/treatment cannot be
considered effective. This design is a good one for evaluating training programs,
and other community interventions.
Time Series Design
Time

O1 O2 O3 X O4 O5 O6
The time series design is similar to the non-experimental pretest-posttest
design except that, it has repeated observations/measurements before
and after the intervention (X). Before the introduction of the
intervention/treatment, a measurement/observation with respect to the
impact/effect indicators will be conducted several times at a regular
interval, say, every 30 days; (O1, O2, O3), and then after the intervention,
another series of measurement/observation will be conducted (O4, O5,
O6), also at the same time interval as the first. The same measuring
instrument/device should be used at all times.

The result or pattern of the observations or testing in the first

series of measurements will be compared with that in the series of
measurements after the intervention. If the post-intervention result or
pattern is better than that of the pre-intervention series, then the intervention
can be considered effective. However, if the pre-intervention and post
intervention results or patterns are the same, or the post intervention result
is not significantly better than that of the pre-intervention, then the
intervention cannot be considered effective.
 For example, one wants to evaluate the effect of a
feeding program which is intended to improve the nutritional
status of pre-school children in a barangay. Before the
introduction of the feeding program, the children (program
beneficiaries) will be weighed (measured) several times at
regular interval, say, every 30 days; (O1, O2, O3) and then
after the feeding program, another series of weighing
(measurement) will be conducted (O4, , O5 , O6) so at the
same time interval as the first series (every 30 days). In
order for the feeding program to be considered effective in
improving the nutritional status of the children, the children’s
weights should improve after the feeding program. Since
the children are also growing, increase in weight may also
be observed during the series of pre-intervention
measurements, however, it is expected that post-intervention
changes must be significantly better than the pre-intervention
changes. If not, the feeding program could not be considered
as having effectively improved the nutritional status of he
children.
Selecting a Study Design

In selecting a study design it is important to consider ethical

issues and the balancing of technical issues against practical and
administrative issues (Fraenkel and Wallen, 1996)..

1. Ethical Issues. The researcher must make sure that the

use of a particular design does not endanger the respondent’s life,
will not result in the violation of people’s rights and dignity or in a
denial of services that otherwise would be available. The research
should not involve unethical procedures. It is important therefore,
that informed consent is secured from the respondents or subjects
before they are involved in a study.

2. Practical and Administrative Issues. Every research

requires sufficient funds, competent personnel and adequate
facilities, but these may not always be available. Most often funds
are limited, time is inadequate, and qualified personnel are few.
These issues often affect the choice of a good design. Limited
resources often result in the adoption of a less ideal design.
3. Technical Issues. The use of appropriate or ideal design helps
minimize possible errors. It is important therefore, that technical
aspects be given serious consideration. Whenever possible, the
following should be done:

a. Experimental and control groups should be randomly assigned.

b. When random assignment is not possible, try to find a comparison

group that is nearly equivalent to the experimental group.

c. When neither randomly assigned control group nor a similar

comparison group is available, try using time series design that can
provide information on trends before and after a program intervention.

d. If time series cannot be used, try to obtain baseline information that

can be compared against post program information (pretest-posttest).

e. If baseline (pretest) information is unavailable, bear in mind that the

type of analysis that you can use is limited.

f. Always keep in mind the issue of validity. Are your measurements

true? Do they measure what they intend to measure?
Guidelines for a GOOD Research Design (Fisher, et.
al., 1991)

1. A “good” research design is an ethical design.

2. A “good” research design is capable of obtaining the

most reliable and valid data given all possible
constraints.

3. A “good” research design is capable of collecting the

needed data or measuring whatever it is that happens
in the field.

4. A “good” research design helps an investigator avoid

making erroneous conclusions.
 EVALUATION
A. Key Terms to Remember

Research Design Control Group Control Group

Pre-experimental designs Pretest Randomization
True experimental Design Posttest Intervention
Quasi-experimental designs Validity Treatment

B. Questions for Discussion

1. What is meant by reliability? What is validity? What is the difference

between a reliable instrument and a valid instrument?
2. What are the different experimental, non-experimental and quasi-
experimental designs? How do you distinguish one from another?
3. What are the common validity threats in research and how can their effects
be minimized, if not nullified?
4. What are quasi-experimental designs? What are the different kinds of
quasi-experimental designs?
C. Exercises
Study the following problems and identify what research
design would be most appropriate for each.

“The Effect of Gender Sensitivity Training on Men’s

Involvement in the School Activities of their Children”

“Aspirations in Life and School Performance of High School

Students”

“Gender Variations in the Choice of Scents Among Young

Workers”

“The Effect of Food Supplementation on the health status of

Pre-school Children”
 WORKSHOP NO. 3
SELECTION OF APPROPRIATE
RESEARCH DESIGN
1. My research problem is.

2. The general objective of my study is:

3. The specific objectives of my study are:

4. The hypothesis of my study are:

5. The research design that I will use is:

6. The reasons for my choice of this design are:

7. (For experimental studies only). Since my study is

experimental, I will follow the following process in allocating the research
subjects to the experimental group and the control group:
 References
 David,Fely P. 2005. Understanding and Doing
Research: A Handbook for Beginners.
Panorama Printing, Inc.
 pp.94-106

 Te, Danilo, M. et al. 2019. Business Research

with Statistical Applications. Rex Book Store.
 pp.44-49
 Module V
Operational Definition of Variables
Objective:

Identify, define, categorize and explain the different types of

variables use in the research study.
 OPERATIONAL DEFINITION OF
VARIABLES

One of the most important concepts in research is the

concept of “variable.” There are many kinds of
variables and many research studies involve the
examination of relationship between variables.
Variables may be studied one at a time or in relation to
other variables. In this chapter, variables are defined,
classified and differentiated. Examples are also given.
What is a Variable?

A variable is a concept that stands for a

variation within a class of objects or persons
(Fraenkel and Wallen, 1996).

A variable is a characteristic or property that

can take different values or attributes
(Schutt, 1999).

Variables are the basic elements which are

measured in a study. They are observable
and measurable.
Examples of variables:
age location of businessdegree of
sex revenue malnutrition
marital status type of work level of fertilizer
income number of meetings type of crop
size of land
Types of Variables according to Their Use in Analysis
Variables can be classified as: dependent, independent,
intervening, and antecedent variables
1. A dependent variable is the “assumed "effect" of another
variable.
2. An independent variable is the “assumed cause" of a problem. It
is an assumed reason for any "change" or variation in a dependent
variable. An independent variable is sometimes treated as "antecedent"
variable (the variable before). Likewise, an "antecedent" variable may be
treated as an “independent” variable.
Examples:
Research Topic 1: "The Relationship Between Extent of
Exposure to Mass Media and Eating Habits among Young
Adults"
Extent of
Exposure to Eating Habits
Mass media

Independent Variable Dependent Variable

Product/Service
Price/Rate Customer Satisfaction
Distribution
Promotion
Ex. Acceptability, Salability, and Profitability Of Milkfish
Bones Burger With and Without Moringa
Figure 1.1 shows the paradigm of independent and
dependent variables on the acceptability, salability, and
profitability of milkfish bones burger with and without
moringa.

Independent Variables Dependent Variables

Acceptability
Milkfish Bones Burger with
Moringa Salability
Milkfish Bones Burger Without Profitability
Moringa

Figure 1.2 Paradigm of the Acceptability, Salability, and

Profitability of Milkfish Bones Burger With and Without
Moringa.
Examples:
Research Topic 2: “Knowledge on the Dangers of Smoking,
Attitudes towards Life, and Smoking Habits of Young
Professionals in Iloilo City”

Knowledge on
Attitudes
the dangers of Smoking Habits
towards life
smoking

Independent Variable Intervening Variable Dependent Variable

Examples:
Research Topic 3: "Attitudes Towards Land Reform and
Acceptance of the Program among Lowland Farmers of
Northern Luzon

Education Acceptance of
Tenurial Status Attitudes
Land Reform
Size of Land towards
Program
Owned Land Reform

Independent Variable Intervening Variable Dependent Variable

Operational Definition of Variables

Some researchers cannot answer their research questions

because they do not have clear measures of their variables. A
variable must be operationally defined according to how it is
used in the study, so that it can be properly measured.
The operational definition gives a specific meaning to
the variable. The definition clarifies how a variable or a term is
used and measured in the study. A variable must be defined in
terms of events/units of measurement that are observable by the
senses (Fisher, et. al., 1991). These events/units of measurement
serve as the indicator of the variable.
The operational definition of a variable specifies how a
variable or a term is interpreted in the study and also sets the
procedure for measuring variable. An operational definition of a
variable used in one study may differ from that employed in
another studies.
Look at the examples below:
Variables Operational Definition
Age Refers to the length of time a person has
lived since he/she was born. In the study it
refers to the age of a respondent on his/her
last birthday.
Educational attainment This refers to the level of education an
individual has attained. In the study it
refers the highest grade/year completed by
respondent.
Exposure to smoking information Whether or not the respondent has heard or
campaign read about the anti-smoking campaign and
the number of times he/she has heard/read
the message/s.
Knowledge about cancer No.of correct answers a respondents gets in
a 10-item questionnaire on cancer.

Family Income Total monthly earnings of a all members

family from all sources.
Establishing Categories of Variables

In some cases, a number, an amount, or a score may not

be sufficient to represent a variable. To facilitate description
and analysis of data, categories of variables can be
established. Each category should also be operationally
defined. The categories must be mutually exclusive (do not
overlap) and exhaustive (include all possible responses).
This means that a respondent cannot be assigned to more
than one category. The categories

If a variable, like knowledge scores can be grouped and

each group is assigned to a category, such as “high level of
knowledge,” “average level of knowledge,” and “low level
of knowledge, ” each of these level categories should also
be operationally defined.
 If the categories of a variable is not yet clear, each
category should also be operationally defined. Take, for
example, the variable “residence.” If residence is
operationally defined as the “geographical characteristics
of the area where the respondents permanently reside,”
the possible answers may be categorized as “rural” and
“urban.” The categories may be defined as:

Rural - refers to a place of residence which is located

outside the geographical jurisdiction of a city or a town
center.

Urban - refers to a place of residence which is located

within the city proper or within the town proper of a
municipality.
The meanings of “rural” and “urban” may be different in
other studies. The operational definition depends on how
the word is used and measured.
Operational Definition of Key Terms

There may be terms in the study (not

variables) that have meanings different from
their "dictionary meaning" or they take on
different meanings, depending on situations or
events. These terms must also be defined
operationally to avoid misinterpretation. The
definition depends on how the word is used
and measured.
Examples:

Family planning user is any currently married woman aged 15 to 49 years

old or a married man aged 15 or older who has used a method to prevent or
space pregnancy at least once during the last three months.

Coastal Barangay is a village or community which is located near the sea

where fishing is the main activity of the residents.

Merging is the absorption of one or more business firms by another

existing firm which retains its identity and takes over the rights, privileges,
franchises, and properties and assumes all the liabilities or obligations of
the absorbed firm/s (Pudadera, 2002).

Interest rate represents the cost of borrowing money, expressed as a

percent rate, for a given period of time.

Improved Performance an increase in the performance rating of a worker

from the last time performance was evaluated.
How to Make Operational Definitions
Here are some guidelines to follow in defining variables operationally:

1. List your independent, dependent and intervening ( if any) variables.

2.Write an operational definition for each variable.
3.Identify the possible categories of each variable and determine if the
categories can be clearly understood, are mutually exclusive (do not overlap)
and exhaustive. The list of categories is complete so that all respondents can
be categorized.

4. List the key terms which may be interpreted differently by different

people, unless they are operationally defined. Write an operational definition
for each term.
5. When defining a variable or a term be guided by the following questions.
a. Does the definition clearly specify the way the variable will be measured?
b. Are the categories of each variable mutually exclusive?
c. Are the categories exhaustive?
 EVALUATION
A. Key Terms to Remember
Variables Nominal Variable
Independent Variable Ordinal Variable
Dependent Variable Interval and Ratio Variables
Intervening Variable Relationships/Association
Antecedent Variable

B. Questions for Discussion

1. What are variables?

2. What are the different types of variables and how do
they differ from each other? Give at least two examples of
each type.
3. How can you measure a variable? Illustrate using the
problem you have selected to study.