COLLEGE OF TEACHER EDUCATION

BSED II
FILIPINO MAJOR

SHARON M. GARDOCE
(GURO SA FILIPINO)

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Module 4

Chapter 4

LEARNING OUTCOMES

By the end of the session, students should be able to:

 Chose appropriate quantitative research design.

 Describe sampling procedure.
 Construct an instrument and establishes its validity and reliability.
 Describe intervention (if application).
 Plan data collection.
 Plan data analysis using statistics and hypothesis testing (if appropriate).
 Present written research methodology.
 Implement design principles to produce research work.
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PRE-TEST
A. Write your concise learning about the following:
1. Modern Language Association (MLA)
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2. Harvard is a style of referencing
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3. Chicago-Style Citation
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4. Plagiarism
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5. Intellectual Property Code of the Philippines
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CONTENT

Chapter 4: Understanding Data and Ways to Systematically Collect Data

Before reading this chapter, you must already have formulated your problems or objectives and
hypotheses, described your conceptual framework, and defined terms that will be used in your study. Likewise,

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you must have already read quite a number of research articles and books about your topic and synthesized them
in your related literature with sources in accordance with ethical standards.
Your research report talks about the review of related literature and studies which provide you with a
starting point of your study. You are supposed to continue where the other researchers have left. Thus, you must
take careful note of the variables used in their studies, their methodology, their findings and conclusions, and you
must able to spot strong and weak points of their studies.
The purpose of this Chapter is to discuss the different quantitative research designs that are often used
in practice. This Chapter outlines the research design, the sampling plan, the instrument to be used, data
collection and the quantitative analysis to be performed. Guidelines in writing the methodology will also be
presented in this chapter. But, before you proceed to the discussion of this Chapter, the following terms should
be defined for easy understanding.
Research design refers to the overall plan and scheme for conducting the study. Thus, the researcher
may utilize a historical design, descriptive design or an experimental design. These designs are discussed in
details in separate sections of this Chapter.
Sampling is the process of selecting and getting the respondents of the study with the minimum cost
such that resulting observations will be representative of the entire population. The ultimate purpose of all the
sampling designs is to imitate the behavior of the entire population based on a few observations only. By studying
the sample, you may fairly generalize your result back to the population from which they were chosen. There are
established results in Statistics known as the family of the laws of large numbers that will ensure the validity of
the various sampling plans.
Instruments are the data-gathering devices that will be used in the study. It is a testing device for
measuring a given phenomenon, such as a paper and pencil test, questionnaire, interviews, research tools, or
set of guidelines for observation. There are three characteristics of an instrument that we need to consider:
usability, validity and reliability.
Quantitative Analysis is the technique utilized for analyzing the data gathered. Analysis of the data may
be statistical in nature or it may be deterministic. In this book, we have focused on statistical analysis although
there are other methods for analyzing data.
In this Chapter, we will discuss the research designs that are commonly used and are also based on
nature of the research problem or issue being addressed. Some research articles will also be presented as
examples of the different research designs.
Descriptive Research Designs
The purpose of this design is to describe the status of an identified variable such as events, people or
subjects as they exist. Descriptive research usually makes some type of comparison contrast and correlation and
sometimes, in carefully planned and orchestrated descriptive researches, cause-effect relationships may be
established to some extent. Examples of descriptive research designs are the following:
Descriptive Normative Surveys
You may not want to test an activity or materials or may not be interested in the association
among variables. Instead, you want to describe trends in a large population of individuals. In this case,
a survey is a good procedure to use. Survey design are procedures in quantitative research in which
you administer a survey questionnaire to a small group of people (called the sample) to identify trends
in attitudes, opinions, behaviors, or characteristics of a large group of people (called the population).
The descriptive-normative survey approach attempts to establish norms or standards based on
a wide class of survey data. The survey data may be demographic data or they may include also data
on “average perceptions” of a set of respondents. In early days, survey was usually connected with
demographic data.
If you want to make a research on knowing the profile of all the principals of public and private
schools in the Philippines you may include in your instruments variable like age, sex, educational
attainment, IQ, languages, spoken, civil status, average family size and others. In order to make this
presentation clear and more informative, you should present both univariate tables (frequent counts for
single variable) and multivariate tables (frequency for cross-classifications). For example, a table, on sex
distribution of the principals may be interesting, but two-way table representing sex and educational
qualification at the same time will be more informative. By induction, a three-way cross tabulation of
factors will be more informative than either one-way or two-way cross tabulations. Since normally in
normative surveys the number of respondents is so large, one can make generalizations or norms based
on the data.
Correlational Research Studies

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If you intend to estimate the extent to which different variables are related to one another in the
population of interest, make use of correlational studies. The elements of this design include
identification of the variables of interest, the group of subjects or respondents where the variables will
be applied, and the estimation produce to determine the extent of relationships. In this design you will
have 2 or more quantitative variables from the same group of subjects. You will determine if there is a
relationship (or covariation) between the two variables (a similarity between them, not a difference
between their means). Theoretically, any 2 quantitative variables can be correlated (for example;
financial support to family and self-concept) as long as you have scores on these variables from the
same participants. However, it is a waste of time to collect and analyze data when there is little reason
to think these two variables would be related to each other.
In correlation studies, you may make sure of the results of the descriptive-normative surveys.
For example, if it is desired to determine the extent of the relationship between managerial effectiveness
and the variables age, educational attainment, and mental ability, then you can start by using a
correlational technique among the principals of the public and private schools in the Philippines.
In correlational studies when you attempt to determine the whether a relationship exist between
two or more quantifiable variables and to what degree it is important to remember that, if there is a
significant relationship between two variables, it does not follow that one variable causes the other.
“Correlation does not mean causation”. When two variables are correlated you can use the relationship
to predict the value on one variable for a participant if you know that participant’s value on the other
variable. Thus, correlation implies prediction but not causation. The researcher frequently reports the
correlation coefficient and the p value to determine strength of the relationship.
Descriptive Evaluative Studies
The purpose of the descriptive evaluative study is to judge to “goodness of a criterion measure”/
Longitudinal studies establish the changes in that criterion measure over a long period of time. Thus, if
one were to study the changes in the IQ levels of children 9-10 years over a five-year period, the
researcher must see to it that the same group of children is tested for IQ over the five-year period. Cross-
sectional studies are design to evaluate changes over time by comparing at the same point in time,
different people representing different stages in the development. For example, to establish changes in
IQ for children 9-10 years old, one may simultaneously test, children 9-10, 11-12, 13-14, 15-16, 17-18,
years old see changes in criterion measure.
Assessment/Evaluation Studies
Assessment evaluation studies attempt to determine the effectiveness or efficiency of certain
practices or policies when applied to a group of respondents. Assessment studies imply measurement
of certain key indicators without attaching any judgement to them. However, evaluation implies putting
judgement and valuing to the measurements obtained and is therefore at a much deeper level than
assessment. Assessment and evaluation always go together for one cannot make judgment without
basis for such. For example, one can make a study on the Relative Effectiveness of the K to 12 program
say six years from today on the daily basis of such factors as cost, efficiency, and impact on quality.
Assessment and evaluation studies are fairly common in the Philippines and are often used as basis for
legislation and policy formulation.
Descriptive Comparative Studies
Descriptive comparative studies endeavor to establish significant differences between two or
more groups of subjects on the basis of criterion measure. No attempts to control the effects of
extraneous factors are made. For example, it may be desired to compare the managerial effectiveness
of three groups of managers A, B, and C. a study may employ a descriptive design which combines two
or more design mentioned above.
This type of research usually involves group comparisons. The groups in the study make up
the values of the independent variable; for example, gender (male versus female), preschool attendance
versus no preschool attendance, or children with working mother versus children without a working
mother. In comparative research the independent variable is not under the researchers’ control: that is,
the researcher cannot randomly assign the participants to a gender classification (male or female) or
socioeconomic class, but has to take the values of the independent variable as they come. The
dependent variable in a study is the outcome variable.
General Considerations in Descriptive Research
Most educational researchers utilize the descriptive method of research. This is partly justified by the
fact that the types of information generated by descriptive researchers are valuable baseline data for policy-

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formulation and decision-making. Educational processes are constantly changing with the times. So, there is a
need to keep abreast with such changes through constant updating of information.
However, there are certain limitations of this design that a researcher must be aware of:
a) The lack of control variables in descriptive designs make them less reliable in terms of actual
hypothesis testing. Statistical test may yield different results when applied to different samples
of the same population.
b) Unless the design is a normative survey where the entire population is considered, conclusions
drawn from descriptive designs are at best tentative.
Experimental Research Designs
Experimental studies are also known as longitudinal or repeated-measures studies, for obvious reasons.
They are also referred to as interventions, because you do more than just observe the subjects. Experimental
research design uses the scientific method to establish the cause-effect relationship among a group of variables
that make up a study. The true experiment is often thought of as a laboratory study, but this is not always the
case; a laboratory setting has nothing to do with it. A true experiment is any study where an effort is made to
identify and impose control over all other variables except one. An independent variable is manipulated to
determine the effects on the dependent variables. Subjects are randomly assigned to experimental treatments
rather than identified in naturally occurring groups.
If the treatment effect is unlikely to wash out between measurements, a control group has to be used. In
these designs, all subjects are then measured, but only the experimental group receives the treatment. All
subjects are then measured again, and the change in the experimental group is compared with the change in the
control group.
An enormous difference between the descriptive method of research and experimental method of
research is the presence of control in the latter design and the absence of the same in the former. A factor called
treatment is introduced into the research investigation and the researcher attempts to isolate the effects of such
treatment by means of control.
In the Physical Sciences, isolating the effects of the treatment may be achieved through actual physical
control. For example, if it is desired to determine the weight of an atom of carbon, then the measurement can be
taken in vacuum to assure that no contamination of other elements are present in the set-up. In the Social
Sciences, controls are much harder to establish and the effects of the treatment are usually obtained through
implications.
Thus, if a group 1 receives a treatment while group 2 does not, then provided sufficient safeguards are
made to isolate the effect of the treatment, the difference in the end-results of 1 and 2 can be attributed to the
treatment effect. The whole point of all experimental designs is to make sure that the effects of the treatment is
carefully isolated or measures. There are many designs that would allow this kind of control and we will discuss
a few of these designs as follows:

Pre-test/Post-test Control Group Design

The design requires two group of equivalent standing in terms of a criterion measure e.g.
achievement or mental ability. The first group is designated as the control group while the second group
is the experimental group. Both groups are given the same pretest. The control group is not subjected
to a treatment while the experimental group is given the treatment factor. After the experimental period,
both groups are again given the same posttest.
The researcher may now conduct a comparison of the posttest results or gains in scores
(posttest-pretest) between the experimental and control groups. This design is threatened by certain
factors: maturation (or the aging of the subjects from the pretest to the posttest period), test-wiseness
(or memorizing the contents of the pretest to score high score on the posttest) and natural attrition (death
of subjects or drop-outs from the experiment).

Single group Pre-test Post-test Design

In experimental condition where a limited number of subjects are available, the single group
pretest-posttest design may be used. The group is first given a pretest followed by the usual treatment
and then a posttest is administered. A new pretest is then administered to the group followed by the
experimental treatment factor and a final posttest. This design is very delicate because the researcher
must see to it that situations are equivalent before and during the experimental factor is introduced. As
one might suspect, this design is more open to threats to internal validity such as the Hawthrone effect
(or test-wiseness), maturation and attrition.

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Solomon Four Group Design

The Solomon four group design makes use of four equivalent groups. The first two groups
follow the pretest-posttest control group design. The third group is given no pretest with a posttest. The
last group is given no pretest, no treatment but with a posttest. The design eliminates the Hawthorne
effect, effects of maturation and attrition but has the main disadvantage of requiring a large number of
respondents.
In this design the subjects are randomly assigned to two study groups and two control groups.
Pretest measures are used for one of the study groups to the intervention or experiment, posttest
measures are collected on all four group.
Time
Period One Period Two
(Pre) (Post)
Experimental Group One R O1 X O3
Control Group One R O2 O4
Experimental Group Two R X O5
Control Group Two R O6

O = Observation
R = Random Assignment
X = Treatment

Marczyk, G.R. et al. 2005. Essentials of Research Design and Methodology, Hoboken, NJ: John Wiley
& Sons.

Factors Affecting the Experimental Plan

The following are factors that affect the results of experimental designs:
1. History. Specific events which occur between the first and second measurement in addition to the
experimental variable may affect the result of the experiment. Examples of these are: a) The 2008
economic recession because of the budget crisis many schools cut back resources. A treatment
implemented around that period of time may be affected by a lack of supporting infrastructure. b)
Attitude of students (subject of the study) may change due to expected events that happen between
pretest and posttest. c) Researcher collects gross sales data before and after a 5 day 50% off sale.
During the sale a super typhoon occurs and the results of the study may be affected because of the
calamity, not the sale.
2. Maturation. The Process of maturing either biological or psychological that takes place in the
individuals (subjects) during the experiment regardless of event can affect experimental outcomes.
These are simply growing older, more bored or anxious and may be mistaken as a result of the
treatment. An example of this is when subjects are tired after completing the training session and
their responses on the post-test are affected.
3. Testing. Subjects may be more aware of the contents of the posttest given to pretest. In other words,
the pre-test becomes a form of post-test. Examples: Subjects take a pretest and think about some
of the items. On the post-test they change to answers they feel are more acceptable. So, the
experimental group learns from the pretest.
4. Mortality. Subject may drop out the experimental plan either voluntarily or involuntarily. The loss of
subjects from comparison groups could greatly affect the comparison of unique characteristics of
those subjects. Groups to be compared need to be the same before and after the experiment.
Examples: a) An experiment is aimed to change the accounting practices after a period of one year.
Twelve (12) accountants drop out of the experimental group and none drop out of the control group.
Not only is there differential loss in the two groups, but the 12 dropouts may be very different from
those who remained in the experimental group. b) A project using flipped classroom, started with
161 students (subjects) and only 98 of them completed the entire module. Those who stayed in the
project all the way to the end may be more motivated to learn and thus achieve higher performance.
The hidden variable, intention to treat, might skew the result.
5. Interaction effects. The interaction of the experimental variable and extraneous factors such as
setting, time and conditions of the experimental set-up. Combination of these factors may interact

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especially in multiple group comparisons to produce erroneous measurements and interpretation
that the treatment caused the effect.
6. Measuring instruments. Changes in instruments calibration of instruments, observers, or scorers
may cause changes in the measurements Example: Interviews are very careful with their first two
or three interviews but on the 4th, 5th and 6th interview it become tiresome and boring thus,
interviewers are less careful and make errors.
7. Statistical Regression. Because of extreme scores of measurements, groups are chosen. Those
scores or measurements tend to move toward the mean with repeated measurements even without
an experimental variable. Example: Managers who are performing poorly are selected for training.
Their average posttest scores will be higher than their pretest scores because of statistical
regression, even if no training was given.
8. Differential Selection. Different individuals or groups would have different previous knowledge or
ability which would affect the final measurement if not taken into account. Example: A group of
subjects who has viewed as TV program is compared with a group which has not. There is no way
of knowing that the groups would have been equivalent since they were not randomly assigned to
view the TV program.
9. John Henry Effect. John Henry was a worker who outperformed a machine under an experimental
setting because he was aware that his performance was compared with that of a machine. It is an
experimental bias which pertains to the tendency of the subjects in the control group to perceive
themselves at a disadvantage, thus working harder to outperform the experimental group.
As you do some experimental research studies, if possible use designs that are less affected by these
extraneous factors. Another factor which affects the results of experimental designs is the initial incompatibility of
the control and experimental groups which, for example, happens when the groups are intact (Padua, 2000). In
this case, the method of covariance analysis is recommended.

4.3 Historical Research Designs

The purpose of a historical research design is to collect, verify, and synthesize evidence from the past
to establish facts that defend or refute your hypothesis. It uses secondary sources and variety of primary
documentary evidence, such as, logs, diaries, official records, reports, archives, and non-textual information like
maps, pictures, audio and visual recordings. The limitation is that the sources must be both authentic and valid.
Quantitative history deals on studies that focus on small groups of people and others that include huge
populations. Some quantitative studies use original data collected in numeric form, such as tax assessments or
business ledgers; others involve the conversion of non-numeric evidence, such as city directories or church
membership list, into numeric form as a first step of analysis. Some quantitative studies employ rudimentary
mathematical techniques (such as addition, subtraction, multiplication, and division) to analyze numeric data;
others make use based on complex theoretical assumptions. Sometimes, quantitative history is called cliometric
by economic historians. These historians also called for social scientists to make historical research and
consciously examine the temporal nature of the social phenomena they explored.
Classical historical research methodology relies upon textual records, archival research and the
narrative as a form of historical writing. The historian describes and explains particular phenomena and events.
Quantitative history has similar goals but tales as its subject the aggregate historical patterns of multiple events
or phenomena. It creates a different set of issues for analysis. A classic historical analysis, for example, may treat
a presidential election as a single event while a quantitative historian considers a particular presidential; election
as one elements in the universe of all presidential elections are interested in patterns which characterize the
universe or several units within it.
The creation of quantitative data sets requires the historian to carefully compile consistent information
about the phenomenon to be investigated and apply the techniques of statistical data analysis to the data set to
answer the research questions. Thus, to make effective use of quantitative evidence and statistical technique for
historical analysis, practitioners have to integrate the developing skills of the social sciences, including sampling,
statistical data analysis and data archiving into their historical work. That task led to the development of new
training programs in quantitative methods for historians (Anderson, 2007).
Statistical analysis of historical data has ranged from simple descriptive statistics to more elaborate
quantitative analyses and models of events and behavior. Quantitative historians have borrowed heavily from
sociology, political science, demography and economics, and made use of the classic linear regression model
and its variants as techniques for more complex analysis. Statistical packages, such as SPSS, |SAS, STATA and
the like strengthen the analysis of quantitative historical work, as they do for the social sciences. An example of

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this is the development of the field of ecological regression, particularly for analysis of electoral patterns. Political
scientist can supplement analysis of election result with surveys of individual voters. Indeed, the National Election
Survey, conducted since 1948, has itself become historical source of changing electoral behavior. But historians
cannot go back and survey voters from the election of 1860, and thus must make use of the aggregate election
results and the ecological characteristics of the voting units- e.g., precincts, districts or counties – that provided
the vote.
Quantitative historians have to acquire the skills necessary to prepare and present statistical results in
print. Historians use visual images, audio and video in the presentations, not as ‘illustration’ to enhance or
supplement an analysis but as core evidence for analysis. Steckel (2005) proposed an agenda for what he called
‘Big Social Science History’, which would extend the capabilities of quantitative history and translate some of its
methods of work to non-quantitative projects. Thus, collaborative effort technologies digitized historical evidence,
and developed appropriate theoretical approaches to such historical work.
Major Processes of Historical Research
The historical method of research follows the three major processes:
1. Data Collection
The historian collects data from the past through relics, fossils or documents found in the activities
or through personal interviews with key informants. Old newspaper clippings, memoirs, dairies and
the like are rich source of historical data.
2. Analysis of Data
The historian brings together the data collected to the state of knowledge about the past event and
use simple to complex statistical tools for analysis.
3. Report of findings
The historian reports his/her findings by carefully explaining discrepancies noted and the probable
causes of such discrepancies.
Sampling Plans, Design and Techniques
Sampling is the process of getting information from a proper subset of population. The fundamental
purpose of all sampling plans is to describe the population characteristics through the value obtained from a
sample as accurately as possible. It is there evident that if one were to draw conclusions based on a small sample
then the sample must imitate the behavior or characteristics of the original as closely as possible.
A sampling plan is a detailed outline of which measurements will be taken at what times, on which
material, in what manner, and by whom that support that the resulting data will contain a representative sample
of the parameters of interest and allow for all questions, as stated in the research objectives to be answered.
The following are the steps involved in developing a sampling plan:
1. Identify the parameters to be measured, the range of possible values, and the required resolution.
2. Design a sampling scheme that details how and when samples will be taken.
3. Select sample size.
4. Design data storage formats
5. Assign roles and responsibilities
Once the sampling plan has been developed, it can be varied and then passed on to the responsible
parties for implementation.
For a quantitative analysis, the sample’s composition must accurately represent the target population, a
requirement that necessitates a careful sampling plan.
Among the issues to consider are these five questions.
1. From where within the target population should we collect samples?
2. What type of samples should we collect?
3. What is the minimum amount of sample for each analysis?
4. How many samples should we analyze?
5. How can we minimize the overall variance for the analysis?
When you collect any sort of data, especially quantitative data, whether observational, through surveys
or from secondary data, you need to decide which data to collect and from whom. This is called the sample. There
are variety of ways to select your sample. Make sure that it gives you result that will be reliable and credible.
Given a population frame, the first question that a researcher often ask is the question of sample size.
How large a sample must one take in order to be certain that the values calculated from this sample will not be
too far from the actual values of the population parameters? Unfortunately, this question cannot be answered as
stated. We need to know more. For example, if we know that the underlying population is normally distributed

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then if we have some estimate of the variability of the population such as the sample variance S2, then the formula
for the sample size is:
n = 4s2 / e2
where e error tolerance (about .05 or .01) for a confidence coefficient of a = .05. For a= .01, then the
formula becomes:
n = 9 s 2 / e2
In the event of lack of knowledge about the behavior of the population, the Slovin’s Formula may be
applied:
n = N/(1+Ne2)
where N = population size and e = error balance

Example: find the ample size required for a population size N=1000 if an error of e=.05 is tolerated.
Answer: here, n = (1000) / (1+(1000)(.05)(.05)
n = 286
Our sampling plan calls for the behavior of the sample to follow the behavior of the population. Suppose that our
population categorized by sexes behaves as follows:
Sex Frequency Percentage
Male 350 35.00
Female 650 65.00
Total 1000 100.00
then our sample frame must follow this behavior. If e = .05 then our sample size is 286. Thus, 35% of 286 must
be males and 65% must be females. Our sampling table will be as follows:
Sex Frequency Percentage
Male 100 35.00
Female 186 65.00
We will therefore randomly select 100 male respondents and 186 female respondents.
The logic of this plan can be carried forward for two-way, three-way and multifactor tables. For example, if the
following population distribution is observed by level of high school students:
Sex
Male Female Total
Junior High School 200 350 550
Senior High School 150 300 450
Total 350 650 1000

It follows that 200/1000 = 20% of our population are males with junior high school and so on. Our sampling plan
calls for 20% x = 286 = 57, males from this category. The complete sampling plan is shown:
Sex
Male Female Total
Junior High School 57 100 157
Senior High School 43 86 129
Total 100 186 286
We will therefore select 57 males who are high school graduates and 100 females who are high school graduates:
43 males who are college graduates and 96 females who are college graduates.

Sampling Plan for Experimental Research

As we move on to sampling plan for experimental research, we shall now discuss the minimum sample
size required for the following designs:
a. Two-Factor design
Suppose that the experiment calls for the use of two factors, factor A and factor B each taking levels.
To measure the interaction of factor A and B, there must be at least two observations per
combination of the factor levels. Thus the minimum sample size is:
n≥2 x a x b.

b. Three Factor design

If three factors are to be considered, Factor A, Factor B and Factor C with levels a, b, and c
respectively, then to measure the interaction effects of the factor levels. The minimum sample size
is, therefore,

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n≥2 x a x b x c.
c. Multifactor Designs
Let A1, A2 … Ak be K different factors with levels A1, A2 … Ak, respectively. The minimum sample
size required is

n≥2 x A1 x A2 x … Ak.
We assume that the entries for all are obtained through an appropriate random mechanism.
The extent to which generalizations can be made from the results of a research depends much on the
sampling technique used and how appropriate it is. If the research findings are not generalizable to some degree
beyond the sample used on the study, then the research cannot provide new knowledge, cannot advance
education as a science and is largely a waste of time. Sampling should be carefully designed for satisfactory
generalizations.
Sampling Techniques
The basic distinction in modern sampling theory is between probability and the non-probability sampling.
Probability Sampling
Probability sampling refers to a sampling technique in which samples are obtained using some objective
chance mechanism, thus involving randomization. They require the use of a sampling frame (a list/map of all the
sampling units in the population). the probabilities of selection are known. They are generally referred to as a
random sample from a finite population. they allow drawing of valid generalizations about the universe/population
whose sampling error can be ascertained. The definition of a probability sample does not require equal
probabilities of inclusion in the sample for all elements in the population.
It makes possible for the investigators to estimate the extent to which the findings based on their sample
are likely to differ in what they would have found by sProbability sampling is the only approach that makes possible
representative sampling plans. tudying the population. the use of probability sampling enables the investigator to
specify the size of the sample that they will need if they want to have the given degree of certainty that their
sample findings do not differ by more than a specified amount from those that a study of a whole population would
yield.
There are commonly used probability sampling techniques which are the 1) simple random sampling 2)
systematic sampling 3) stratified sampling 4) cluster sampling and 5) multi-stage sampling.
1. Simple random sampling is the basic probability sampling design, in which the sample is
selected by a process that does not only give each element in the population a chance of
being included in the sample but also makes the selection of every possible combination
of the desired number of cases equally likely. The sample is selected in one of two ways:
by means of a table of random numbers or by using the lottery technique.
2. Systematic random sampling is affected by drawing units at regular intervals from a list.
The starting point or the first units to be taken is a random choice. It differs from one simple
random sampling where each member of the population is not chosen independently.
Once the first member has been selected, all the other members of the random sample
are automatically determined. The population list in the systematic sampling must be in
random order.
3. Stratified random-sampling is selecting sub-samples proportionate in size to the significant
characteristics of the total population. Different strata in the population are defined and
each member of the stratum is listed. Simple random sampling is applied to each stratum.
The number of units drawn from each stratum depends on the ratio of the desired sample
in the population (n/N). stratified sampling assures the researcher that his/her sample will
be representative of the population in terms of certain critical factors that have been used
as the basis for stratification. It also assures adequate cases for sub-group analysis.
4. Cluster sampling is a technique in which the unit of sampling is not the individual but the
naturally occurring group of individuals. The technique is used when it is more convenient
to select individuals from a defined population. It is considers a universe divided into N
mutually exclusive sub-group called cluster. It has simpler requirements. A random sample
of n cluster is selected and their elements are completely enumerated. It is administratively
convenient to implement and its main advantage is saving time and money.
5. Multi-stage sampling refers to the procedure as in cluster sampling which moves through
a series of stages from more inclusive to the less inclusive sampling units until arriving at
the population elements that constitute the desired sampling.

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Non probability sampling
This is a technique when there is no way of estimating the probability that each element has of being
included in the sampling and no assurance that every element has a chance of being included. The major forms
of non-probability sampling are accidental, purposive and quota.
1. Accidental sample is one which the investigator simply reaches out and takes the cases
that are at hand, continuing the process until the sample reaches a designated size. This
is one of the most common techniques of sampling. This is also known as “the man on the
street” interviews conducted frequently by television news program to get quick reading of
public opinion. In clinical practice, clients who are available to medical people are usually
their sample. In research, samples are usually those who simply volunteer for the study.
The problem here is lack of evidence that they are the representatives of the population
you are interested in generalizing.
2. Purposive sampling or judgment sampling is used when practical considerations prevent
the use of probability sampling. Since sampling errors and biases cannot be computed for
non-probability samples, purposive or judgmental sampling should be limited to situations
like when the probable errors are not serious and when probability sampling is practically
impossible. Data from judgmental samples at best suggest or indicate conclusions but in
general they cannot used as the basis of statistical testing procedures.
In purposive sampling, you sample with a purpose in mind. Usually you seek with one or
more specific predefined groups. An example of this is when you run people in a mall or
on the street that are carrying a clipboard and who are stopping various people and asking
if they could interview them. Most likely, they are conducting purposive sample that might
be looking for a Filipino female with long hair between ages 17-25 years old. They size up
the people passing by and anyone who looks into the category they stop to ask if they will
participate. One of the first things they do is to verify that the respondent meets the criteria
for being in the sample.
3. Quota sampling is a technique with provision to guarantee the inclusion in the sample of
diverse elements in the population and to make sure that these diverse elements are taken
into account in proportion in which they occur in the population. in quota sampling, you
select people non-randomly according to some fixed quota. There are two types of quota
sampling: proportional and non-proportional.
In proportional quota sampling you want to represent the major characteristics of the
population by sampling a proportional amount of each. For example, if you know the
population has 70% women and 30% men, and that you want a total sample of 100, you
will continue sampling until you get those percentages and then you will stop. So, if you’ve
already got the 70 women for your sample, but not the 30% men, you will continue to
sample men but even if legitimate women respondents come along, you will not sample
them because you already “met your quota”. The problem here is that you have to decide
the specific characteristics on which you will base the quota. Will it be by gender, age,
education race, religion, etc.?
Non-proportional quota sampling is a bit less restrictive. In this technique, you specify
the minimum number of sampled units you want in each category. You will not be
concerned with having numbers that match the proportions in the population. instead, you
simply want to have enough to assure that you will be able to talk about even small groups
in the population. this technique is the non-probabilistic analogue of stratified random
sampling. It is usually used to assure that smaller groups are adequately represented in
your sample.
Instrumentation
An important part of the research study is the instrument in gathering the data because the quality of
research output depends to a large extent on the quality of research instruments used. Instrument is the generic
term that researchers use for a measurement device like survey, test, questionnaire, and many others. To help
distinguish between instrument and instrumentation, consider that the instrument is the device and
instrumentation is the course of action which is the process of developing, testing, and using the device.
Researchers can choose the type of instruments to use based on their research questions or objectives.
There are two broad categories of instruments namely; 1) researcher-completed instruments and 2) subject-
completed instruments. Examples are shown on the following table below:

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Researcher-completed Instruments Subject-completed Instruments
Rating scales Questionnaires
Interview schedules/guides Self-checklists
Tally sheets Attitude scales
Flowcharts Personality inventories
Performance checklists Achievement/aptitude tests
Time-and-motion logs Projective devices
Observation forms Sociometric devices

A critical portion of the research study is the instrument used to gather data. The validity of the findings
and conclusions resulting from the statistical instruments will depend greatly on the characteristics of your
instruments. We will discuss the general criteria of good research instruments which are validity and reliability.
Validity
Validity refers to the extent to which the instrument measures what it intends to measure and performs
as it is designed to perform. It is unusual and nearly impossible that an instrument is 100% valid that is why
validity is generally measured in degrees. As a process, validation involves collecting and analyzing data to
assess the accuracy of an instrument. There are numerous statistical test and measures to assess the validity of
quantitative instruments that generally involves pilot testing. There are three major types of validity. These are
content validity and criterion validity which are presented in the table below while discussion for each type follows.
Table 1. Types of validity
Types of Validity Description
The extent to which a research instrument accurately measures all
Content Validity
aspects of a construct
The extent to which a research instrument (or tool) measures the
Construct Validity
intended construct.
The extent to which a research instrument is related to other
Criterion Validity
instruments that measure the same variables.

Content validity looks at whether the instrument adequately covers all the content that it should with
respect to the variable. In other words, it refers to the appropriateness of the content of an instruments. It answers
the question “Do the measures (questions, observation logs, etc.) accurately assess what you want to know?” or
“Does the instrument cover the entire domain related to the variable, or construct it was designed to measure?”
in an undergraduate nursing course with instruction about public health, an examination with content validity
would cover all content in the course with greater emphasis on the topics that had received greater coverage or
more depth. A subset of content validity is face validity, where experts are asked their opinion about whether an
instrument measures the concept intended.
Construct validity refers to whether you can draw inferences about test scores related to the concept
being studied. For example, if a person has a high score on a survey that measures anxiety does this person truly
have a high degree of anxiety? Another example is a test of knowledge of medications that requires dosage
calculations which instead testing the mathematics knowledge or skills. There are three types of evidence that
can be used to demonstrate a research instrument has construct validity:
1. Homogeneity – this means that the instrument measures one construct.
2. Convergence – this occurs when instrument measures concept similar to that of other instruments.
Although if there are no similar instruments available this will not be possible to do.
3. Theory evidence – this is evident when behavior is similar to theoretical propositions of the construct
measured in the instrument.

An example of this is when an instrument measures anxiety, one would expect to see that participants
who score high on the instrument for anxiety also demonstrate symptoms of anxiety in their day-to-day lives.
The final measure of validity is criterion validity. A criterion is any other instrument that measures the
same variable. Correlations can be conducted to determine the extent to which the different instruments measure
the same variable. Correlations can be conducted to determine the extent to which the different instruments
measure the same variable.
Criterion validity is measured in three ways:
1. Convergent validity – shows that an instrument is highly correlated with instruments measuring
similar variables.

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Example: geriatric suicide correlated significantly and positively with depression, loneliness and
hopelessness.
2. Divergent validity – shows that an instrument is poorly correlated to instruments that measure
different variables.
Example: there should be a low correlation between an instrument that measures motivation and
one that measures self-efficacy.
3. Predictive validity – means that the instrument should have high correlations with future criterions.
Example: a score of high self-efficacy related to performing a task that should predict the likelihood
a participant completing the task.
Reliability
Reliability relates to the extent to which the instruments is consistent. The instrument should be able to
obtain approximately the same response when applied to respondents who are similarly situated. Likewise, when
the instrument is applied at two different points in time, the responses must highly correlate with one another.
Hence, reliability can be measured by correlating the responses of subjects exposed to the instrument at two
different time periods or by correlating the responses of the subjects who are similarly situated.
An example of this is when a participant completing an instrument meant to measure motivation should
have approximately the same responses each time the test is completed. Although it is not possible to give an
exact calculation of reliability can be achieved through different measures.
The three attributes of reliability are outlined in Table 2 below.
Attributes Description
1. Internal consistency or The extent to which all the items on a scale measure one construct
homogeneity
2. Stability or Test-Retest The consistency of results using an instrument with repeated
Correlation testing
3. Equivalent Consistency among responses of multiple users of an instrument,
or among alternate forms of an instrument

1. Internal consistency or homogeneity is when an instrument measures a specific concept. This

concept is through questions or indicators and each question must correlate highly with the total for this
dimension. For example, teaching effectiveness is measured in terms of seven questions. The scores
for each question must correlate highly with the total for teaching effectiveness.
There are three ways to check the internal consistency or homogeneity of the index.
a) Split-half correlation. We could split the index of “exposure to televised news” in half so
that there are two groups of two questions, and see if the two sub-scales are highly
correlated. That is, do people who score high on the first half also score high on the second
half?
b) Average inter-item correlation. We can also determine the internal consistency for each
question on the index. If the index is homogeneous, each question should be highly
correlated with the other three questions.
c) Average item-total correlation. We can correlate each question with the total score of the
TV news exposure index to examine the internal consistency of items. This give us an idea
of the contribution of each item to the reliability of the index.
2. Stability or test-retest correlation. This is an aspect of reliability where many researchers report that a
highly reliable test indicates that the test is stable overtime. Test-retest correlation provides an indication
of stability over time. It is extent to which scores on a test are essentially invariant over time. This
definition clearly focuses on the measurement instrument and the obtained test scores in terms of test-
retest stability. An example of this is when we ask the respondents in our sample the four questions
once in the month of September and again in December. We can examine whether the two waves of
the same measures yield similar results.
3. Equivalent. Equivalent reliability is measured by the correlation of scores between different versions of
the same instrument, or between instruments that measure the same or similar constructs, such that
one instrument can be produced by the other. If we want to know the extent to which different
investigators use the same instrument to measure the same individuals at the same time yield consistent
results. Equivalence may also be estimated by measuring the same concepts with different instruments,
for example, which is known as multiple-forms reliability

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When you gather, considering readability of the instrument. Readability refers to the level of difficulty of
the instrument relative to the intended users. Thus, an instrument in English applied to a set of
respondents with no education will be useless and unreadable.
The student who intends to use an instrument used in an earlier investigation is well advised to review
the contents of the instrument. If possible, you have to conduct a second run of validation to make sure
that the instruments you are using possess the criteria mentioned above.
Quantitative Data Collection Methods
In quantitative research, data collection methods rely on random sampling and structured data
collection instruments that fit various experience into predetermined response categories that produce results
that are easy to summarize, compare, and generalize.
Since quantitative research is concerned with the testing of hypotheses derived from theory and or being
able to estimate the size of a phenomenon of interest participants may be randomly assigned to different
treatments depending on the research questions or objectives. If this is not feasible, the researcher may collect
data on participant and situational characteristics in order to statistically control for their influence on the
dependent variable. If the intent is to generalize from the research participants to a large population, the
researcher will employ probability sampling to select participants.
Sources of Data
Data can be collected from two sources namely; primary sources and secondary sources. Data
collected from primary sources are known as primary data and data collected from primary sources are
called secondary data.
Primary data are also known as raw data which can be collected from the original source in a
controlled or an uncontrolled environment. An example of a controlled environment is an experimental
research where certain variables are being controlled by the researcher. On the other hand, data
collected through observation or questionnaire survey in a natural setting are examples of data obtained
in an uncontrolled environment. Secondary data are data obtained from secondary sources such as
reports, books, journals, documents, magazines, the web and more.
Interviews
The use of interview as a data collection method begins with the assumption that the
participants’ perspectives are meaningful, knowable, can be made explicit, and that their
perspectives affect the success of the project. The following are types of interviews that could
be used in data collection.
a. Structured Interview. In quantitative research like survey research, interviews are more
structures interview, the researcher asks a standard set of questions and nothing more.
The interview follows a specific format with the same line of questioning. The aim of this
approach is to ensure that each interview is presented with exactly the same questions in
the same order. Structured interviews are also known as standardized interviews or a
researcher-administered survey that must be performed by skilled researchers.
b. Face to face interviews. Interviewing face-to-face remains the most frequently used
quantitative research method. Interviews can be conducted in the respondent’s home or
workplace, in halls or even in simply on the street. It has a distinct advantage of enabling
the researcher to establish rapport with potential participants and therefore gain their
cooperation. These interviews yield highest response rates in survey research. They also
allow the researcher to clarify ambiguous answer and when appropriate, seek follow-up
information. Disadvantages include impractical when large samples are involved, time
consuming and expensive.
c. Telephone interviews are less time consuming and less expensive. The researcher has
access to anyone who has a telephone. Telephone interviews are conducted by
experienced telephone interviewers who are skilled at building rapport with the
respondents. The response rate of this interview is not as high as the face-to-face interview
and considerably higher than the mailed questionnaire. The sample may be biased to the
extent that people without phones are part of the population about whom the researcher
wants to draw inferences. Much of the telephone work consists of usage and attitude
surveys, customer satisfaction surveys or exploration of the potential for new products or
services. Most of the interviews use Computer Assisted Telephone Interviewing to ensure
that interviewer asks the right questions to the right people.

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d. Computer-Assisted Personal Interviewing (CAPI) is a form of personal interview but
instead of completing a questionnaire, the interviewer brings along a laptop or hand-held
computer to enter the information directly into the database. This method saves time
involved in processing the data, as well as saving the interviewer from carrying around
hundreds of questionnaires. However, this type of data collection method can be expensive
to set up and requires that interviewers have computer and typing skills.
Questionnaires
The main purpose of a questionnaire is to help extract data from respondents. It serves as a standard
guide for the interviewers who need to ask the questions in exactly the same way. Without this standard, questions
would be ask in a disorganized way at the discretion of the individual. Questionnaires are also an important part
in the data collection methodology. They are the medium in which responses are recorded to facilitate data
analysis.
There are normally five sections in a questionnaire namely; the respondent’s identification data, an
introduction, instruction, classification data and information. 1) The respondent’s identification data include
respondents name, address, date of the interview and name of the interviewer. The questionnaire will also be
numbered for purposes of entering the data into the computer. 2) An introduction is the interviewer’s request for
help. It is normally scripted and lays out the credentials of the market research company, the purpose of the study
and any aspects of confidentiality. 3) Instruction refers to the interview is and the respondent’s directions on how
to move through the questionnaire such as which questions to skip and where to move to if certain answers are
given. 4) Information is the main body of the document and is made up of the many questions and response
codes. 5) Classification data and information establish the important characteristics of the respondents,
particularly related to their demographics which are sometimes at the front of the questionnaire or sometimes at
the end.
a) Paper-pencil-questionnaires can be sent to a large number of people and saves the
researcher time and money. People are ore truthful while responding to the
questionnaires regarding controversial issues in particular due to the fact that their
responses are anonymous. But they also have drawbacks. Majority of the people who
receive questionnaires don’t return them and those who do might not be
representative of the originally selected sample.
b) Web-based questionnaire is a new and inevitably growing methodology using the
internet based research. This would mean receiving an email on which you would click
on an address that would take you to a secure web-site to fill in a questionnaire. This
type of research is often quicker and less detailed. Some disadvantages of this method
include the exclusion of people who do not have a computer or are unable to access
a computer. Also, the validity of such surveys are in question as people might be in a
hurry to complete it and so might not give accurate responses.
c) Self-Administered Questionnaires. Questionnaires are generally distributed
through mail, filled out and administered by the respondents themselves which is
return via mail to the researchers. The questionnaires can also be distributed by
means of magazine and newspaper inserts or they can be left and/or picked up by
company personnel or the researcher. Questionnaires enable the researcher to elicit
detailed information from respondents who may not be accessible. Self-administered
questionnaires can be used for pre-testing of program materials. In this case, the
questionnaire is mailed to the respondent along with the pretest materials.
Questionnaires often make use of checklist and rating scales. These devices help
simplify and quantity people’s behaviors and attitudes. A checklist is a list of behaviors,
characteristics, or other entities that the researchers is looking for. Either the researcher or
survey participant simply checks whether each item on the list is observed, present or true or
vice versa. While a rating scale is more useful when a behavior needs to be evaluated on a
continuum. Rating scales state the criteria and provide three or more responses to describe the
quality or frequency of a behavior, skills, strategies or variables of the study.
There are typical quantitative data gathering strategies that include the following:
a) Experimenting or conducting clinical trials.
b) Observing and recording well-defined events (like counting the number of
patients waiting in emergency at specified times of the day)
c) Obtaining relevant data from management information systems

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d) Acquiring secondary data from valid and reliable sources in the web
e) Administering surveys with closed-ended questions (like face-to-face and
telephone interviews, questionnaires etc.)
Observations
Observation is way of gathering data by watching behavior, events, or nothing physical characteristics
in their natural setting. If respondents are unwilling or unable to provide data through questionnaires or interviews,
observation is a method that requires little from the individuals for whom you need data.
Observations can be overt when everyone knowns they are being observed or convert when no one
knows they are being observed and the observer is concealed. The benefit of covert observation is that people
are more likely to behave naturally if they do not know they are being observed. However, you will typically need
to conduct overt observations because of ethical problems related to concealing your observation. Observations
can also be either direct or indirect. Direct observation is when you watch interaction, processes, or behaviors as
they occur; for example, observing a teacher teaching a lesson from a written curriculum to determine whether
they are delivering it with fidelity. Indirect observations are when you watch the results of interactions, processes,
or behaviors; for example, measuring the amount of food left by students in a school cafeteria to determine
whether a new food is acceptable to them or not.
There are ways of collecting observation data. These methods can be combined to meet your data such
as recording sheets and checklist which are most standardized way of collecting observation data and include
both preset questions and responses. These forms are typically used for collecting data that can be easily
described in advance like topics that might be covered in an HIV prevention lesson. Observation guides list the
interactions, processes, or behaviors to be observed. They provide space to record open-ended narrative data.
Field notes are the least standardized way of collecting observation data and do not include preset questions or
responses. Field notes are open-ended narrative data that can be written or dictated unto a tape recorder. These
observations as quantitative data can be decoded to quantify the variables for statistical analysis.
Tests
Test provide a way to assess subjects’ knowledge and capacity to apply this knowledge to ne situations.
Test take many forms. They may require respondents to choose among alternatives like selecting a correct
answer or an incorrect answer to cluster choices into like groups, to produce short answer, or to write extended
responses. A question may address a single outcome areas depending on the research study.
Test provide information that is measured against a variety of standards. The most popular test has
traditionally been norm-referenced assessment. Norm-referenced test provide information on how the target
performs against a reference group or normative population. Other assessments like criterion-referenced
assessments are constructed to determine whether or not the respondents/subjects have attained mastery of skill
or knowledge area. These tests provide data on whether important skills have been achieved or not. An alternative
on the criterion-referenced approaches the proficiency testing. Like the criterion-referenced test, the proficiency
test provides an assessment against a level of skill attainment, but included standards for performance at varying
levels of proficiency, typically a three- or four-point scale ranging from below basic to advanced performance. A
researcher who wants to gather information on the status of knowledge or the change in status of knowledge over
time can use using tests which could be analyzed descriptively or quantitatively.
Secondary Data
Secondary data is a type of quantitative data that has already been collected by someone else for a
purpose different from yours. These data are collected by researchers, government and private agencies,
institutions or organizations or companies that provide important information for government planning and policy
recommendation and theory generation. There are many sources of data and most people tend to underestimate
the number of sources and the amount of data within each of these sources. These can be classified as paper-
based sources and electronic sources. Paper-based sources are those from books, journals, periodicals,
abstracts, indexes, directories, research reports, conference papers, market reports, annual reports, internal
records of organizations, newspapers and magazines while electronic sources come from CD-ROMs, on-line
databases, internet, videos and broadcasts. Examples of these data are from the Philippine Statistics Office,
Philippines Statistics on Education, Department of Health, Department of budget and Management, Commission
on Audit and other government agencies and institutions. You can also use data from international sources like
World Bank, UNESCO, TIMSS, World Health Organization etc. these data are normally free, use a wide sample
(like census) so could be seen as representative, cover a large time span and easy to analyze and would allow
you to compare various groups, cultures or nationalities. Since secondary data have been collected for different
purpose from yours, you should treat it with care. The basic questions you should ask are:
a) Where do the data come from?

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b) Do they cover the correct geographical location?
c) Are they up-to-date or recent?
d) If you need to combine with other data, do the data have the same, units, time
and so on?
e) If you are to compare with other data are they similar o “as good as”?
The usefulness of existing sources varies depending on whether they are accessible
and accurate. When using instruments, it is advisable to do a quick scan to assess data before
undertaking extensive analysis.
Quantitative Analysis
This part of your research paper discusses the various quantitative tools that may be needed to answer
your research problems or objectives of the study.
Quantitative data analysis is a systematic approach to investigations during which numerical data are
collected and the researcher transforms what are collected or observed into numerical data. It often describes a
situation or event, answering the research questions or objectives if the study. It is often concerned with finding
evidence to either support or contradict an idea or hypothesis you might have. A hypothesis is where a predicted
answer to a research question is proposed. An example of this is you might propose that if you expose students
with varied constructivist strategies in learning mathematics there will be an improvement of their mathematics
performance and attitude towards the subject. You could then go on to explain why a particular answer is expected
- you put forward a theory to support or contradict your findings.
Most often when a researcher is interested in hypothesis testing they will conduct an experiment to
gather their data. So, you could take one group of students, expose them to varied constructivist strategies in
learning mathematics while another group of them learns the subject in a traditional way. You can compare the
mathematics performance and attitude of the two groups and could see which group did better through a variety
of different measures.
Data analysis in quantitative research studies is often seen as discouraging process. Much of this is
associated with apparently complex language and the notion of statistical tests. The researcher should clearly
identify what statistical tests were undertaken, why these tests were used and what were the results. A rule of
thumb is that, studies that are descriptive in design only use descriptive statistics, correlational studies, quasi-
experimental and experimental studies use inferential statistics.
Inferential statistics is subdivided into tests to measure relationships and differences between variables.
Inferential statistical tests are used to identify if a relationship of difference between variables is statistical
significant. Statistical significance helps the researcher to rule out one important threat to validity and the result
could be due to chance rather than to real differences in the population.
To enhance readability, researchers normally present their findings and data analysis section under the
headings of the research questions. This can help the reader determine if the results that are presented clearly
answer the research questions. Tables, charts and graphs may be used to summarize the results and should be
accurate, clearly identified and enhance the presentation of the results. The percentage of the sample who
participated in the study is an important elements in considering the generalizability of the results. At least fifty
percent of the sample is needed to participate if a response bias is to be avoided according to Polit and Beck
(2006).
In doing quantitative research, remember the following in reporting the results of you study:
a) Explain the data you have collected, the statistical treatment and all relevant results in relation to
the research problem that you are investigating.
b) Describe unexpected events that occurred during your data collection. Explain how the actual
analysis differs from the planned analysis. Explain how you handled the missing data and why any
missing data did not undermine the validity of your analysis.
c) Explain the techniques you used to “clean” your data set.
d) Choose a statistical tool and discuss its use and a reference for it. Specify any computer programs
or software used in the 3 study such as SPSS, MINITAB PHStat and so on..
e) Describe well the assumptions for each procedure and the steps you took to ensure that they were
not violated.
f) Provide the descriptive statistics, confidence intervals, and sample sizes for each variable as well
as the value of the test statistics, its direction, the degrees of freedom, and the significance level
(report the actual p value) when you use inferential statistics.
g) Avoid inferring causality, particularly in nonrandomized designs or without further experimentation.

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h) Use tables to provide exact values and use figures to convey global effect. Keep figures small in
size and include graphic representations of confidence intervals whenever possible.
i) Inform the reader what to look for in tables and figures.

Operations Research Tools

1. Linear Programming (LP) – used when a problem calls for a maximization or minimization of a
linear constraints.
2. Non-Linear Programming (NLP) – used when a problem calls for a maximization or minimization
of non-linear functions.
3. Game theory – used when a problem calls for optimal strategies between two or more
competitors.
4. Inventory Control – used when the problem requires you to determine optimum stock levels and
reordering points.
5. Simulation and Monte Carlo methods - used when:
a. The descriptive method of research is present – oriented.
b. The descriptive method of research is not able to explain cause – effect relationship but
is able to provide clues to such relationship.
c. The descriptive method of research describes and interprets what is currently prevailing.
When you use the descriptive method of research, do not attempt to control the factors but allow them
to interact with one another in a purely statistical and random approach. Your role as a research is to interpret
such chance variation or see through the patterns that come out in such chance relationships.
The next pages to follow are research articles that use quantitative research with different methods and
designs.
The Writing Methodology
This section provides the methods and procedures used in your research study. You should provide
detailed information on the research design, participants, equipment, materials, variables, and actions taken by
the participants. The method section should provide enough information to allow other researchers to replicate
your experiment or study.
Participants: Describe the participants in your research study, including who they are how many there
are, and how they are selected. Explain how the samples were gathered, any randomization techniques and how
the samples were prepared.
For example:
We randomly selected 100 children from elementary schools of Cebu City..
Materials: Describe the materials, measures, equipment, or stimuli used in your research study.
This may include testing instruments, technical equipment, books, images, or other materials used in
the course of research.
For example:
Two stories from Sullivan et al.’s (1994) second-order false belief attribution task were used to
assess children’s understanding of second-order beliefs.
Design: Describe the research design used in your research study. Specify the variables as well as the
levels and measurement of these variables. Explain whether your research study uses a within-groups
or between-groups design. Discuss how the measurements were made and what calculations where
performed upon the raw data. Describe the statistical techniques used upon data.
For example:
The experiment used a 3x2 between-subjects design. The independent variables were age and
understanding of second-order beliefs.
Procedure: the detail of the research procedures used in your research study should be
properly explained. Explain what your participants, respondents do, how you collected the data, and the
order in which steps occurred. Observe some ethical standards in gathering your data.
For example:
A researcher interviewed children individually in their school I one session that lasted 20
minutes on average. The researcher explained to each child that he or she would be told two short
stories and that some questions would be asked after each story. All sessions were videotaped so the
data could later be coded.

Tips:

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1. Always write the method section in the past tense. (Use the future tense if it is a research design)
2. Provide enough details that another researcher could replicate your experiment, but focus on
brevity. Avoid unnecessary detail that is not relevant to the outcome of the experiment.
3. Remember to use proper American Psychological Association (APA) format. As you are writing your
method section, keep a style guide published by the American Psychological Association on hand,
such as the Publication Manual of the American Psychological Association.
4. Take a rough draft of your method section with your teacher or research adviser for additional
assistance.
5. Proofread your paper for typos, grammar problems, and spelling errors. Do not just rely on computer
spell checkers. Always read through each section of your paper for agreement with other sections.
If you mention steps and procedures in the method section, these elements should also be present
in the results and discussion sections.

LEARNING ACTIVITY
Direction. Write your answers in Filipino based on the approved research topic.
1. Read and download at least five (5) research articles about the topic you have chosen for your research
output.___________________________________________________________________________________
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2. Identify and describe the research designs used in the chosen research
articles___________________________________________________________________________________
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3. Decide what research design you are going to use in conducting your research
topic_____________________________________________________________________________________
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4. Write the research design of your topic.
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EVALUATION
1. With the download five (5) research articles about the topic you have chosen for your
research output. Read its sampling design and technique.
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2. Describe and compare the sampling designs of each article. Make a matrix of your comparison.

Title of the Sampling Design Sampling Sample Size Remarks/Reaction

Research used in the Study Techniques
Article

1.

2.

3.

4.

5.

3 Decide what research design you are going to use in conducting your research topic.
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4.Write the sampling design and technique of your topic.
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REFERENCES
Judith Green and Nicki Thorogood. Qualitative Methods for Health Research, 2nd Edition. 2009.
Priscilla R. Ulin, Elizabeth T. Robinson, Elizabeth E. Tolly. Qualitative Methods in Public Health. A Good Guide
for Applied Research. 2005.
Ann Bowling. Research methods in Health, 1997. Buckingham.
Diana M. Bailey. Research foe the Health Professional. A Practical Guide. 2nd Edition. 1997. USA.

METHODS OF REASEARCH RES 111

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John Qvertveit. Evaluating Health Interventions. 1998. Buckingham.
Norman K. Denzin and Yvonna S. Lincoln. Strategies of Qualitative Inquiry. 1998. USA.
Pamela Maykut and Richard Morehouse. Beginning Qualitative Research. A Philosophic and Practical Guide.
1995. The Falmer Press, UK.
Yvonne Carter and Cathryn Thomas. Research Methods in Primary Care. 1997. UK.
Janice M. Morse and Peggy Anne Field. Qualitative Research Methods for Health Professionals. 2nd Edition.
1995. USA.

RUBRICK IN WRITING A RESEARCH PAPER

METHODS OF REASEARCH RES 111

 COLLEGE OF TEACHER EDUCATION

METHODS OF REASEARCH RES 111