SLOVIN'S Formula in Determining the Sample Size
Population (N) consists of members of a group that a researcher is interested in studying the
members of a group that usually have common or similar characteristics.
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Margin of error is the allowable error margin in research.
A confidence interval of 95% gives a margin of error of 5%;
a 98% give a margin of error of 2%;
a 99% confidence interval gives a 1% margin of error
Sample Size Determination
A sample (n) is a selection of respondents for a research study to represent the total population
(N).
The following are some reasons for the use of samples
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Saves time
Saves money
Allows more particular attention
Less sampling error in a survey
Performed on a sample of items
The sample size can be obtained by the formula
n= N /1+Ne2
Where:
n = sample size
N = total population
e = margin of error
Sampling Procedures
Sampling is a formal process of choosing the correct subgroup called a sample from a population
to participate in a research study.
To create a sample, you may follow any of the following categories of sampling techniques:
 Probability sampling schemes
 Non-probability sampling schemes
Probability Sampling Procedures
A. Simple Random Sampling
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This is most frequently used type of probability sampling technique
This is characterized by the idea that the chance of selection is the same for every
member of the population.
B. Systematic Random Sampling
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Follows specific steps and procedures in doing the random selection of the samples.
It requires a list of the elements every nth element in the list is drawn for inclusion
in the sample.
C. Stratified Random Sampling
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The population is first divided into two or more mutually exclusive categories based
on your variables of interest in the research study.
The population is organized into homogeneous subsets before drawing the samples.
Subpopulation – strata
D. Cluster Sampling
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Used when the target respondents in a research study is spread across a geographical
location.
Method used, the population is divided into groups called clusters which are
heterogeneous in nature and are mutually exclusive
Random sampling technique is used on relevant clusters to be included in the study.
CLASSIFICATIONS
1. single - stage = all members from each of the selected clusters are used in the sampling
process.
2. two-stage = a subset of elements within each selected cluster is randomly selected for
inclusion in the sample.
3. multi - cluster sampling = more than two steps are taken in selecting clusters from
clusters.
Non-Probability Sampling Procedures
A. Convenience Sampling (haphazard or availability sampling)
This is a method of selecting samples that are available and are capable of participating
in a research study on a current issue.
B. Snowball Sampling
A technique where the researcher identifies a key of informant about a research of
interest and then ask that respondent to refer or identify another respondent who can
participate in the study.
C. Purposive Sampling (judgmental or subjective sampling)
Employs a procedure in which samples are chosen for a special purpose. It may involve
members of a limited group of population.
D. Quota sampling
Gathering a representative sample from a group based on certain characteristics of the
population chosen by the researcher. Usually the pop is divided into specific groups.
The main difference between Stratified random sampling and quota sampling can be explained
in a way that in quota sampling, you use non-random selection.
CONSTRUCT AN INSTRUMENT AND ESTABLISH ITS VALIDITY AND RELIABILITY
INSTRUMENTS IN QUANTITATIVE RESEARCH
o Tools used to gather data for a particular research topic.
o Improve the validity and reliability of data.
o Compute, inter-coder or inter-rater agreement
- refers to the level on concurrence between the scores given by two or more
raters.
NUMBER OF ASPECTS IN DESCRIBING INSTRUMENTS.
1. The actual instrument used
2. The purpose of the instrument
3. The developer of the instrument (an institution or other researchers)
4. The number of items or sections in the instrument
5. The response format used (multiple choice, yes or no)
6. The scoring for the responses
7. The reliability and validity of the study
INSTRUMENT VALIDITY
Validity- refers to the degree to which an instrument measures what it is supposed to measure.
TYPES OF VALIDITY:
1. Face validity
2. Content validity
3. Construct validity
4. Concurrent validity
5. Predictive validity
1. Face validity
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when it "appears" to measure the variables being studied.
2. Content validity
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Refers to the degree to which an instrument covers a representative sample (or specific
elements) of the variable to be measured.
3. Construct validity
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It is the degree to which an instrument measures the variables being studied as a whole.
Thus, the instrument is able to detect what should exist theoretically.
4. Criterion validity
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Refers to the degree that n instrument predicts the charateristics of a variable in a
certain way.
a. Concurrent validity - when it is able to predict results similar to those of a test already
validated in the past.
b. Predictive validity - when it produces results similar to those of another instrument that will
be employed in the future.
INSTRUMENT RELIABILITY
Reliability - refers to the consistency of the measures of an instrument. It is an aspect involved
in the accuracy of measurement.
TYPES OF RELIABILITY
1. Test-retest reliability
2. Equivalent form reliability
3. Internal consistency reliability
4. inter-rater reliability
Test-retest is achieved by administering an instrument twice to the same group of participants
and then computing the consistency of scores
Equivalent form – measured by administering two test identical in all aspects excepts the
actual wording of items. (pretest & posttest)
Internal consistency - measure of how well the items in two instrument measure the same
construct.
a. split-half coefficient
b. Cronbach's alpha
c. Kuder coefficient
Inter-rater - which measures the consistency of scores assigned by two or more raters on a
certain set of results.
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Kappa coefficient is one of the most popular statistical tools in measuring inter-rater
reliability.
Designing The Questionnaire
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It consists of a series of questions that respondents provide answers to a research
study.
Will produce a valid and reliable instrument for gathering data.
Five (5) main types of variables:
Step 1 - Background
a. Dependent variables - trying to explain
b. Independent or explanatory variables - that cause, influence or explain a
change in the dependent variable.
c. Control variables - used to test for a possible erroneous relationship
between the identified independent and dependent variables.
d. Continuous variables - defined on a continuous scale.
e. Discrete variables - which can also be counted but must be a whole number.
Step 2 - Questionnaire
Conceptualization
a. Choose the response scale to use.
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Yes/No
Yes/No/Don't know
Likert Scale
b. Generate the items or questions of the questionnaire based on the purpose and objectives of
the research study.
Guidelines in mind:
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The questions should be clear, concise and simple using minimum number of words.
Avoid lengthy and confusing lay-out.
Classify your questions under each statement based on your problem statement
Questions should be consistent within the needs of the study.
Avoid sensitive or highly debatable question.
c. Choose the types of questions in developing the statements.
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Dichotomous question
Open-minded question
Closed question
Rank-order scale questions
Rating scale question.
The sent arxingy is using formulas. The formula below by the National Education Ohe Ched
States can be used to compe for the needed sample size. Each variable has a set value you can
use for the computation.
s = (X ^ 2 * NP(1 - P))/(d ^ 2 * (N - 1) + X ^ 2 * P(1 - P))
where :
s = required sample size
x ^ 2 = table value of chi-square for 1 degree of freedom at the desired confidence level (1.96)^2
N = population size
P = population proportion (0.5)𝑠
d = the degree of accuracy expressed as a proportion