knowlegable

1. Introduction to
Research Methods
in Education
Day 3
Chapter III – RESEARCH
METHODOLOGY
 Research Design
 Respondents of the Study
 Research Locale
 Instruments/ Instrumentation
 Data Gathering Procedure
 Statistical Treatment of Data
Research Methods
 Why is it important to understand research
methods for interdisciplinary researchers?
 Types of research
 How do you measure learning
experimentally?
 Within subjects design
 Between subjects design
 What are you measuring?
 Important Statistical terms
 [other methods]
What do you want your data &
results to look like?
 Do you want to show learning, engagement,
the process of the activity?
 Do you want to show that your tool works, or
that it is better than an alternative?
 Do you want to describe, code, run statistics,
present a case study?
 How will you design the study to get the type
of results you want to present?
Two Types of Research
 Quantitative
 Experimental
 Surveys (usually)
 Qualitative
 Biography, phenomenology, grounded theory,
ethnography & case study
 Mixed methods
 You can’t account for context with numbers
 The plural of anecdote is not data
Three Basic Research Design
1. Descriptive Research – events are
recorded, described, interpreted, analyzed
and compared (Castillo, 2002). Its objective
is to describe systematically a situation,
condition or area of interest factually and
accurately.
Include observation, surveys, interviews,
standardized test and case studies.

2. 2. Historical Research
 A research design wherein past events are
studied and related to the present or in the
future time. Its purpose is to reconstruct the
past objectively and accurately.
3. Experimental Research
 A research design wherein the cause and
effect relationship of a treatment on a
variable is determined (Castillo, 2002). This
can be further divided to true experimental
design or quasi-experimental design.
True Experimental Design
 Investigate possible cause and effect
relationships among variables under study.
This is done by exposing one or more
experimental groups to various treatment
conditions and comparing the results with
one or more control groups receiving the
treatment.
The quasi-experimental design
 Tries to approximates the conditions of the
true experimental design in a setting which
does not allow control or manipulation of all
relevant variables.
How do you measure
learning experimentally?
Pre/post test design (within subjects)
 Sequestered problem solving (SPS)
 Preparation for future learning (PFL)
 Free-write/free recall
 Delayed post-test
 Multiple baseline/single case design
How do you measure
learning experimentally?
Control design (between subjects)
 Only some of the participants receive
intervention & compare post-test scores
between control & experimental groups
 Compare ‘experimental group’ with
previous groups who did not receive
intervention
 Randomized control trials (the medical
model)

3. But what are you measuring…
 Is it valid?
 Internal validity (is the effect caused by the IV)
 External validity (would it replication beyond the sample)
 Is it reliable?
 test-retest reliability
 Split-test reliability
 [piloting]
What sort of learning will you measure?
Respondents of the Study
 Explain how and where the subjects will be
taken/selected. If the total population is large,
sampling is used. Sampling technique to be
used should be explained as t its
appropriateness.
Important statistical terms
Population:
a set which includes all
measurements of interest
to the researcher
(The collection of all
responses, measurements, or
counts that are of interest)
Sample:
A subset of the population
Why sampling?
Get information about large populations
 Less costs
 Less field time
 More accuracy i.e. Can Do A Better Job of
Data Collection
 When it’s impossible to study the whole
population

4. Target Population:
The population to be studied/ to which the
investigator wants to generalize his results
Sampling Unit:
smallest unit from which sample can be selected
Sampling frame
List of all the sampling units from which sample is
drawn
Sampling scheme
Method of selecting sampling units from sampling
frame
Types of sampling
Non-probability samples
Probability samples
Non probability samples
 Convenience samples (ease of access)
sample is selected from elements of a population
that are easily accessible
 Snowball sampling (friend of friend….etc.)
 Purposive sampling (judgemental)
 You chose who you think should be in the
study
 Quota sample
Non probability samples
Probability of being chosen is unknown
Cheaper- but unable to generalize
potential for bias
Probability samples
 Random sampling
 Each subject has a known probability of being
selected
 Allows application of statistical sampling
theory to results to:
 Generalise
 Test hypotheses
Conclusions
 Probability samples are the best
 Ensure
 Representativeness
 Precision

5. Methods used in probability
samples
 Simple random sampling
 Systematic sampling
 Stratified sampling
 Multi-stage sampling
 Cluster sampling
Simple random sampling
Table of random numbers
6 8 4 2 5 7 9 5 4 1 2 5 6 3 2 1 4 0
5 8 2 0 3 2 1 5 4 7 8 5 9 6 2 0 2 4
3 6 2 3 3 3 2 5 4 7 8 9 1 2 0 3 2 5
9 8 5 2 6 3 0 1 7 4 2 4 5 0 3 6 8 6
Sampling fraction
Ratio between sample size and population size
Systematic sampling
Systematic sampling
Cluster sampling
Cluster: a group of sampling units close to each
other i.e. crowding together in the same area or
neighborhood

6. Cluster sampling
Section 4
Section 5
Section 3
Section 2Section 1
 Stratified sampling
 Multi-stage sampling
Systematic error (or bias)
Inaccurate response (information bias)
Selection bias
Sampling error (random error)
Errors in sample
Type 1 error
 The probability of finding a difference with our
sample compared to population, and there
really isn’t one….
 Known as the α (or “type 1 error”)
 Usually set at 5% (or 0.05)
Type 2 error
 The probability of not finding a difference
that actually exists between our sample
compared to the population…
 Known as the β (or “type 2 error”)
 Power is (1- β) and is usually 80%
Sample size
Quantitative Qualitative
2D
2σ2Z
n 
2
2
2
2
1
D
)xFσ(σ
n
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
2
2
D
π)π(1Z
n
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2
D
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n 

7. Problem 1
A study is to be performed to determine a
certain parameter in a community. From a
previous study a sd of 46 was obtained.
If a sample error of up to 4 is to be accepted.
How many subjects should be included in this
study at 99% level of confidence?
Answer
881~3.880
24
246x22.58
n 
2D
2σ2Z
n 
Problem 2
 A study is to be done to determine effect of 2
drugs (A and B) on blood glucose level. From
previous studies using those drugs, Sd of
BGL of 8 and 12 g/dl were obtained
respectively.
 A significant level of 95% and a power of
90% is required to detect a mean difference
between the two groups of 3 g/dl. How many
subjects should be include in each group?
Answer
groupeachin
243~6.242
3
)x10.512(8
n 2
22



2
2
2
2
1
D
)xFσ(σ
n


Problem 3
It was desired to estimate proportion of
anaemic children in a certain preparatory
school. In a similar study at another school a
proportion of 30 % was detected.
Compute the minimal sample size required at a
confidence limit of 95% and accepting a
difference of up to 4% of the true population.
Answer
505~21.504
(0.04)
0.3)0.3(1x1.96
n 2
2



2
2
D
π)π(1Z
n



8. Problem 4
In previous studies, percentage of
hypertensives among Diabetics was 70% and
among non diabetics was 40% in a certain
community.
A researcher wants to perform a comparative
study for hypertension among diabetics and
non-diabetics at a confidence limit 95% and
power 80%, What is the minimal sample to
be taken from each group with 4% accepted
difference of true value?
Answer
2.2413
0.04
x7.80.55)-(10.55x2
n 2

2
D
F)P-(1P2
n 
Research Locale
 The place / institution where the study will be
conducted should be well discussed. The
institutions place, its brief history, its mission
and vision, objectives, any background
information about the place under study.
Instrumentation
 There is a need to describe and explicitly
explain the adoption, construction, validation,
administration of research instruments in
gathering data. Instruments include tests,
questionnaires, interview guidelines and / or
schedule, etc.
 In this section, variables covered by the
instrument are enumerated, the scaling to be
used.
For researcher made instruments,
the process of validation should
be narrated. How it as validated
and who helped in the validation
of the process should be narrated
also.
Data Gathering Procedure
 The researcher must narrate, step by step
how the research questionnaires will be
distributed among the respondents. Start
from asking permission to conduct the study
in the selected research locale. Future tenses
of the verbs should be used in narrating the
data gathering procedure in a thesis
proposal.

9. Statistical Treatment of Data
 It is directed by the questions for which the
research is designed.
 The level, distribution and dispersion of data
also suggest the type of statistical test to be
utilized.
Important statistical ideas
 Independent variables
 The thing you manipulate/control for
 Main effects & Interaction effects between IV
 Dependent variables
 The outcome measure
 Floor effects & ceiling effects
 Statistical significance
 Usually <.05 for social science
 Indicates whether the effect is genuine or due to
chance
Important statistical ideas
 Level of measurement
 Nominal
 Ordinal
 Interval (& ranking)
 Population & sample
 Normal distribution
 Descriptive statistics
 Means, standard deviations, standard errors,
 Parametric and non-parametric statistics
Assumptions for parametric statistics
 Level of measurement must be at least
interval
 Sample is drawn from a normally distributed
population
 Homogeneity of Variance
 Variance of two samples is not significantly
different
 Independence of scores
Key things to look for:
 Are the differences between conditions
significant:
 T-tests, ANOVA, Chi Square
 Is there a relationship between variables?
 Correlations (note: you can’t tell causation from this)
 Pay attention to r values (between 1 and -1).
 Which of the IVs cause the DV?
 Regression Analysis (note: need very large sample
size; controversial technique)
Survey design
 In the past 24 hours, did you watch more
than an hour of television programming,
or not? Yes/No
 In the past 24 hours, did you read a daily
newspaper, or not? Yes/No
 On a scale of 1 to 7, please rate
How satisfied were you with what you
learned and the usability of the software?
(1) Agree strongly…….(7) disagree strongly

10. Survey design (things to remember)
 Is there only one question in each item?
 Pilot with a number of people – do they read the
question the way it was intended?
 Are all your scales in the same direction (if not,
reverse them before analysis)
 Do the answers match the questions?
 How will you make sense of the answers
 What sort of analysis can you do on rating, frequency,
open-ended items?
 Are particular answers ‘socially desirable?’
Group Activity
 Prepare Chapter III
For Proposal Defense
Things to prepare and to remember:
 Powerpoint presentation
 Start by an opening prayer
 The group leader should introduce to the
panelist the members of the group, and the
proposed thesis title.
 Present your study and conclude your
statement that you are now ready to accept
clarification, suggestions, recommendation
for your study
Contents of the Slides
 Title
 Significance of the Study
 Statement of the Problem
 Methodology