DEVELOPING RESEARCH PROPOSAL
METHODOLOGY
LEARNING OBJECTIVE
■ To understand the research design
■ To identify the sampling techniques
■ To identify technique for sample size determination
■ To identify appropriate research instrumentation
■ To identify appropriate data collection procedures
■ To determine appropriate data analysis
TABLE OF CONTENT
■ Research design of the study
■ Sampling procedures
■ Sample size determination
■ Instrumentation
■ Data collection procedures
■ Data analysis
INTRODUCTION
■ In Methodology, subtopics that need to add in are
1. Introduction
2. Research Design
3. Sampling
4. Instrumentation
5. Data collection procedures
6. Data analysis
RESEARCH DESIGN
■ RESEARCH DESIGN refers to the plan, structure, and strategy of research--the
blueprint/framework that will guide the research process (data collection,
measurement, analysis).
■ To choose the research design, answer:
1 - What is going on (Descriptive research)?
2 - Why is it going on (Experimental research)?
■ Deliver the evidence necessary to answer the research problem as
accurately, clearly as possible.
RESEARCH DESIGN
• THE TEST FOR THE QUALITY OF A STUDY’S RESEARCH
DESIGN IS THE
STUDY’S CONCLUSION VALIDITY.
RESEARCH DESIGN
➢ CONCLUSION VALIDITY
refers to the extent of researcher’s ability to draw accurate conclusions from the
research. that is, the degree of a study’s:
a) INTERNAL VALIDITY— correctness of conclusions regarding the relationships among
variables examined
• whether the research findings accurately reflect how the research variables are
really connected to each other.
b) EXTERNAL VALIDITY – generalizability of the findings to the intended/appropriate
population/setting
• whether appropriate subjects were selected for conducting the study
DESCRIPTIVE RESEARCH (SURVEY)
■ Determining the views or practices
■ describe the attitudes, opinions, behaviors, or characteristics
■ Interviews or by administering a questionnaire
■ Limitation:
– Do not reveal factors that cause or influence
– Information obtained maybe inaccurate or misinterpreted
DESCRIPTIVE RESEARCH (SURVEY)
■ Method:
– Phone interview
■ Not often used. Mostly used in marketing research
– Personal interview
■ Meets with each member of the sample needed
– Distributed questionnaire
■ Sample have geographically spread or cannot be brought together as group.
■ By mailing, distributed by hand or having another person to distribute
DESCRIPTIVE RESEARCH
(CROSS SECTIONAL)
■ Method for testing/observational study that analyzes data from a population, or a
representative subset, at a specific point in time—that is, cross-sectional data.
– Eg: fitness level for different age
Different group from age 6 – 18 had been taken and assess their fitness.
– Limitation: cannot always answer longitudinal study
DESCRIPTIVE RESEARCH
(LONGITUDINAL)
■ The collection of data at more than one point in time over an extended period.
■ Eg: twice a year respondent from age 6 – 18 will be observed their fitness level.
■ May takes many years to complete
■ Difficult to keep track the participant over the many years
■ The result more valuable compare to cross sectional study
DESCRIPTIVE RESEARCH
(CASE STUDY)
■ Measurement and/or observation of a small group of individual or a
person or event in great detail
■ Highly organized and very systematic in collecting the information
needed to write the report
DESCRIPTIVE RESEARCH
(CORRELATIONAL)
■ To determine if a relationship exist between variables
■ Could not establish cause and effect
DESCRIPTIVE RESEARCH
(OBSERVATION)
■ Observations of people or program
■ Eg:
– 5 days a week for 18 weeks a researcher observed a
community program and wrote down everything he
observed
– End of 18 weeks, the researcher wrote a report based on
those recorded observations
WHAT CAN BE OBSERVED
Human behavior or physical
action
Shoppers movement
pattern in a store
Verbal behavior
Statements made by
airline travelers who wait
in line
Expressive behavior
Facial expressions, tone of
voice, and other form of
body language
DESCRIPTIVE RESEARCH
(CAUSAL COMPARATIVE)
■ Also call ex post facto
■ Seeks to investigate cause and effect relationships that explain differences
that already exist in groups or individuals
■ Eg: investigate the effect of eyesight on motor skill
■ It cannot be manipulated where it is unethical
■ Just look at the different between groups but not a causality
EXPERIMENTAL RESEARCH
■ Independent variable is manipulated to observe the effect on a
dependent variable
■ The purpose is to determine a cause and effect relationship
■ IV consist of control and treatment group can be manipulated to
get the outcome (DV)
EXPERIMENTAL RESEARCH (PRE-EXPERIMENTAL)
EXPERIMENTAL RESEARCH (TRUE EXPERIMENTAL)
■ The most recommended
■ Good control group
■ Random sampling
■ All threats to internal validity control
■ Example: pre post test and control group
– Pre post test: received treatment between pre and post
– Control group: receives no treatment
– Random sampling perform to divide sample between each groups
EXPERIMENTAL RESEARCH
(QUASI- EXPERIMENTAL)
■ Much better than pre experimental design
■ Either lack random sampling of participants or random assignment of
participants to groups
■ Example: Pre post-test and control group BUT not assigned to groups by using
random sampling
SUMMARY
EXAMPLE
EXPERIMENTAL RESEARCH
■ Explain data
collection protocol
■ Example >>>
PILOT STUDY
■ Use a tool with established reliability
■ Pilot study:
– to establish reliability prior to actual data collection
– Test reliability and validity of the measurement
– Reliability: consistency of the measurement
– Validity: test measuring what it is intended to measure
PILOT STUDY
Cont..
■ Eg: evaluate reliability of raters
– Rater’s responses are less reliable during data collection than
during specific testing
■ Questionnaire, report Cronbach’s alpha (more than .80 acceptable)
SAMPLING
To identify technique for sample population determination
SAMPLING PROCEDURE
■ Participants must be appropriate to the methods, techniques, and
instrumentation
■ Availability of participant throughout study duration?
■ Number must be large enough to
– Ensure reliability of the research results
– Permit a reasonable number of participants
Three basic questions to consider:
1. Participants appropriate for the research question?
2. participants representative of the population?
3. How many research participants should be used?
TECHNICAL SAMPLING TERMS
■ Population - refers to an entire group or aggregate of people
having common characteristics
■ Sample – a small subgroup of a population thought to be
representative of a population
■ Sampling – the process of selecting a subgroup or sample of the
population
■ Sampling Frame –
the accessible population or collection of elements
from which the sample is actually drawn
SAMPLING PROCEDURE
■ Sampling process:
– Identify the target population
– Identify the accessible population
– Determine the desired sample size
– Select the specific sampling technique
– Implement the sampling plan
RANDOM PROCESSES IN RESEARCH
■ Random Selection
• Enable generalization of the
results to a larger population.
■ Random Assignment
• Enable assumption that groups
are “equivalent” at the
beginning of the study.
• This adds control to a study; it
has nothing to do with the
selection of the sample.
RANDOM PROCESSES IN RESEARCH
Randomization:
– To ensure representativeness of the
sample to the population
– Unbiased
– Equalize characteristics
SAMPLE SELECTION METHODS
Probability Sampling
– Sampling techniques in which
probability of selecting
known
– Utilizes random processes,
guarantee the
population
the
each participant is
but does not
sample is representative of
– Every element within the population has a known probability of being selected for
the sample
– Estimates of sampling error (chance variation may occur) are possible
SAMPLE SELECTION METHODS
Non Probability Sampling
– Samples are not selected at random
– Difficult to claim sample is representative of population
– Intact groups,
volunteers
SAMPLE SELECTION METHODS
■ Probability Sampling
– Simple random sampling
– Stratified random sampling
– Systematic sampling
– Cluster sampling
■ Non Probability Sampling
– Purposive sampling
– Convenience sampling
SIMPLE RANDOM SAMPLING
■ Equal probability of being selected for the sample.
■ The selection of one member of the population does not affect the chances of
any other
■ Bias free: no factor can affect selection
■ Sampling with replacement vs. without replacement
■ Usual procedure:
– Fishbowl technique
– Table of random numbers
– Computer generated sampling
STRATIFIED RANDOM SAMPLING
▪
One obtained by separating the population elements into non-overlapping subgroups (strata) and then selecting a simple random sample from each strata.
▪
No sampling unit can appear in more than one strata.
▪
The number of sampling units drawn
from each strata depends upon the size
of the sampling frame as well as each
strata
SYSTEMATIC SAMPLING
An alternative to simple random sampling
in which the sampling units are selected
in a series according to some
predetermined sequence.
CLUSTER SAMPLING
■ A simple random sample in which each sampling unit is a collection
(group) or cluster, of elements
■ The sampling unit is the “cluster.”
■
•
•
•
Cluster sampling is an effective when:
A good frame listing population elements is not available
Removal of elements from cluster unit is not possible
Impractical to conduct simple random sampling
NONPROBABILITY SAMPLING
■ The probability that an element will be chosen is not known, with the result
being that a claim for representativeness of the population cannot be made
■ The researcher’s ability to generalize findings beyond the actual sample is
greatly limited
■ But ... it is less expensive and less complicated
■ Convenience sampling and purposive
sampling are common examples
PURPOSIVE SAMPLING
■ When members of the sample are purposively
selected because they possess certain traits that are
critical to the study
■ Limited generalizability
■ Example: Selecting elite athletes for a
biomechanics study
CONVENIENCE SAMPLING
■ Refers
to
selecting
research
participants on the basis of being
accessible and convenient to the
researcher
■ Often involves use of volunteers
■ Limited generalizability
■ Example: Using fellow graduate
students as research participants
SAMPLE SIZE
To identify appropriate sample size
SAMPLE SIZE
■ Sample size must representative of the population
■ A well selected and controlled small sample is better than a
poorly selected and poorly controlled large sample.
■ Points to consider regarding sample size:
– Nature of the study
– Statistical considerations
– Number of treatment groups
– Practical factors
IDENTIFY THE
SAMPLE SIZE
Table by Krejcie and Morgan (1970)
2. Power and effect size (ES) (G power software)
– Power:
■ Probability of making a correct rejection of the null
hypothesis when it is false
■ Acceptable level of power is more than 80 (Cohen, 1992)
– Effect size:
■ The larger different between 2 pop means, the greater ES
■ The smaller variance within 2 population, the greater ES
■ There are the scale for each type of effect size
EFFECT SIZE
D
• 0.2 BE CONSIDERED A 'SMALL' EFFECT SIZE,
• 0.5 REPRESENTS A 'MEDIUM' EFFECT SIZE
• 0.8 A 'LARGE' EFFECT SIZE
EFFECT SIZE
S : pooled standard deviation
INSTRUMENTATION
Instrumentation is the course of action (the process of developing, testing, and using the device).
INSTRUMENTATION
■ The published instruments (device)
– cite manufacturer’s specification or
– State the validity and reliability of the instrument
■ Main concern:
– Establish the instrumentation used in the study was appropriate for the particular
research problem
– The process of using the device
■ If the instrument is already established, the proposal should include its name and reported
reliability and validity
■ List out every variable that will be measured and instrument that will be used to measured the
variable
DATA COLLECTION PROCEDURE
EXAMPLE:
Ethic approval
Ask permission to do research
Give briefing to the sample about the study and
questionnaire
Distribute questionnaire to the sample
Collect the questionnaire
DATA ANALYSIS
• Describes the methods of handling and presenting data and outlines the
statistical procedures to be used.
• Data presented as mean ± SD (SE)
• 2 types:
• Descriptive statistics
• Inferential statistics
DATA ANALYSIS
• Set the significant level (0.5, 0.05, 0.01)
DATA ANALYSIS
■ 2 types:
– Descriptive statistics
– Inferential statistics
Hypothesis
There is no significant
difference on body
weight between
endurance and strength
training among FSR
students
Inferential statistics
IV : strength training
endurance training
DV: body weight
▪ Set the significant level (0.5, 0.05, 0.01)
Paired sample t-tests
METHODOLOGY
3.1
3.2
Introduction
Research design
- Name of the design - This proposed study will use the randomized pretest
posttest control group design to test the hypothesis.
Justification - This design was chosen because .....
IV & DV
Research notation/ paradigm
R
01 X
03
R
02
04
-
Figure 1: ......
3.3
Population/ sample/participants/ sampling techniques
3.3.1 Population
3.3.2 Sample/participants,
- Inclusion criteria
- Exclusion criteria
- Sampling technique
3.3.4 Sample size
- calculation
Krejchie & Morgan table
- G-power calculation (show the table/graph)
3.4 Treatment (for experimental/quasi-experimental design only)
3.5 Instrumentation
3.5.1 Questionnaire (identify the questionnaire used) (cite)/domain/score
3.5.2 What do you use to measure IV and DV
3.5.2.1 Describe the model of the machine used/how to conduct
3.6 Data collection procedure
- Include also flowchart
3.7 Data analysis
THANKS!
ALinoby_19