This document provides an overview of research methods and statistical concepts. It discusses research design types including descriptive, historical, and experimental. Experimental design can be true experiments or quasi-experiments. It also discusses quantitative and qualitative research approaches and mixed methods. Key statistical concepts are defined, such as population, sample, probability and non-probability sampling, and levels of measurement. Common statistical tests are introduced along with important assumptions. The document provides guidance on how to measure learning experimentally using different research designs. It also discusses how to determine appropriate sample sizes and select statistical analyses based on the research questions.
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The Development of the Biostatistics & Clinical Epideimiolgy Skills (BACES) A...Pat Barlow
A poster based on my dissertation work developing a new biostatistics and clinical epidemiology skills self-assessment for medical residents. I used an Item Response Theory (IRT) approach to gather preliminary data on 30 multiple choice items that will eventually turn into an online self-assessment module for residents looking to hone their skills in these critical areas. The article is currently under review in Medical Teach, and I am looking for more sites / collaborators for expanding the instrument. Email if you're interested!
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http://KAMCResearch.org
Bursys, software engineering company developing software products, mobile applications, CAD experts meets specific CAD standards for USA, UK, and Australia.
Meaning, Need, Features, Concept relating to Research Design, Types of Research Design, Sampling – Meaning, Steps in Sampling, Sampling Methods (Probability and Non-Probability Methods)
This was a presentation that was carried out in our research method class by our group. It will be useful for PHD and master students quantitative and qualitative method. It consist sample definition, purpose of sampling, stages in the selection of a sample, types of sampling in quantitative researches, types of sampling in qualitative researches, and ethical Considerations in Data Collection.
Summary slides for "Systematic Review and Meta-Analysis Course for Healthcare Professionals", January 8-9, 2013, King Abdullah Medical City, Makkah, Saudi Arabia
http://KAMCResearch.org
Bursys, software engineering company developing software products, mobile applications, CAD experts meets specific CAD standards for USA, UK, and Australia.
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Cambridge International AS A Level Biology Coursebook - EBook (MaryFosbery J...
Methodology
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
2
2
D
π)π(1Z
n
2
D
F)P-(1P2
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