Methadology

specific strengths of using qualitative methods

— Obtain a more realistic view of the lived world that cannot be
understood or experienced in numerical data and statistical
analysis;

— Provide the researcher with the perspective of the participants
of the study through immersion in a culture or situation and as a
result of direct interaction with them;

— Allow the researcher to describe existing phenomena and
current situations;

— Develop flexible ways to perform data collection, subsequent
analysis, and interpretation of collected information;

— Yield results that can be helpful in pioneering new ways of
understanding;

— Respond to changes that occur while conducting the
study ]e.g., extended fieldwork or observation] and offer
the flexibility to shift the focus of the research as a result;

— Provide a holistic view of the phenomena under
investigation;

— Respond to local situations, conditions, and needs of
participants;

— Interact with the research subjects in their own language
and on their own terms; and,

— Create a descriptive capability based on primary and
unstructured data.

Specific limitations associated with using qualitative
methods

— Drifting away from the original objectives of the study in response
to the changing nature of the context under which the research
is conducted;

— Arriving at different conclusions based on the same information
depending on the personal characteristics of the researcher;

— Replicatication of a study is very difficult;
— Research using human subjects increases the chance of ethical

dilemmas that undermine the overall validity of the study;
— An inability to investigate causality between different research

phenomena;

— Difficulty in explaining differences in the quality and
quantity of information obtained from different
respondents and arriving at different, non-consistent
conclusions;

— Data gathering and analysis is often time consuming
and/or expensive;

— Requires a high level of experience from the researcher to
obtain the targeted information from the respondent;

— May lack consistency and reliability because the
researcher can employ different probing techniques and
the respondent can choose to tell some particular stories
and ignore others; and,

— Generation of a signficant amount of data that cannot
be randomized into managable parts for analysis

Different between quantitative & Qualitative

Attributes Qualitative Research Methods Quantitative Research
Methods

This research method focuses on to Quantitative research method

Analytical objectives describe individual experiences and focuses on describing the

beliefs. characteristics of a population.

Types of questions Open ended question Closed ended question
asked
Use semi-structured methods such as Use highly structured
Data collection in-depth interviews, focus groups, methods such as structured
Instrument and participant observation observation
using questionaires & servey
Form of data
produced Descriptive data Numerical data

Degree of flexibility Participant responses do not
Participant responses affect how and influence or determine how
which questions researchers ask next and which questions

researchers ask next

Population & sampling

Population

— A research population is also known as a well-
defined collection of individuals or objects known
to have similar characteristics. All individuals or
objects within a certain population usually have a
common, binding characteristic or trait.



Sampling

Definition

A sample is defined as a subset of the target
population selected to represent the population that
the researcher would like to generalize the result of
the study

Purpose

— sampling is to gain information about the
population through a sample as it is usually not
feasible to conduct a census survey

Criteria

— Inclusion criteria are the criteria for including subjects
in the study.

— Exclusion criteria are the criteria for excluding subjects
from the study

— These criteria are based on factors such as age,
geographical location, disease severity or stage,
previous treatment, presence of other medical
conditions

Type of sampling Probability
Sampling
Type
Non-Probability
Sampling

1.Probability sampling

— every subject in the target population has a more
than zero chance of being selected

— The main characteristic of a probability sample is
the use of random selection process to select a
sample from the target population

ProbabilitySampling technique

Simple random
sampling

Systemic
sampling

Stratified Random
sampling

Cluster Sampling

I. Simple Random Sampling

every subject in the group to be sampled has an
equal chance or even probability of being selected
through the use of a table of random numbers or
sampling with replacement.

II. Systematic Sampling

It is random sampling with a system.From the
sampling frame, a starting point is chosen at
random, and thereafter at regular e.g intervals is
selected. The e.g interval is calculated as:

Where n is the sample size, and N is the population
size. For example, if you have a population of
2,000 lecturers and you need to sample 200, then
every tenth person (2,000/200=10) can be selected



III. Stratified Random Sampling

– Process of selecting a sample in such a way that
identified sub- groups or strata in the population is
represented in the sample in the same proportion that
they exist in the population.

– Sample members are selected from each stratum at
random.



III. Stratified Random Sampling

– Population stratum may be chosen on the basis of
gender, geographical location, educational
background or income, etc. E.g. Urban-rural
stratification and university type (public-private)
stratification. Select equal samples from each
stratum or proportional allocation to the size of
the population.

Iv. Cluster Sampling

Cluster sampling selects groups, not individuals.
A random sample of cluster is selected and all
individuals in the cluster are included in the
study .



2.Non Probability sampling

— The sample subjects are chosen from the population
by non-random methods which are likely to
produce a bias sample

— The researcher cannot estimate the probability that
each subjects of the population will be included in
the sample

Non probabilityNon-Probability sampling technique
sampling
Convienience
sampling

Judgemental
Sampling

Quota Sampling

snow ball
sampling

a.Convenience Sampling (Accidental Sampling)

— sampling method in which the sample happen to be
available at the time or period of the study and is
selected for the sake of convenience.

— Convenience sampling does not represent any
group apart from itself. It does not seek to
generalise about the wider population.



b.Judgemental Sampling (Purposive Sampling)

— The researcher establishes certain criteria felt to be
representative of the target population and
deliberately selects subjects according to those
criteria.



c.Quota Sampling

— It is described as the non-probability equivalent of
stratified sampling .

— The researcher specifies a percentage for each
group of subjects so that the sample size does not
become overloaded with subjects having certain
characteristics.







Sample size

— Quantitative research it is very important to do
sample size calculations before embarking on a
study, because it may not be worthwhile to do a
study at all if the feasible sample size is much less
than the desirable sample size.

— the maximum sample size is determined by the
availability of resources such as time, manpower,
transport, and equipment

— A sample size of 30 is often quoted as the minimum
for any decent study

— The bigger the sample the more likely to detect a
difference between groups

— Sampling must never be biased

— The smaller the sample the larger the error and the
larger the sample the smaller the error (Kerlinger &
Lee, 2000)

— For Descriptive research: 10 – 20% of population (Gay
& Airasian, 2000)

— Generally, the sample size for any study depends on
the:

◦ Acceptable level of significance
◦ Power of the study
◦ Expected effect size
◦ Underlying event rate in the population
◦ Standard deviation in the population

How to determine population and sample
size

— A sample is a selection of respondents chosen in
such a way that they represent the total population
as good as possible.

— How many people should my sample consist of?

There are two measures that affect the accurateness of the
data.

— First

◦ margin of error (or confidence intervals)
this is the positive and negative deviation you allow on your
survey results for the sample. Or, in other words, the deviation
between the opinions of your respondents and the opinion of
the entire population.

◦ An example you set your

– margin of error on 5%,
– 90% of your respondents like(you can be ‘sure’ that between 85% (90%-5)

and 95% (90%+5) of the entire population.

— Second

◦ confidence level tells you how often the percentage of the
population that likes (lies within the boundaries of the
margin of error/how sure you can be that between 85%
and 95% of the population likes).

◦ your choose the 95% confidence level

— how many respondents you actually need.

◦ Depending on the confidence level and the margin of error

Concept of Sample Size in Medical Research

— Sample size too small

◦ Well conducted study may fail to
answer its research question.

◦ May fail to detect important effects
◦ May estimate those effects

imprecisely

— Sample size too large

◦ Costly – the longer the study the
higher it cost

◦ Difficulties face – lack of
manpower and time

◦ Tiring – recruitment of outcome or
subjects maybe tiring for a long
time

Larger Samples are needed when…

— a large number of uncontrolled variables are interacting
unpredictably

— the total sample is to be divided into several subsamples
(the researcher is interested in also studying subgroups
within the sample)

— the population is made up of a wide range of variables
and characteristics

— differences in the results (effect size) are expected to be
small

— high attrition of subjects is expected

Concept of Sample Size in Medical Research

— Do researchers estimate their sample size?
— Sample size estimation should in line with the

objective.
— This will involve various types of statistical tests

Krejcie &Morgan,1970

— One of the most used method is the Krejcie and
Morgan Sampling Method. To simplify the process
of determining the sample size for a finite
population, Krejcie & Morgan (1970), came up with
a table using sample size formula for finite
population.



Concept of Sample Size in Medical Research

Sample size Objective

Statistical test:
Eg: Ind. Samp. T
-test, Pearson Chi

Square,
Correlation, etc

Formula!

Sample size: What is it?

— The minimum required number of
sample/observation to be included in a study

— Depends on study objective, study design and
statistical analysis

Sample size: Why is necessary?

— Guide : When to start and stop collecting? How are
we going to collect it?

— Feasibility: Depends on availability of the sample,
time constraint, subject constraint and ethical issues

— Research design : Influence the quality and accuracy
of research

— Economy : Waste of resources if not having the
capability to produce useful results

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