UNIVERSITY OF CAPE COAST
Cape Coast, Ghana
SOC 311: Research Methods
Solomon Sika-Bright (Ph.D.)
Dept. of Sociology and Anthropology
UNIVERSITY OF CAPE COAST
CAPE COAST - GHANA
Research Design
•The researcher’s overall plan for obtaining
answers to the research questions or for testing
the research hypotheses.
•A research design provides the framework for the
collection and analysis of data.
•Research design outlines:•Data and how it will be collected
•Analysis and interpretation of data
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Research Design
•Popular research designs
•Case study
•Phenomenology
•Field Survey
•Ethnography
•Experimental design •Secondary data analysis
•Observational design •Evaluative design
•Comparative design
•Historical/Content
analysis
•Action research
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Research Design
• The Case Study Design
• An in-depth investigations of a problem in one or a small
number of real-life settings (cases) over an extended period of
time.
• It usually provides rich detail about those cases, of a
predominant qualitative nature.
• A case study generally aims to provide insight into a particular
situation and often stresses the experiences and
interpretations of those involved.
• It may generate new understandings, explanations or hypotheses.
• However, it does not usually claim representativeness and should
be careful not to over-generalise.
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Research Design
• The Survey Design
• Surveys capture snapshots of practices, beliefs, or situations
from a random sample of subjects in field settings through a
survey questionnaire or a structured interview.
• Because of the larger number of cases, a survey generally
involve some quantitative analysis
• Alternatively, a survey might make use of already available
data, collected for another purpose.
• A survey may be cross-sectional (data collected at one time)
or longitudinal (collected over a period).
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Research Design
• Cross-sectional study
• Longitudinal Study
• Cross-sectional
• An observation of a sample or a cross-section of a population or
phenomenon that are made at one point in time.
• Longitudinal
• It is designed to permit observations of the same phenomenon
over an extended period of time.
• Three types:
• Trend studies
• Cohort studies
• Panel studies
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Research Design
Longitudinal
• Trend studies: a type of study that observes changes
within a population over time , that is where a given
characteristics of the same population is monitored over
time (Census analysis etc)
•Cohort studies: a type of study that examines specific
sub-populations (cohorts) as they change over time
although data may be collected from different members
in each set of observation (age, era borns, etc).
• Panel studies: a type of study in which data are collected
from the same set of people at several points in time
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●
Research Design
Experimental Design
An experiment design investigates cause and effect under
highly controlled conditions.
-Experimental design is explanatory, meaning
that it asks not just what happens but why.
Typically, researchers conduct experiments to test
hypotheses, unverified statements of a
relationship between variables.
Most experiments are conducted in laboratories
and employ experimental and control groups.
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Research Design
● An experiment has 2 main features:
a. Researcher’s ability to manipulate the independent
variable (making it vary as they wish)
b. There is random assignment of persons to groups exposed
to different levels of the independent variable.
-People who take part in experiments are called subjects
Because they are subjected to different values of the
independent variable
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Research Design
Design
Type
Experiment
Case study
Survey
Method
Questionnaire
Questionnaire
Questionnaire
Interview
Interview
Interview
Observation
Observation
Observation
Analysis of
Documents
Analysis of
Documents
Analysis of
Documents
Sample and Sampling Techniques
§Population/ census/ universe vrs sample
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• Population: The total number of the units of the
phenomenon to be investigated in the study area. Eg. All
students, all doctors, all Ghanaians etc.
• Sample: A proportion of the number of units in the universe
selected for investigation.
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Sample and Sampling Techniques
• A sample is “a smaller (but hopefully representative)
collection of units from a population used to
determine truths about that population” (Field,
2005).
• Why sample?
• Resources (time, money) and workload
• Gives results with known accuracy that can be
calculated mathematically
• The sampling frame is the list from which the
potential respondents are drawn
• Class registers
• List of households in a community
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Sample and Sampling Techniques
• The sampling process comprises several stages:
• Defining the population of concern
• Specifying a sampling frame, a set of items or
events possible to measure
• Specifying a sampling method for selecting items
or events from the frame
• Determining the sample size
• Implementing the sampling plan
• Sampling and data collecting
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Sample and Sampling Techniques
• 2 main factors that influence sample representative-ness
• Sampling procedure
• Sample size
• When might you sample the entire population?
•When your population is very small
•When you have extensive resources
•When you don’t expect a very high response
Sample and Sampling Techniques
•Probability (Random) Samples
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• Simple random sample
• Systematic random sample
• Stratified random sample
• Multistage sample
• Cluster sample
•Non-Probability Samples
• Convenience sample
• Purposive sample
• Quota
• Snowball
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Sample and Sampling Techniques
• PROBABILITY SAMPLING: Drawing a sample that is
statistically representative of the target population.
• STEP 1: Identify target population.
• STEP 2:Construct the sampling frame such that all
population elements have a known, non-zero
probability of being selected.
• STEP 3: Determine the sample size
STEP 4: Select the sample using random
procedure best suited for the study.
sampling
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Sample and Sampling Techniques
• Determining the sample size:
• Krejcie and Morgan (1970) sample determination
formula is widely used.
• The formula is as follows:
s= X2 NP(1-P)÷d2(N-1)+X2P(1-P)
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Sample and Sampling Techniques
s= X2 NP(1-P)÷d2(N-1)+X2P(1-P)
s = required sample size
X2 = the table value of chi-square for 1 degree of freedom
at the desired confidence level (3.841)
N = the population size.
P = the population proportion (assumed to be .50 since
this would provide the maximum sample size).
d = the degree of accuracy expressed as a proportion
(.05).
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Sample and Sampling Techniques
s= X2 NP(1-P)÷d2(N-1)+X2P(1-P)
s = required sample size
X2 = (3.841)
N = 100
P = 0.5
d = 0.05.
s= X2 NP(1-P)÷d2(N-1)+X2P(1-P)
s= 3.841(100×0.5)(1-0.5) ÷0.052(100-1)+3.841(0.5)(1-0.5)
s= 79.5=80
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Sample and Sampling Techniques
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Sample and Sampling Techniques
Sample size determination for
most practical situations
Source: Krejcie, R. & Morgan
D. (1970). Determining
Sample Size for Research
Activities, Educational and
Psychological Measurement,
30, 607-610.
Where:
N = Population Size
S = Sample Size
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Sample and Sampling Techniques
•Simple Random Sampling
• it is the most basic of the probability sampling methods
‘Random’ means mathematically each unit from the sampling
frame has an equal chance of being included in the sample.
It is more appropriate when a population size is small and has
similar characteristics of interest
• The sampling units are selected through two main methods
• The lottery method
• The table of random numbers
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Sample and Sampling Techniques
•Simple Random Sampling
The lottery method:
• Construct a sampling frame
• Determine the sampling size
• Write names listed on the sampling frame or their numbers
in slips of paper and put in a container
• Mix well and remove a slip at a time from the container
• Register or record the name or number. Put it back into the
container before the next one is picked
• Continue the process till the sample is achieved.
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Sample and Sampling Techniques
•Simple Random Sampling
Using The Table of Random Numbers
• Samples are selected using already generated tables of random
numbers usually found at the back of statistics textbooks
• Construct a sampling frame
• Determine the sampling size
• Select table of random numbers
• Pick numbers from table and register the name in sampling frame
corresponding to the number.
• The table is entered randomly where the researcher proceeds
vertically or horizontally
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Sample and Sampling Techniques
•Simple Random Sampling
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Systematic Random Sampling
Sample points are spread over entire sampling frame.
1)Determine the sampling interval N/n
• where N = population size; n = sample size
• e.g., N = 8000 and n = 200 è N/n = 8000/200 = 40
• 2) Determine one random number (k) between 1 and 40 in the
first interval. e.g.,
k = 12
• 3) The sample contains the kth element in each sampling interval.
i.e., 1st interval è12th element
•
2nd interval è12 + 40 = 52nd element
•
3rd interval è12 + 80 = 92nd element
•
4th interval è 12 + 120 = 132nd element …
•
200th interval è 12 + 7960 = 7072nd element
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Stratified Random Sampling
• Stratification – splitting the population into the different
strata (variables e.g. gender, age, ethnic background).
• ‘Gold standard’ of sampling. Why?
• Designed to be more representative of the population
where the sampling frame is ‘stratified’ according to
population variables .
• Variables selected for stratifying are determined by the
characteristics needed by the research.
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Stratified Random Sampling
•Identify the stratification variable and divide population
accordingly
•Develop sampling frame for each stratum/group
•Employ any of the random sampling method to select
sample from each group.
• Since each stratum is treated as an independent
population, different sampling approaches can be
applied to different strata.
•Merge all samples as one sample for the population.
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Cluster Sampling
• Cluster sampling is an example of 'two-stage
sampling' .
• 1st stage: A sample of areas is chosen;
• 2nd stage: A sample of respondents within those
areas is selected.
• Population divided into clusters of homogeneous
units, usually based on geographical contiguity.
• Sampling units are groups rather than individuals.
• A sample of such clusters is then selected.
• All units from the selected clusters are studied.
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Cluster Sampling
Difference between cluster and Strata
• Although strata and clusters are both non-overlapping
subsets of the population, they differ in several ways.
• All strata are represented in the sample; but only a subset
of clusters are in the sample.
• With stratified sampling, the best survey results occur
when elements within strata are internally homogeneous.
• However, with cluster sampling, the best results occur when
elements within clusters are internally heterogeneous
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Multi-stage Sampling
• Complex form of cluster sampling in which two or
more levels of units are embedded one in the
other.
• First stage, random number of districts chosen
in Cape Coast Metropolis
• Followed by random number of towns within the
sampled districts.
• Then third stage units will be houses within the
sampled towns.
• All ultimate units (houses, for instance)
selected at last step are surveyed.
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Non-Probability Sampling
• Any sampling method where some elements of
population have no chance of selection
• Nonprobability Sampling includes: Convenience
Sampling, Quota Sampling, Purposive Sampling and
Snowball Sampling
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Convenience Sampling
• Sometimes known as opportunity sampling or
accidental or haphazard sampling.
• A type of non-probability sampling which involves
the sample being drawn from that part of the
population which is close to hand. That is, readily
available and convenient.
• – Cannot generalise findings (do not know what population
group the sample is representative of) so cannot move
beyond describing the sample.
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Convenience Sampling
• Use participants that are easy to
get
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•
Quota Sampling
Aim is to sample reflecting proportions of
population in different categories or quotas (e.g.
gender, age, ethnicity).
• Used often in market and opinion poll research.
• + easy to manage, quick
• – only reflects population in terms of the quota,
possibility of bias in selection.
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Snowball Sampling
• Useful when a population is hidden or difficult to gain access
to.
• The contact with an initial person or group is used to make
contact with others.
• + access difficult to reach populations (other methods may
not yield any results).
• - not representative of the population and will result in a
biased sample as it is self-selecting.