Unit 2
Introduction
A research design is a framework or blueprint for conducting a marketing research project.
It details the procedures necessary for obtaining the information needed to solve marketing
research problems
Need for Research Design
It reduces inaccuracy;
Helps to get maximum efficiency and reliability;
Eliminates bias and marginal errors;
Minimizes wastage of time;
Helpful for collecting research materials;
Classification of Research design
Research designs may be broadly classified as exploratory or conclusive
Exploratory research
The primary objective of exploratory research is to provide insights and helps in
understanding of marketing phenomena
Four exploratory techniques
Secondary Data Analysis
Projective Techniques
Focus groups
Depth interviews
Descriptive Research
Descriptive research, as the name implies, describes characteristics of objects, people,
groups, organizations, or environments
Descriptive research can be further classified into cross-sectional and longitudinal research
Cross-sectional designs: Cross-sectional designs involve the collection of information from
any given sample of population elements only once.
Longitudinal designs: Longitudinal studies repeatedly draw sample units of a population
over time.
Causal research: Causal research is used to obtain evidence of cause-and-effect (causal)
relationships.
Measurement
Measurement means assigning numbers or other symbols to characteristics of objects
according to certain pre-specified rules.
Scaling
Scaling involves creating a continuum upon which measured objects are located.
Primary scales of measurement
There are four primary scales of measurement: Nominal, Ordinal, Interval and Ratio
Nominal scale and ordinal scales are also called as categorical data
Interval and ratio scales are called as metric data.
Nominal scales:
A nominal scale is a labelling scheme in which the numbers serve only as labels or tags for
identifying and classifying objects.
Ordinal scales
Ordinal scales are those that measure rank-ordered data, such as the ranking of students in a
class as first, second, third, and so forth, based on their grade point average or test scores.
Interval scale
Interval scales are those where the values measured are not only rank-ordered, but are also
equidistant from adjacent attributes.
Ratio scale
Ratio scales are those that have all the qualities of nominal, ordinal, and interval scales,
and in addition, also have a “true zero” point (where the value zero implies lack or non
availability of the underlying construct).
Comparison of scaling techniques
The scaling techniques can be classified into comparative and non-comparative scales
Comparative scales:
Comparative scales involve direct comparison of stimulus objects.
Ex : Respondents may be asked whether they prefer Coke or Pepsi.
Non comparative scales:
Also referred as metric scales. Each object is scaled independently of the others in the
stimulus set.
Ex: Respondents may be asked to evaluate Coke on a 1 to 5 preference scale (1 = not at all
preferred, 5 = greatly preferred).
Comparative scales:
Paired comparison scaling
Rank order scaling
Constant sum scaling
Q-sort procedures
Paired comparison scaling: The respondent is presented with two objects and asked to
select one according to some criteria. The data obtained are ordinal in nature.
Rank order scaling: In rank order scaling respondents are presented with several objects
simultaneously and asked to order or rank according to some criteria.
Constant sum scaling: In constant sum scaling, respondents allocate a constant sum of units,
such as points among a set of stimulus objects with respect to some criteria.
Q-sort procedures: This technique uses a rank order procedure in which objects are sorted
into piles based on similarity with respect to some criterion.
Non-comparative scaling techniques
Continuous rating scale
Itemised rating scales
Continuous rating scale: In a continuous rating scale, also referred to as a graphic rating
scale, respondents rate the objects by placing a mark at the appropriate position on a line that
runs from one extreme of the criterion variable to the other.
Itemised rating scales: In an itemised rating scale, respondents are provided with a scale that
has a number or brief description associated with each category. The categories are ordered in
terms of scale position
Likert scale: The Likert scale is a widely used rating scale. It requires the respondents to
indicate a degree of agreement or disagreement with each of a series of statements about the
stimulus objects.
Semantic differential scale: The semantic differential is a seven-point rating scale with end
points associated with bipolar labels that have semantic meaning.
Stapel scale: The Stapel scale, named after its developer, Jan Stapel, is a unipolar rating scale
with 10 categories numbered from –5 to +5, without a neutral point (zero). This scale is
usually presented vertically. Respondents are asked to indicate by selecting an appropriate
numerical response category
Sampling design and procedures
1. A population is the set of all the elements of interest in a study.
2. A sample is a subset of the population.
Sampling is the statistical process of selecting a subset (called a “sample”) of a population of
interest for purposes of making observations and statistical inferences about that population.
The sampling design process
1. Define the target population
2. Determine the sampling frame
3. Select sampling techniques(s)
4. Determine the sample size
5. Execute the sampling process
6. Validate the sample
Sampling techniques
Probability sampling is a technique in which every unit in the population has a chance (non-
zero probability) of being selected in the sample.
Probability sampling types:
Simple random sampling
Systematic sampling
Stratified sampling
Cluster sampling
Simple random sampling: A sampling procedure ensuring that each element in the
population will have an equal chance of being included in the sample is called simple
random sampling.
Systematic sampling: In systematic sampling, the sample is chosen by selecting a random
starting point and then picking every ith element in succession from the sampling frame.
Stratified sampling :Stratified sampling is a two-step process in which the sampling frame
is partitioned into sub-populations, or strata. Each strata should be homogeneous and non-
overlapping subgroups
Cluster sampling: Cluster sampling is similar to stratified sampling as the sampling frame
is divided into discrete groups prior to sampling. The groups are termed clusters.
Non-probability sampling relies on the personal judgement of the researcher rather than on
chance to select sample elements.
Non-Probability sampling types:
Convenience sampling
Judgemental sampling
Quota sampling
Snowball sampling
Convenience sampling refers to sampling by obtaining people or units that are conveniently
available.
Judgemental sampling is a form of convenience sampling in which the population
elements are selected based on the judgement of the researcher.
Snowball sampling is commonly used when it is difficult to identify members of the
desired population, for example people who are working while claiming unemployment
benefit.
Quota sampling is entirely non-random and is normally used for interview surveys. It is
based on the premise that your sample will represent the population as the variability in
your sample for various quota variables is the same as that in the population.
