This document discusses research design and approaches. It begins by defining research design and explaining its importance. A good research design provides a framework for data collection and analysis, establishes a plan to answer research questions, and maximizes control over variables. The document outlines key concepts in research design like dependent and independent variables. It also describes different forms of research design classified by purpose, number of contacts with subjects, and nature of investigation, including descriptive, exploratory, explanatory, cross-sectional, before-and-after, and longitudinal designs.

1. Alpha University College
Business Research Methods
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2. Part III
Research Design and
Approach
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3. Contents
:
Meaning and Importance of Research
Design
Important Concepts Concering
Research Design
Features of Good Research Design
Forms/Classifications of Research
Design
Research Approaches
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4. 1. Meaning and Importance of Research Design
 Once the researcher has determined the specific question to be
answered and research question into a clear, measurable
objectives, it is time to consider a suitable research design.
 Design is a means/key for research
 Design refers to the over all strategy that you choose
 A research design –is a grand plan of approach to a research topic.
 is a master plan/a blue print or conceptual structure/ that
specifies the methods and procedures for collecting, measuring and
analyzing the needed information.
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5. Meaning …
 A research design provides a framework for the collection and
analysis of data.
 Research design represents a structure that guides the execution
of a research method and the analysis of the subsequent data.
 A choice of research design reflects decisions about the priority
being given to a range of dimensions of the research process.
These include the importance attached to:
 expressing causal connections between variables;
 generalizing to larger groups of individuals than those actually forming
part of the investigation;
 understanding behavior and the meaning of that behavior in its
specific social context;
 having a temporal (i.e. over time) appreciation of social phenomena
and their interconnections.
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6. Meaning...
 Research design is the blueprint for fulfilling research
objectives and answering research questions.
 In other words, it is a master plan specifying the
methods and procedures for collecting and
analyzing the needed information
 it must ensure that the information collected is
appropriate for solving a problem.
 “A research design is the arrangement of conditions for
collection and analysis of data in a manner that aims
to combine relevance to the research purpose with
economy in procedure.”
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7. Importance of Research Design
 The research design is the conceptual structure
 within which research is conducted;
 constitutes the blueprint for the collection,
measurement and analysis of data.
 Research design specifies:
 the data collection process,
 the instrument development process,
 the sampling process, and
 The analysis proces
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8. Importance of…
 More specifically, research design is helpful in
addressing the following issues in research
 What is the study about?
 Why is the study being made?
 Where will the study be carried out?
 What type of data is required?
 Where can the required data be found?
 What periods of time will the study include?
 What will be the sample design?
 What techniques of data collection will be used?
 How will the data be analyzed?
 In what style will the report be prepared?
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9. Importance of…
 In a nutshell, research design:
 Maximizes control over factors to increase the
validity of the findings
 Guides the researcher in planning and
implementing a study
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10. Components of research design
 In attempting to address the above issues, a research
design can have the following components.
 the sampling design which deals with the method of
selecting items to be observed for the given study;
 the observational design which relates to the conditions
under which the observations are to be made;
 the statistical design which concerns with the question of
how many items are to be observed and how the information
and data gathered are to be analyzed; and
 the operational design which deals with the techniques by
which the procedures specified in the sampling, statistical
and observational designs can be carried out.
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11. 2. Features of Good Research Design
 The design that clearly stated the objective of the problem to be
studied,
 A good research design often possesses the qualities such as
being flexible, suitable, efficient, economical, and so on
 The design which minimizes bias and maximizes the reliability
of the data collected and analyzed;
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12. Features of…
 The design which gives the smallest experimental error;
 The design which yields relevant information and provides an opportunity for
considering many aspects of a problem;
 The design that assumes the availability and skills of the
researcher
 The design that considers the availability of time and money for
the research work
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13. 3. Important Concepts Relating to Research
Design
There are several concepts related to research design. These
are:
 Dependent and Independent variables
Extraneous variable
Control
Confounded relationship
Research hypothesis
Experimental and control groups
Treatments
Experiment
Experimental unit(s)
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14. Dependent and Independent Variables
 A concept which can take on different quantitative
values is called a variable. As such the concepts like
weight, height, income are all examples of variables.
Qualitative phenomena (or the attributes) are also
quantified on the basis of the presence or absence of
the concerning attribute(s).
 Phenomena which can take on quantitatively different
values even in decimal points are called ‘continuous
variables
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15. Dependent and Independent Variables
 But all variables are not continuous. If they can only be
expressed in integer values, they are non-continuous
variables or in statistical language ‘discrete variables’.**
 Age is an example of continuous variable, but the
number of children is an example of non-continuous
variable
 If one variable depends upon or is a consequence of
the other variable, it is termed as a dependent variable,
and the variable that is antecedent to the dependent
variable is termed as an independent variable.
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16. Dependent and Independent Variables
 For instance, if we say that height depends upon age,
then height is a dependent variable and age is an
independent variable
 Further, if in addition to being dependent upon age,
height also depends upon the individual’s sex, then
height is a dependent variable and age and sex are
independent variables.
 Similarly, readymade films and lectures are examples
of independent variables, whereas behavioural
changes, occurring as a result of the environmental
manipulations, are examples of dependent variables.
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17. Extraneous variable
 Independent variables that are not related to the
purpose of the study, but may affect the dependent
variable are termed as extraneous variables.
 Suppose the researcher wants to test the hypothesis
that there is a relationship between children’s gains in
social studies achievement and their self-concepts
.
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18. Extraneous variable
 In this case self-concept is an independent variable
and social studies achievement is a dependent
variable. Intelligence may as well affect the social
studies achievement, but since it is not related to
the purpose of the study undertaken by the
researcher, it will be termed as an extraneous
variable
 Whatever effect is noticed on dependent variable as a
result of extraneous variable(s) is technically described
as an ‘experimental error’
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19. Control
 One important characteristic of a good research design
is to minimize the influence or effect of extraneous
variable(s).
 The technical term ‘control’ is used when we design
the study minimising the effects of extraneous
independent variables. In experimental researches, the
term ‘control’ is used to refer to restrain experimental
conditions
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20. Confounded relationship
 When the dependent variable is not free from the
influence of extraneous variable(s), the relationship
between the dependent and independent variables is
said to be confounded by an extraneous variable(s).
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21. Research hypothesis
 When a prediction or a hypothesised relationship is to
be tested by scientific methods, it is termed as
research hypothesis
 The research hypothesis is a predictive statement that
relates an independent variable to a dependent
variable
 Usually a research hypothesis must contain, at least,
one independent and one dependent variable.
 Predictive statements which are not to be objectively
verified or the relationships that are assumed but not
to be tested, are not termed research hypotheses
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22. Experimental and non-experimental
hypothesis-testing research
 When the purpose of research is to test a research
hypothesis, it is termed as hypothesis-testing research
 It can be of the experimental design or of the non-
experimental design. Research in which the
independent variable is manipulated is termed
‘experimental hypothesis-testing research’
 and a research in which an independent variable is not
manipulated is called ‘non-experimental hypothesis-
testing research’.
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23. Experimental and non-experimental
hypothesis-testing research
 For instance, suppose a researcher wants to study
whether intelligence affects reading ability for a group
of students and for this purpose he randomly selects
50 students and tests their intelligence and reading
ability by calculating the coefficient of correlation
between the two sets of scores.
 This is an example of non-experimental hypothesis-
testing research because herein the independent
variable, intelligence, is not manipulated
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24. Experimental and non-experimental
hypothesis-testing research
 But now suppose that our researcher randomly selects
50 students from a group of students who are to take a
course in statistics and then divides them into two
groups by randomly assigning 25 to Group A, the usual
studies programme, and 25 to Group B, the special
studies programme.
 At the end of the course, he administers a test to each
group in order to judge the effectiveness of the training
programme on the student’s performance-level.
 This is an example of experimental hypothesis-testing research
because in this case the independent variable, viz., the type of training
programme, is manipulated.
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25. Experimental and control groups
 In an experimental hypothesis-testing research when a
group is exposed to usual conditions, it is termed a
‘control group’,
 but when the group is exposed to some novel or special
condition, it is termed an ‘experimental group’.
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26. Treatments
 The different conditions under which experimental
and control groups are put are usually referred to as
‘treatments’.
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27. Experiment:
 The process of examining the truth of a statistical
hypothesis, relating to some research problem, is
known as an experiment
 For example, we can conduct an experiment to
examine the usefulness of a certain newly developed
drug
 Experiments can be of two types viz., absolute
experiment and comparative experiment
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28. Experimental unit(s):
 The pre-determined plots or the blocks, where
different treatments are used, are known as
experimental units.
 Such experimental units must be selected (defined)
very carefully.
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29. 4. Classifications of Research Design
 The various designs have been classified by
examining them from three different perspectives:
 purpose of the study
 the number of contacts with the study population;
 the reference period of the study;
 the nature of the investigation
 Other classifications
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30. Classifications …
1) Based on purpose of investigation, research
design is classified into:
 Descriptive design
 Exploratory design
 Explanatory design
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31. Classifications …
i) DESCRIPTIVE: investigator attempts to describe a group of
individuals/organizations on a set of variables or characteristics.
 Involves surveys & fact-finding enquiries
 State of affairs as it exists
 Enables classification and understanding
 No control over variables
 Tries to discover causes (i.e., ex-post facto)
 METHODS: survey research, case study, qualitative, developmental (natural history
of something, patterns of growth and change), normative, evaluation
 APPLIED: Finding a solution for an immediate problem & not rigorous / flexible in
application of the conditions
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32. Classifications …
 Researchers who choose to apply descriptive study should:
 Identify the problem accurately
 Demarcate the population
 Take representative sample from the population
 Record what has been perceived
 Organize and present the report
 E.g.
 Stating the status of organizational performance
 Investigating attitude of consumers towards a given brand
 Survey on community’s attitude and perception towards
corruption
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33. Classifications …
ii) EXPLORATORY
 gaining some familiarity with a topic, discovering some of its
main dimensions, and possibly planning more structured
research
 Its purpose is to shed light on the issue at hand.
 METHODS: observation, interview, discussions, document
review
 E.g.
 Exploring whether there is conflict in the workplace.
 Need assessment to develop infrastructure in a given locality
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34. Classifications …
iii) EXPLANATORY
 Investigates the degree to which two or more variables are
associated with each other;
 Examines the cause-effect relationships between variables
 This is more advanced design than the previous designs
 METHODS: survey questionnaire, recorded data, interviews
 E.g.
 What are the causes of conflict in the workplace?
 Is there relationship between organizational climate and
employee commitment?
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35. Classifications …
2) Based on the number of contacts with the study
population, research design is classified into:
 Cross-sectional studies;
 Before-and-after studies;
 Longitudinal studies.
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36. Classifications …
i) Cross-sectional Study
 Researcher studies a stratified group of subjects at one point in
time; also known as one-shot or status studies
 Well-suited to describing variables and their distribution patterns
 They are useful in obtaining an overall `picture' as it stands at the
time of the study.
 Such studies are cross-sectional with regard to both the study
population and the time of investigation.
 Can be used for examining associations; determination of which
variables are predictors and which are outcomes depends on the
hypothesis
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37. Classifications …
 E.g. what does the monthly spending composition of
households in X locality look like?
 Select a sample of 100 households participating in a
market (on a market day) in the locality.
 Identify items purchased by households
 Measure the amount spent on each item
 A questionnaire may be used to gather information
about items and the amount spent on each item.
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38. Classifications …
 Strengths
 Fast and inexpensive
 No loss to follow-up (no follow-up)
 Ideal for studying prevalence of a phenomenon, situation,
problem, attitude or issue
 Convenient for examining potential networks of causal links
 Weaknesses:
 Difficult to establish a causal relationship from data collected
in a cross-sectional time-frame (lack of a temporal relationship
between predictor variables and outcome variables - does not
establish sequence of events)
 Not practical for studying rare phenomena
 Cannot measure changes/stability
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39. Classifications …
ii) Before-and-after studies design (also known as the pre-
test/post-test design)
 can be described as two sets of cross-sectional observations
on the same population
 it can measure change in a situation, phenomenon, issue,
problem or attitude
 it is the most appropriate design for measuring the impact or
effectiveness of a program
 the change is measured by comparing the difference in the
phenomenon or variable(s) before and after the intervention
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40. Classifications …
 A before-and-after study is carried out by adopting the same
process as a cross-sectional study except that it comprises two
cross-sectional observations, the second being undertaken after
a certain period.
 Examples
 The impact of administrative restructuring on the quality of the
services provided by government offices
 The effect of televised advertisement on return on sales of a
certain brand
 The effect of tax programs on awareness and voluntary payment
of tax
 The effect of random breath testing on road accidents.
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41. Classifications …
 Strengths
 Can measure changes in phenomenon or situations etc
 Limitations
 It is more expensive and more difficult to implement
 It is less helpful for studying the pattern of change
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42. Classifications …
iii) Longitudinal studies
 Are used to determine the pattern of change and stability in
relation to time
 In these studies, the study population is visited a number of
times at regular intervals, usually over a long period, to
collect the required information
 They can be considered as repetitive cross-sections
 Longitudinal studies could be panel, time series or cohort
type
 Examples (the above examples can be taken)
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43. Classifications …
 Strengths
 it allows the researches to measure the pattern of change
 Disadvantages
 similar to before-and-after studies, in some instances to an
even greater degree.
 it can suffer from the conditioning effect- lose interest for
repeated questions in the inquiry
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44. Classifications …
3) Based on the reference period of the study, research designs
are classified as:
 Retrospective (reviewing past);
 Prospective (reviewing future); and
 Retrospective-prospective (both)
 The reference period refers to the time-frame in which a
study is exploring a phenomenon, situation, event or
problem.
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45. Classifications …
a) Retrospective (reviewing past) study designs
 They investigate a situation, problem or issue that has
happened in the past (historical design)
 They are usually conducted either on the basis of the data
available for that period or on the basis of respondents’
recall of the situation
 Examples
 Migratory movements from East Africa to Arab countries in
the last two decades
 FDI flow in the 27 years of the EPRDF regime
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46. Classifications …
b) Prospective (reviewing future) study designs
 Prospective studies refer to the likely prevalence of a
phenomenon, situation, problem, attitude or outcome in the
future.
 Such studies attempt to establish the outcome of an event or
what is likely to happen.
 Experiments are usually classified as prospective studies as
the researcher must wait for an intervention to register its
effect on the study population
 Examples
 Prospects of Islamic banking in Tigray
 The likelihood of rising inflation in the Ethiopian economy
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47. Classifications …
c) Retrospective-prospective (both) study designs
 focus on past trends in a phenomenon and study it into the
future.
 a study is classified under this category when you measure the
impact of an intervention without having a control group
 In this study a part of the data is collected retrospectively from
the existing records before the intervention is introduced and
then the study population is followed to ascertain the impact
of the intervention.
 Examples
 Fertilizer subsidies and farm productivity in the past and the
future
 School feeding and student academic performance
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48. Classifications …
4) Based on the nature of the investigation, research designs are
classified as follows
 Experimental design (True Experiment);
 Quasi-experimental design; and
 Non- experimental or observational design (one or more of the
previous types are of this nature)
 This classification is based on the control method applied in the study
 Efficacy: the benefit of an intervention compared to a control or standard
program under controlled, randomized conditions
 Randomized controlled trial (RCT) design often used
 Effectiveness: the benefit of an intervention under less controlled ‘real
world’ conditions
 Quasi-experimental design often used
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49. Classifications …
a) Experimental (True Experiment) Design
 study participants are randomly assigned to experimental
and control groups
 Purpose is to compare 2 or more groups that are formed by
random assignment
 The groups differ solely on the basis of what occurs between
measurements (i.e, intervention)
 Changes from pretest to posttest can be reasonably
attributed to the intervention
 Most basic is the pretest-posttest control group design
(randomized controlled trial, RCT)
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50. Classifications …
 Two groups: experimental and control groups (with and without
‘treatment’)- should be equivalent
 Randomization: participants are assigned at random to experimental
and control groups in the hope that this will result in equal
assignment of people to both groups (for small groups).
 change in the experimental groups before and after ‘treatment’
compared with change in the control group
 Matching is an alternative procedure to create equivalent groups
(large groups)
 Pairs of subjects are formed on the basis of similarity on one or
more variables.
 One subject is randomly assigned to the experimental group, the
other to the control group (i.e.: matched block design).
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51. Classifications …
 Purpose of an experiment:
 To investigate a causal relationship between two (or more) variables
 Way of establishing a causal relationship:
 Manipulation of the independent variable by the researcher
 Random assignment of the research units to the groups
 Control of all other conditions
 Origin:
 Natural sciences like chemistry, biology, etc.
 Importance:
 The course of reasoning in experimental designs has become the
basic logic for other research strategies
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52. Classifications …
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53. Classifications …
Examples:
1) The Hawthorne effect is a good example in management
 It refers to the tendency of people to work harder and perform
better when they participate in an experiment
2) Does school feeding improve students’ academic
achievement?
 Dependent variable (academic achievement)
 Independent variable (school feeding)
3) Does training on organizational health and safety reduce
accidents in textile factories?
 Dependent variable (accidents)
 Independent variable (training on OHS)
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54. Classifications …
Strengths
 Controls the influence of confounding variables, providing more
conclusive answers
 Randomization eliminates bias due to pre-randomization
confounding variables
 Blinding the interventions eliminates bias due to unintended
interventions
 Increased internally validity
 Fewer rival hypotheses
Weaknesses
 Costly in time and money
 Many research questions are not suitable for experimental designs
 Usually reserved for more mature research questions that have
already been examined by descriptive studies
 Experiments tend to restrict the scope and narrow the study
question
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55. Classifications …
b) Quasi-experimental design
 Quasi-experimental designs do not use randomized
assignments for comparisons
 It is a mixed design where random and non-random
experiments are employed together
 Usually this happens when the independent variable in
question is something that is an innate characteristic of the
participants involved
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56. Classifications …
 Examples:
1) Does membership to farmer cooperatives increase
farmers’ market participation?
 Outcome: market participation
 Membership decision is affected by other factors and
farmers cannot be randomly assigned as members and
non-members.
2) Members of trade/workers’ union are less likely to
face sexual harassment in textile factories.
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57. Classifications …
Strengths
 Q-E designs are a reasonable alternative to RCT
 Useful where pre-selection and randomization of groups is difficult
 Saves time and resources vs. experimental designs
Weaknesses
 Nonequivalent groups may differ in many ways -- in addition to the
differences between treatment conditions, introducing bias
 Non-blinding allows the possibility of unintended interventions;
blinding can be used in some Q-E studies
 Must document participant characteristics extensively
 Potential biases of the sample must be acknowledged when
reporting findings
 Causal inferences are weakened by the potential for biases vs.
experimental designs
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58. Classifications …
5) Other classifications
 Case Study Design
 Comparative Designs
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59. Classifications …
a) Case Study Design
 The basic case study entails the detailed and intensive
analysis of a single case
 Often a description of a individual case’s condition or
response to an intervention
 can focus on a group, institution, school, community,
family, etc.
 data may be qualitative, quantitative, or both
 Case series: observations of several similar cases are
reported
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60. Classifications …
 The case study design is a very popular and widely used
research design in business research and some of the best-
known studies in business and management research are
based on this design.
 It is a research known for depth than breadth
 Examples:
 Employee loyalty in one organization
 Organizational climate and employee commitment in company
X.
 Financial ratio analysis of company Z.
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61. Classifications …
 Strengths
 Enables understanding of the totality of an individual’s (or
organization, community) experience
 The in-depth examination of a situation or ‘case’ can lead to
discovery of relationships that were not obvious before
 Useful for generating new hypotheses or for describing new
phenomena
 Weaknesses
 No control group
 Prone to selection bias and confounding
 The interaction of environmental and personal characteristics
make it weak in internal validity
 Limited generalizability
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62. Classifications …
2) Comparative design
 entails the study using more or less identical methods of two or
more contrasting cases.
 It embodies the logic of comparison, in that it implies that we can
understand social phenomena better when they are compared in
relation to two or more meaningfully contrasting cases or
situations.
 The comparative design may be realized in the context of either
quantitative or qualitative research.
 There are at least two cases (which may be organizations,
nations, people, etc.) and that data are collected from each,
usually within a cross-sectional design format.
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63. Classifications …
 Seeks to make comparisons between or within groups of
interest
 Often associated with experimental research
 Is there a difference between the control group and the experimental
group?
 Comparison of one group to another on the basis of existing
characteristics
 Examples:
 Do employee motivation packages vary between private and
public banks in Ethiopia?
 Comparison of academic achievement of students in private and
government schools in Mekelle city.
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64. 5. How to choose a research design?
 There will be no one right/best way of conducting
business research- One single design cannot serve the
purpose of all types of research problems. this will
depend on a number of factors .
 Does it adequately test the hypothesis?
 Does it identify & control extraneous factors?
 Are results generalizable?
 Can the hypothesis be rejected or retained via statistical
means?
 Is the design efficient in using available resources?
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65. How to Choose…
 When selecting research design for your study, you
need to:
 Identify the design
 Name it specifically
 Provide a map of the design
 Discuss your rationale for using this design
 Describe threats to the validity of the chosen
design
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66. 6. Problems with study designs
 There are several problems/limitations in selecting
research design
 Inappropriate for the study purpose or the research
framework
 Poorly developed designs
 The research methods were poorly implemented
 Inadequate treatment, sample, or measurement
methods
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67. 7. Research approaches
 There are three research approaches in the social
sciences.
 Quantitative research approach
 Qualitative research approach
 Mixed research approach
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68. Research approaches
a) Quantitative research approach
 Is based on the measurement of quantity or amount.
 Expression of a property or quantity in numerical terms
 Quantitative research helps:
 Precise measurement
 Knowing trends or changes overtime
 Comparing trends or individual cases/ units
 The objective of quantitative research is to develop
and employ mathematical models, theories and
hypotheses pertaining to natural phenomena.
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69. Research approaches
 The process of measurement is central to quantitative
research because it provides the fundamental connection
between empirical observation and mathematical
expression of an attribute.
 This approach typically concentrates on measuring or
counting and involves collecting and analyzing numerical
data and applying statistical tests.
 It is guided by realist ontology and positivist paradigm
 It works more with natural settings and natural sciences
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70. Research approaches
 METHODS: questionnaires, secondary/numeric data,
mathematical and statistical models and equations,
numeric reporting tools such as tables, figures, graphs and
interpretations
 Examples
 Profitability analysis
 Determinants of household economic welfare
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71. Research approaches
2) Qualitative research approach
 Seeks to describe how individuals perceive their own
experiences within a social context
 Emphasizes in-depth, nuanced understanding of human
experience and interactions
 METHODS include in-depth interviews, direct
observations, examining documents, focus groups, case
studies
 Data are often participants’ own words and narrative
summaries of observed behavior
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72. Research approaches
 The goal of qualitative research is to look for
meaning, concepts, definitions, characteristics,
symbols, and descriptions of things.
 It is based on ontological idealism and intepretivist
paradigm
 Works more in the social, humanity and historical
sciences
 Examples:
 Employees’ perception of leadership styles of their
supervisors
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73. Research approaches
Strengths
 Data based on the participants’ own categories of meaning
 Useful for studying a limited number of cases in depth or describing complex
phenomena
 Provides understanding and description of people’s personal experiences of phenomena
 Can describe in rich detail phenomena as they are embedded in local contexts
 The researcher can study dynamic processes (i.e., document sequential patterns/change)
Weaknesses
 Knowledge produced might not generalize to other people or other settings
 It is difficult to make quantitative predictions
 It might have lower credibility with some administrators and commissioners of programs
 Takes more time to collect and analyze the data when compared to quantitative research
 The results are more easily influenced by the researcher’s personal biases and
idiosyncrasies
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74. Research approaches
 Research designs
 Quantitative sampling methods
 Research tools/instruments
 Validity & reliability of the
tools/instruments
 Possible biases
 Minimizing possible errors
 Dealing with narrow but large data
sets
 Statistical methods
 How to play with figures
 How to report statistically
A qualitative researcher must be
familiar with
A quantitative researcher must be familiar
with:
Element/case/unit of analysis
Qualitative sampling methods
Dealing with deep but small data
set
Coding/de-coding/re-coding
techniques
Abstract constructing
How to play with the abstracts
How to report narratively
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75. Research approaches
3) Mixed research approach
 It mixes quantitative and qualitative approaches and
methods.
 Its is based on the assumption that methodological
‘purism’ or ‘monoism’ does not provide complete picture
of social reality.
 It is based on ‘pragmatist’ paradigm.
 ‘whatever works better’ is better in addressing research
questions in the social sciences
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76. Research approaches
 The following questions are raised while using mixed
research approach
 Are the quantitative and qualitative data collected simultaneously
or sequentially?
 Which has priority – the quantitative or the qualitative data?
 What is the function of the integration – for example,
triangulation, explanation, or exploration?
 At what stage(s) in the research process does multi-strategy
research occur? (research question formulation, data
collection, data analysis, or data interpretation)
 Is there more than one data strand?
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77. Research approaches
 Five justifications for combining quantitative and
qualitative research
 Triangulation: convergence, corroboration, correspondence
or results from different methods.
 Complementarity: ‘seeks elaboration, enhancement,
illustration, clarification of the results from one method with
the results from another’
 Development: ‘seeks to use the results from one method to
help develop or inform the other method, where
development is broadly construed to include sampling and
implementation, as well as measurement decisions’
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78. Research approaches
 Five justifications for combining…
 Initiation: ‘seeks the discovery of paradox and
contradiction, new perspectives of [sic]
frameworks, the recasting of questions or results
from one method with questions or results from the
other method’
 Expansion: ‘seeks to extend the breadth and range
of enquiry by using different methods for different
inquiry components’
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79. Types of mixed methods designs
I. Triangulation mixed methods Design
II. Explanatory mixed methods Design
III. Exploratory mixed methods Design
QUAN
Data and Results
QUAN
Data and Results
+
QUAL
Data and Results
QUAL
Data and Results
qual
Data and Results
quan
Data and Results
Interpretation
Follow-up
Building

80. Research approaches
 Outcomes of mixing different methods
 Triangulation
 Offset
 Completeness
 Different research questions
 Explanation
 Unexpected results
 Instrument development
 Credibility
 Illustration
 Confirm and discover
 Diversity of views
 Enhancement
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