The document discusses different research design frameworks, including classical experimental design, cross-sectional design, and quantitative-qualitative design. For classical experimental design, it describes the key elements of having an experimental and control group, manipulating an independent variable, pre-testing and post-testing, and comparing results. Cross-sectional design is more common in social sciences and uses statistical analysis rather than manipulation. Quantitative design seeks to objectively explain social facts while qualitative design understands social phenomena from participant perspectives. The appropriate design depends on the research question, level of control, and type of data.

1. LECTURE THREE
Michael Poku-Boansi, Ph.D.
September, 2012
RESEARCH DESIGN
FRAMEWORKS

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 Research designs are concerned with turning the research
question into a testing/testable project.
 Any research design deals with at least four (4) problems:
INTRODUCTION

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- What questions to study;
- What data are relevant;
- What data should you collect; and
- How to analyze the results.
INTRODUCTION

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 Every research design has its positive and negative sides.
 Research design can be divided into fixed and flexible designs
or in other instances, they are referred to as “Quantitative” and
‘Qualitative” research designs.
INTRODUCTION

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 Fixed designs do not need to be quantitative and flexible
design need not be qualitative.
 In fixed designs the design of the study is fixed before the main
stage of data collection takes place.
INTRODUCTION

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 Fixed designs are normally theory driven; otherwise it is
impossible to know in advance which variables need to be
controlled and measured (often these variables are
quantitative).
 Flexible designs allow for more freedom during data collection.
One reason for using the type of design is that the variable of
interest is not quantitatively measurable (e.g. culture, decision
making process, plan approval process, Case Studies etc)
INTRODUCTION

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 In other cases, theory might not be available before one starts
the research.
 So you can see that there are several classifications and models
of research design but we will deal with the following three
classical ones:
INTRODUCTION

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- Quantitative – Qualitative Design;
- Classical Experimental Design; and
- Cross – Sectional Design
INTRODUCTION

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Introduction
 There are several classifications and models but will deal with
three that are mostly used in planning:
- Classical Experimental Design;
- Cross-Section Design; and
- Quantitative - Qualitative Design.
TYPICAL RESEARCH DESIGNS IN PLANNING

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Key Elements
a)Two comparable groups or situations: experimental and control
groups
b)The experiment group is exposed to an independent variable
(treatment or change agent); control group is not.
CLASSICAL EXPERIMENTAL DESIGN

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c) Cases are randomly selected in both experimental and control
groups
d) Effect of the independent variable on the dependent variable is
measured on 2 occasions.
CLASSICAL EXPERIMENTAL DESIGN

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a) Pre-test - before introduction of change
agent/intervention
b) Post-test - after introduction or injection of
change/intervention .
CLASSICAL EXPERIMENTAL DESIGN

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e)Differences between the two measurements are compared and
inferences made under some critical assumptions. What are
they?
f) If the difference in the experimental group is significantly larger
than the control group, it is inferred that the independent
variable is causally related to the dependent variable
CLASSICAL EXPERIMENTAL DESIGN

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Groups Pre-test Post-test Difference
Experimental Qi x Q2 Q2-Q1 = de
Control Q3 Q4 Q4-Q3 = dc
DIAGRAMMATIC DESIGN

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Why Experimental Designs?
Experiments are more associated with natural sciences -
physical and biological/natural sciences.
Why experimental designs in Social Sciences?

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Two (2) reasons:
a)Helps us to understand the logic of all research designs, the
model against which we can evaluate other designs.
b)Allows the investigator to draw causal inferences, and
establish whether or not the independent variables caused
changes in the dependent variable.

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Experimental design is used less frequently by social scientists
primarily because of its rigid structure which often cannot
easily be adapted to the social sciences

18. KEY COMPONENTS OF EXPERIMENTAL DESIGN
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a) Comparison – Normal
- Necessary to establish that two variables are correlated or
causally related.
- E.g. compare behaviour of two groups of youth who smoke
and those who do not or socio economic conditions along a
rehabilitated road corridor and a deteriorated road corridor.
- Both internal (time-wise) and external comparison.

19. KEY COMPONENTS OF EXPERIMENTAL DESIGN
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b) Manipulation
- Helps to establish time-order of events
- Manipulate X and Y is disturbed or also changed.
- Can then say that change in Y is caused by change in X

20. KEY COMPONENTS OF EXPERIMENTAL DESIGN
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b) Manipulation
- E.g. if drug abuse among the youth causes indiscipline,
then
it follows that the use of drugs precedes the expression of
indecent behaviour.
- It is important to note that change occurs after the
activation or injection of independent variable.

21. KEY COMPONENTS OF EXPERIMENTAL DESIGN
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c) Control - Internal Validity
- Third criterion of causality requires that rival explanations
are ruled out.
- Enables us to determine that the observed variation is non-
spurious.
- Non-spurious relation is a relationship between two (2)
variables that cannot be explained by a third variable.

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- So control means → the operation that enables the
researcher to rule out other factors as rival explanations of
the observed association between variables.
→ this issue of internal validity
- To establish internal validity, the researcher must answer
the question that the independent variable caused the
changes in dependent variable.

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- Factors that may affect Internal Validity are:
a) Extrinsic factors - those occurring prior to the research
operation; and
b) Intrinsic factors - affect the results during the study
period.

24. KEY COMPONENTS OF EXPERIMENTAL DESIGN
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d) Generalisation: External Validity.
- The extent to which the research findings can be
generalised to larger populations and applied to different
social and political settings.

25. KEY COMPONENTS OF EXPERIMENTAL DESIGN
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- Two (2) conditions must be satisfied
a) Representativeness of the sample
b) Minimisation of reactive elements - reducing artificial
disturbances in the natural setting.

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Related research approach
a)Explanatory research - desire to establish causality - e.g.
epidemiology - causes of morbidity; infant mortality.
b) Impact Studies:
- Establishing the degree of change resulting from the
injection of change variable

27. KEY COMPONENTS OF EXPERIMENTAL DESIGN
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- Trying to establish the explanatory variables ‘with’ and
‘without’ and ‘Before’ and ‘After’
E.g. Agric experiments, Micro-finance, community
participation

28. CROSS - SECTIONAL DESIGNS (CSD)
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Introduction
a)The most predominant design used in social sciences.
- Often identified with survey research - a method of data
collection and analysis common in social science studies.
- Research mostly aimed at describing the pattern of
relations before any attempt at casual inference is
made

29. CROSS - SECTIONAL DESIGNS (CSD)
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b) Comparison with Experimental Design.
E.g. The effect of gender/literacy/alcoholism on communal
violence.
- Researcher cannot
a) Manipulate the independent variable
b) Cannot incorporate control (time element of change is
not possible)

30. CROSS - SECTIONAL DESIGNS (CSD)
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c)To overcome methodological limitations associated with CSDs,
the researcher uses statistical analysis to approximate some of the
operations that are in-built in Experimental design.
- Statistical analysis is used to show the interrelationship
- Using statistics to organise describe, and summarise
observations.

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→ E.g. Cross-tabulation or bivariate percentage analysis (20%
women, 40% men are involved in communal violence).
- Though this statistical analysis will give us the behaviour of
men and women, we cannot:
a) Establish causality between the variables
b) We cannot explain why?

32. CROSS - SECTIONAL DESIGNS (CSD)
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- When Cross - Sectional Designs (CSD) are used, explanatory
factors are statistically controlled unlike experimental
design.
- In Cross - Sectional Designs (CSD), multivariate methods of
statistical analysis are used as alternatives to experimental
methods of control and drawing of inferences e.g.
elaboration by cross-tabulation, multiple regression etc

33. CROSS - SECTIONAL DESIGNS (CSD)
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Main advantage:
a)can be carried out in natural settings and permit researchers to
employ random probability samples.
b) This allows statistical inferences to broader populations and
permits generalisation of findings to real life situations and
thereby increasing external validity.

34. CROSS - SECTIONAL DESIGNS (CSD)
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 Disadvantage:
- Lack of adequate control over rival explanations - internal
validity.
 Related Research Approaches
Panel (Longitudinal) Research
- A vigorous solutions to the time dilemma or problem in
cross-sectional studies

35. CROSS - SECTIONAL DESIGNS (CSD)
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Designed to collect time series data from the same sample -
collect data before injection of the change agent and then after
E.g. yearly E.g. snapshot and Video - A snapshot is like single
case study whiles a Panel Study is like a video

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Advantages
a)A good way of studying trends - behaviour, population etc
b) Measures changes with high degree precision
- Better than changing samples

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Problems
a)The likelihood of flagging commitment of respondents
- Affects representativeness of sample
b) Panel conditioning
- Respondents giving a given set of answers to please
researcher.

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Example: Case Study
Definition:
- It’s an empirical enquiry that allows the researcher to
investigate and understand the dynamics of a particular
system

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- It has 3 attributes:
a) Investigates a contemporary phenomenon within its
real-
life context.
b) When the boundaries between phenomenon and
context are not clearly evident.
c) Multiple sources of evidence are used.

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Case Study is preferred when:
a) “how” and “why” questions are being asked.
b) the researcher has little control over events
c) the focus is on contemporary phenomenon within a
real life context.

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Relevant to Planning:
- It enables the researcher to learn from practice in
order to inform the theory on which that practice is based.

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Choice of Appropriate Design
- Selection of appropriate research design or strategy
depends
on 6 conditions:
a) The type of research question.
b) The control an investigator has over actual behavioural
events

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c) The focus on contemporary as opposed to historical
phenomena
d) Purpose of research
e) Time available
f) Type of data involved.

44. QUANTITATIVE (Q1) AND QUALITATIVE (Q2) APPROACHES
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General Definitional Difference
Qi - Behaviour can be explained by objective facts
Q2 - There are multiple realities that are socially defined.

45. QUANTITATIVE (Q1) AND QUALITATIVE (Q2) APPROACHES
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The 2 are further compared in four ways:
- Assumptions of the world
Qi - There are social facts with objective reality.
Q2 - Reality is socially constructed through individual or
collective definition of the situation

46. QUANTITATIVE (Q1) AND QUALITATIVE (Q2) APPROACHES
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Purpose
Qi - Explains the causes of change of social facts through
objective measurements and quantitative analysis
Q2 - Concerned with understanding of social phenomenon
from actors perspectives through participation

47. QUANTITATIVE (Q1) AND QUALITATIVE (Q2) APPROACHES
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Approach-focus of research
Qi - Often employs experimental and correlational designs
to
reduce error - people are objects of investigation.
Q2 - Ethnographic description of process

48. QUANTITATIVE (Q1) AND QUALITATIVE (Q2) APPROACHES
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Choice of Appropriate Design
Researchers role
Qi - Detached to avoid bias
Q2 - Immersed in the phenomenon of interest

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Practical level
i. Nature of variables
Qi - Quantifiable e.g. income, output etc
Q2 - Perceptional variables reflecting attitudes, preferences
and priorities.

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ii. Interviews format
Qi - Structured, formal, pre-designed questionnaire
Q2 - Open-end, semi-structured, interactive

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iii. Sampling
Qi - Probability Sampling
Q2 - Purposive Sampling

52. MEASUREMENTS
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Introduction
The concept of ISOMORPHISM: concerns how measurement
instruments relate to the reality being measured. The
researcher is always confronted with the need to:
a) Search for an already developed measure and reported in
literature;

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b)Develop a new or original measure to convert empirical
observations in relation to the research problem; and
C) Provide evidence that the measures used are valid and
reliable.

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Nature of Measurement
Definition: “measurement is a procedure in which a researcher
assigns numerals - numbers or other symbols - to empirical
properties (or variables) according to rules”
Example: Job applicant develops a rating system to help him to
select the most appropriate job offer

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 Nature of Measurement
→ Scale: 1, 2, 3, 4
Criteria: Job satisfaction, Salary, Location Jobs obtained:
Teaching, NGO, public service.

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 Basic concepts in Measurements
The basic concepts used in defining measurement include
Numerals, Assignments and Rules.
a) Numerals
- A numeral has no quantitative meaning unless you give it
such a meaning e.g. I, II, III.

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- It can be used to identify phenomena, objects or persons
and thus can be used to designate months, books, streets,
etc.
- Numerals that are given quantitative meaning become
numbers that the researchers can use to explain, describe
and predict phenomenon.

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b)Assignment - in definition of measurement, it means
“mapping”. Numerals or numbers are mapped onto objects or
events:

59. MEASUREMENTS
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c)Rules - It specifies the procedure a researcher used to assign
numerals or numbers to objects or events.
E.g. Assign numerals 1 to 5 to a level of impact or satisfaction
with a Job.

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→ Rules are the most significant component of the
measurement procedure because they determine the
quality of measurement.
→ Poor rules make measurements meaningless.
→ Measurement is assignment of numerals or numbers to
objects, events or variables according to rules.

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Levels of Measurement
- Scientist distinguish between different ways of measuring:
Levels or scale of measurement
- 4 levels or scales or “Measurements”

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a)Nominal Scale
- It is the lowest and simplest level.
- Numbers or other symbols are used to classify objects or
observations into a number of categories.
- These numbers only constitute a nominal or classificatory
scale.

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- E.g. - classification of population into sex (1,2), religions
(1, 2, 3), ethnicity (1, 2, 3, 5, 5), level of education.
- Mathematically, the basic property of the nominal scale is
that the properties of objects in one category are designated
as identical for all its cases.
.

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b) Ordinal scale
- Unlike nominal, it brings in the question of relationship
between variables (order).
E.g. higher, greater, lower etc.
- It allows for ranking of variables in either ascending or
descending. E.g. Highly Favourable, Favourable, Not
Favourable, etc.
.

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c) Interval Scale
- Apart from being able to rank a set of observations, it also
gives the exact distance between each of the observations
and
the distance is constant.
- It is characterised by a common and constant unit of
measurement that assigns real numbers to all (pairs of) the
objects in the ordered set.

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- Income, exams results, weight, output, population size, age,
mileage etc.

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d) Ratio Scale
- Variables that have natural Zero-point can be measured on a
ratio scale. E.g. freezing point of water
- Examples are weight, time and length.
Interval scale - the difference of amount measured is applied
to an arbitrary point → whereas with Ratio, it is applied to
an
obsolete zero-point

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- General Principle: Properties that can be measured at higher
level can also be measured at lower levels but not vice versa.
- E.g. - If a variable can be measured at Ratio level, it can also
be measured at nominal level.

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Measurement Error
Errors are associated with measurements
- Definition: differences in measurement scores that are due
to
anything other than real differences.

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Reasons
1.The score obtained maybe influenced by an associated
attribute, an attribute the researcher did not intend to
measure. E.g. unfairness in judgement about an event - football
2. Differences in temporal situation - e.g. health, mood of the
person.

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Reasons
3.Differences in the settings in which the measure is used e.g. –
age, probability, gender.
4. Difference in the administration of the measuring instrument –
e.g. poor lighting, tiredness of interviewer etc.

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Reasons
5.Difference in processing - wrong or inconsistent coding of
responses
6.When different people interpret the measuring instrument
differently.

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Validity
- This is concerned with: “Am I measuring what 1 intend to
measure?”.

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- This comes about because many measurements in the Social
Science are indirect. Under this situation, researchers are
never fully certain that they are measuring the variable for
which they designed their measurement operation. E.g. does
voter turnover really measure democracy? Community
preference - various types
e.g. content relevance to characteristics
Empirical: Prediction of results and same
Construct etc: Relation to thematic framework

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Reliability
Reliability is a central concern to the social Scientist because the
instruments used are rarely valid - i.e. problem free.
It refers to the extent to which a measuring instrument contains
variable errors - that is errors that appear inconsistent from
observation to observation during any one measurement attempt
or that vary each time measurement is taken.

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E.g. if you use a ruler to measure 2 points in time and there is a
difference, then the ruler contains a variable error.
 Errors are common because the variables in Social Science are
indirect or qualitative. - e.g. momentary distraction,
ambiguous construction etc.

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 Each measurement consists of two Parts: true or error
components
Reliability: the ratio of the true-score variance to the total
variable in the score as measured.

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1.Rating
According to Gyedu et al (1999), the rating scale is a device
used in evaluating products, attitude or other characteristics
of
variables. Mugenda and Mugenda (1999) also define it as a
scale
used to measure perception, attitude, values and behaviour.

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Con’t
Rating scales are type of scales that consists of numbers and
descriptions which are use to rate or rank the subjectivity and
intangible components in research. Rating scales are limited by
characteristics of human memory, sensitivity and accuracy of
observation. Essentially, there are three types of rating scales,
namely numeral scales, graphic scales and forced choice scale.

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i Numeral Rating Scale
This method measure characteristic by assigning numbers to
the specific rating categories. For example rating a teacher’s
performance along a scale of 1 to 10. It is very easy to construct
and the length of the scale or number of points along the scale is
an arbitrary decision.

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Con’t
A disadvantage of this scale is that, individual respondents
have different frame for referencing which may affect rater
reliability or consistency, that is, a rating of seven of an
object by a person may be equal to a rating of five by another
person on the same scale.

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ii Graphic Rating Scale
Is similar to numerical scale except that the scale itself
contains words rather than numbers. It has an objective and a
straight line with rating categories positioned along the line.

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iii Forced Choice Rating Scale
It is a scale that presents the respondents with a series of
choices and requires him to choose one over the others. It is
much more complex to develop and score than the numerical
and graphical scale. Example can be as follows:

85. TYPES OF SCALE
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The teacher is
{ } Always on time for class
{ } Pleasant in class
{ } Very sincere when talking with students
{ } Well read

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This method provides an overall ranking to the factor being
studied without asking the respondents to rank order. This
method has two advantages:
It overcomes the rater’s tendency to leniency because he is
forced to make some kind of judgements; and
The distribution of ratings is much more realistic than some
other rating scale distribution.

87. TYPES OF SCALE
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2. Thurstone Scale
Thurstone is one of the first and most productive scaling
theorists. He actually invented three different methods for
scaling a uni-dimensional scale: the method of equal appearing
intervals, the method of successive intervals and the method of
paired comparisons. The three methods differed in how the
scale values for items were constructed, but in all three cases,
the resulting scale was rated the same way by respondents.

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It was used in measuring core attitude when you have multiple dimensions
or concerns around that attitude.
In Thurstone scaling, the researcher would obtain a panel of judges (say
100 of them) and then dream up every conceivable question you can ask
about a phenomenon.
By administering the questionnaire to the panel, the researcher can analyse
inter-item agreement among judges and then even use the discrimination
index to weed out what are called the non-homogenous items.
It is based on the premise that, scaling is all about homogeneity, a term
sometimes used as synonymous with being one-dimensional.

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3. Likert Scale
They were developed in 1932 as the familiar five-point bipolar
response format most people are familiar with today. It is a
rating scale used in measuring the strength of agreement with a
clear statement.
Likert scaling is a one-dimensional scaling which usually ask
people how they agree or disagree, approve or disapprove,
believe to be true or false.

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4. Guttmann’s Scale
It was developed in the 1940s and is a technique of mixing
questions up in the sequence they are asked so that respondents
don’t see that several questions are related.
It is sometime known as cumulative scaling or scalogram
analysis.