Research intro, sampling, scales, error & validity

Part I
Introduction to research, Types of
Research, Sampling, Measurement
Scales and Errors

Session Plan
• Introduction to research
• Sampling
– Plan
– Frame
– Types
• Scaling Techniques
• Errors
– Sampling
– Non-Sampling
• Editing, Tabulating and Data Validation

Research is Search of
Creation of new knowledge
Examining the validity of
existing knowledge
Knowledge
SEARCH OF KNOWLEDGE BY SYSTEMATIC
METHODS

Theory Building for Research
• A theory is a simplified representation of a limited
part of reality.
• In a theory, a researcher is just trying to provide a
representation of just some part of a real world
phenomenon.
• Theory is a human effort to provide a representation
of the reality, using human language
• Theory, Theoretical model or model
• Ex. of theoretical model

CROI
Service
Excellence
Search Con.
Evaluation Con.
Transaction Con.
Post-Purchase
Con
Pleasure
Escapism
Consumer Trait
Product
Characteristics
Access Con.
Usefulness
Convenience
Enjoyment Trust in Online
Shopping Previous OLS Exp.
Attitude
towards
online
buying
Intention to
purchase
online
Theoretical Model

REVIEW OF LITERATURE
Review the concepts
and Theories
Review Previous
Research Findings
RESEARCH DESIGN STRATEGY
Data Collection
Design Sampling Design
Instrument Development and Pilot Testing
DATA COLLECTION, EDITING AND
TABULATION
DATA ANALYSIS AND INTERPRETATION
REPORT WRITING AND
PRESENTATION
INTRODUCTION TO THE PROBLEM
Formulating Hypothesis
8Course Instructor - Dr. Parikshit Joshi

RESEARCH DESIGN
Course Instructor - Dr. Parikshit Joshi 9

Research Design
• A plan for gathering data for answering specific
research questions.
• Constitutes the blueprint for the collection,
measurement and analysis of data
• Essentials of Research Design:
– An activity and time based plan
– A plan based on research question
– A guide for selecting source and type of information
– A framework for specifying the relationship among
the study variables

RD answer the following FAQ’s related
to study:
• What the study is 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 will be the time period of the study?
• What will be the sample design?
• What techniques of data collection will be used?

Research
Design
Sampling
Design
Observational
Design
Statistical
Design
Operational
Design

Features of a Good Research Design
• A good design is often characterized by
adjectives like flexible, appropriate, efficient
and economical.
• Generally, the design which minimizes bias
and maximizes the reliability of the data
collected and analyzed is considered a good
design.
• The design which gives the smallest
experimental error is supposed to be the best
design in many investigations.

• Similarly, a design which yields maximum
information and provides an opportunity for
considering many different aspects of a
problem is considered most appropriate and
efficient design in respect of many research
problems.
• Thus, the question of good design is related to
the purpose or objective of the research
problem and also with the nature of the
problem to be studied.

• A design may be quite suitable in one case,
but may be found wanting in one respect or
the other in the context of some other
research problem.
• One single design cannot serve the purpose of
all types of research problems.

Types of Research Design
• Exploratory Research Design
• Descriptive Research Design (Diagnostic
Research Design)
• Causal Research Design (Experimental
Research Design)

Exploratory Research Design
• Also known as formulative research studies, as they
are helpful in formulating the problem or developing
working hypothesis.
• Conducted when problem is not clear or subject is
new.
• Exploration is particularly useful when researchers
lack a clear idea of the problems they will meet
during the study.
• Through exploration researchers develop concepts
more clearly, establishes priorities, develop
operational definitions, and improves final research
design.

• Can be performed using a literature search,
surveying certain people about their experience,
focus group and case studies.
• When surveying people, exploratory research
studies would not try to acquire a representative
sample, but, rather seek to interview those who
are knowledgeable and who might be able to
provide insights concerning the relationships
among the variables.
• Non-Probability sampling methods are usually
used for sample selection. (Judgmental sampling)

Descriptive Research Design
• Descriptive research studies are those studies
which are concerned with describing the
characteristics of a particular individual, or of a
group.
• Most of the social research comes under this
category.
• In descriptive studies, the researcher must be
able to define clearly, what he wants to measure
and must find adequate methods for measuring
it along with a clear cut definition of
‘population’ he wants to study.

• The design in such studies must be rigid and not
flexible and must focus attention on the following:
a. Formulating the objective of the study (what the
study is about and why is it being made?)
b. Designing the methods of data collection (what
techniques of gathering data will be adopted?)
c. Selecting the sample (how much material will be
needed?
d. Collecting the data (where can the required data
be found and with what time period should the
data be related?)
e. Processing and analyzing the data.
f. Reporting the findings.

Causal Research Design
• Also known as experimental studies
• Determine cause and effect relationships
• Manipulation of one or more independent
variables
• Basic Principles of Causal Research
• Principle of Replication – Experiment should be
repeated more than once.
• Principle of Randomization – Provide protection against
external factors

Types of Experimental Design
i. Before-and-after without control design.
ii. After-only with control design.
iii. Before-and-after with control design.

Before-and-after without control
design

After-only with control design

Before-and-after with control design

SAMPLING THEORY……..

Sampling: Introduction
• One taste from a drink tells us whether it is
sweet or sour
• The basic idea of sampling is that by selecting
some of the elements in a population, we may
draw conclusions about the entire population
• A population element is the individual
participant or object on which the
measurement is taken. It is the unit of the
study

Sampling contd……
• All the items in any field of inquiry constitutes a
Universe or Population
• A census involves a complete count of every
individual member of the population of interest.
• Ex. If 4000 files defines a population, a census
would obtain
• We call the listing of all population elements
from which the sample will be drawn the
sampling frame

Why Sample [Objectives of Sampling]
• There are several compelling reasons for
sampling:
– Lower cost
– Grater Accuracy of Result: with few respondents
better supervision, more thorough investigation
and better processing is possible
– Grater speed of data collection
– Availability of population elements

Why Census
• Two conditions are necessary for a census
study:
1. Feasible – when the population is small and
variable, any sample we draw may not be
representative from the population from
which it is drawn.
2. Necessary – when the elements are quite
different from each other (Heterogeneous
Population)

Good Sample
• The ultimate test of a sample design is how well
it represents the characteristics of the
population it purports to represent
• In measurement terms, sample must be valid
• Validity of sample depends on two
considerations:
– Accuracy : the degree to which biasness is absent
from the sample
– Precision: How closely the sample represents the
population

Characteristics of a Good Sample
• It must be representative
• Sampling errors should be minimum
• Optimum use of funds
• Biasness should be absent
• Sample should be such that the result of the
sample study can be applied, in general, for
the universe.

Sampling Design for Surveys
Motivation – Online Shopping Behavior of
people of Tier 3 cities of India.
• Target Population: A complete collection of
objects whose description is major goal of study.
– All online buyers of Tier 3 cities of India.
• Sample: A subset of Target Population.
• Sampled Population: The population from which
sample is actually selected.
– Online buyers of Bareilly, Rampur, Moradabad.

Sampling Design for Surveys
• Perfect Survey
Target Population = Sampled Population
• Observation Unit: The object upon which data are
collected.
• Sampling Unit: The object that is actually sampled.
• Sampling Frame: List of sampling units.

Bareilly
Mbd.
Rampur
Amroha
Etawah
Roorkee
Vellore
Salem
Raipur
Haldwani
Kashipur
Bhadoi
U

Bareilly
Mbd.
Rampur
Amroha
Etawah
Roorkee
Vellore
Salem
Raipur
Haldwani
Kashipur
Bhadoi
U
Sample

Bareilly
Mbd.
Rampur
Amroha
Etawah
Roorkee
Vellore
Salem
Raipur
Haldwani
Kashipur
Bhadoi
U
All online buyers of Bareilly makes one sampling unit
Sample

Bareilly
Mbd.
Rampur
Amroha
Etawah
Roorkee
Vellore
Salem
Raipur
Haldwani
Kashipur
Bhadoi
U
Sampled Population
All online buyers of Bareilly makes one sampling unit
Sample

Sampling Technique
Sampling Technique
ProbabilityNon – Probability
Convenie
nce
Sampling
Judgmen
tal
Sampling
Quota
Sampling
Snowball
Sampling
Cluster
Sampling
Stratified
Sampling
Systematic
Sampling
Simple
Random
Sampling

Non – Probability Sampling
• Sampling technique that do not use chance
selection procedures.
• They rely on the personal judgment /
experience of the researcher.
• Types :
– Convenience
– Judgmental
– Quota
– Snowball

Convenience
• A non probability sampling technique that
attempts to obtain a sample of convenient
elements.
• The selection of sampling units is left primarily
to the interviewer.

Judgmental Sampling
• A form of Non Probability sampling in which the
population elements are purposely selected
based on the judgment / experience of the
researcher.
• Ex. using student volunteers as subjects for the
research.
• In pilot studies, convenience sample is usually
used because it allows the researcher to obtain
basic data and trends regarding his study without
the complications of using a randomized sample.

Quota Sampling
• A non probability sampling technique that is a
two stage restricted judgmental sampling.
• The first stage consist of developing control
categories or quotas of population elements.
• In the second stage, sample elements are
selected based on convenience or judgment.
• If you found out the larger population is 40% women and
60% men, you would need a sample of 40 women and 60 men
for a total of 100 respondents. You would start sampling and
continue until you got those proportions and then you would
stop. So, if you’ve already got 40 women for the sample, but
not 60 men, you would continue to sample men and discard
any legitimate women respondents that came along

Snowball Sampling
• A non probability sampling technique in which
an initial group of respondents is selected
randomly.
• Subsequent respondents are selected based
on the referrals or information provided by
the initial respondents.
• This process may be carried out in waves by
obtaining referrals from referrals.

PROBABILITY SAMPLING METHODS
Course Instructor - Dr. Parikshit Joshi 47

Simple Random Sampling (SRS)
• A probability sampling technique in which
each element in the population has a known
and equal probability of selection.
• Every element is selected independently of
every other element and the sample is drawn
by a random procedure from a sampling
frame.
• Ex. The population might be all hospitals in
Noida that perform heart bypass surgery.

Systematic Sampling
• A probability sampling technique in which the
sample is chosen by selecting a random
starting point and then picking every ith
element in succession from the sampling
frame.

• Steps in Systematic Sampling:
– Prepare a sampling frame
– Identify skip interval ‘K’
Population size
K = --------------------------
Sample size
– Identify the random start: By lottery method pick
out the starting number
– Draw the sample by choosing every kth element

Stratified Sampling
• This method is useful when the population
consist of a number of heterogeneous
subpopulation and the members within a
given subpopulation are relatively
homogeneous.
• Steps in Stratifies Sampling:
– Population of interest is subdivided into
subpopulation, on the basis of their age, gender,
income and so on, called strata.

• From each strata a sub sample is drawn in
proportion to its size.
• For drawing sample from sub population use
simple random sampling
• 10% [Pharmacy+ Management+ Engineering+ Applied Science] = Desired Sample
AMITY UNIVERSITY
Pharmacy
Engineering Applied Science
Management

Stratified Sampling
UniverseSample

Cluster Sampling (Area Sampling
Method)
• This method is useful when population consist
of a large number of similar groups which are
geographically distant.
• One-stage sampling. All of the elements
within selected clusters are included in the
sample.
• Two-stage sampling. A subset of elements
within selected clusters are randomly selected
for inclusion in the sample.

Cluster Sampling
CLUSTER 1
CLUSTER 2
CLUSTER 3
CLUSTER 4
Sample
Universe

When to Use Cluster Sampling
• Cluster sampling should be used only when it
is economically justified - when reduced costs
can be used to overcome losses in precision.
• This is most likely to occur in the following
situations.
– Constructing a complete list of population
elements is difficult, costly, or impossible.
• For example, it may not be possible to list all of the
customers of a chain of hardware stores.

• The population is concentrated in "natural" clusters
(city blocks, schools, hospitals, etc.). For example, to
conduct personal interviews of operating room nurses,
it might make sense to randomly select a sample of
hospitals (stage 1 of cluster sampling) and then
interview all of the operating room nurses at that
hospital. Using cluster sampling, the interviewer could
conduct many interviews in a single day at a single
hospital. Simple random sampling, in contrast, might
require the interviewer to spend all day traveling to
conduct a single interview at a single hospital.

Steps in sampling design
i. Type of Universe/Population –
 Universe is finite or infinite
ii. Sampling Unit –
– Sampling unit may be a geographical area such
as state, district, village, factory/industry
premises (various subsections)
iii. Source List – Sampling frame

iv. Size of sample :
– Refers to number of items to be selected from the
universe
– The sample size should neither be excessively
large nor too small. It should be optimum.
– Optimum size is one which fulfills the
requirements of efficiency, effectiveness,
reliability and flexibility
– Factors deciding sampling size: Accuracy,
Precision, Parameters of interest

• Asking participants to recruit further participants by
word-of-mouth is what type of sampling?
– Quota Snowball Stratified random Cluster
• If groups of participants are selected to represent
sub-groups in the population (e.g. such as selecting
an entire class of psychology students to be
compared to a group of history students), this is
known as…
– Simple Random Cluster Quota Systematic

SAMPLING ERROR

Population
[Frame Error]
Sampling
Frame
[Chance
Error]
Sample
[Response
Error]
Response

• Sampling Error = Frame Error + Chance Error
+ Response Error
• The magnitude of the sampling error depends
on the nature of universe; the more
homogeneous the universe, the smaller the
sampling error
• Sampling error is inversely proportional to size
of sample

Parikshit Joshi
Measurement Scales
66

Parikshit Joshi
Measurement Scales
• The researcher cannot identify variation unless it
can be measured.
• Measurement is important in accurately
representing the concepts of interest and is
instrument in the selection of the appropriate
method of analysis.
• Data can be classified into one of two categories:
– Non-metric (Qualitative)
– Metric (Quantitative)
67

Parikshit Joshi
Non-Metric Measurement Scale
• Non-metric data describe differences in type of
kind by indicating the presence or absence of a
characteristic or property.
• These properties are discrete in that by having a
particular feature, all other features are excluded.
• E.g. If a person is male, he cannot be female.
• Types:
– Nominal Scale
– Ordinal Scale
68

Parikshit Joshi
• Nominal Scale
– assigns numbers as a way to label or identify
subjects or objects
– numbers assigned have no quantitative meaning
beyond indicating the presence or absence of the
attribute or characteristic under observation
– also known as categorical scale
– E.g.
• Demographic attribute - gender, religion, occupation
• Behaviour – voting behaviour, purchase behaviour
• Any other action that is discrete – happens or not
69

Parikshit Joshi
• Ordinal Scale
– next higher level of measurement “precision”
– variables can be ordered or ranked in the relation
to the amount of the attribute possessed
– Scale provides no measure of the actual amount
or magnitude in absolute terms, only the order of
values
– the researcher knows the order but not the
amount of difference between the values
– E.g. Measuring satisfaction:
• Highly Satisfied Satisfied Not at all satisfied
70

Parikshit Joshi
Metric Measurement Scale
• Metric data are used when subjects differ in
amount or degree on a particular attribute.
• Metrically measured variables reflect relative
quantity or degree and are appropriate for
attributes involving amount or magnitude
• Provides the highest level of measurement
precision, permitting nearly any mathematical
operation to be performed.
• Types:
– Interval Scale Scale (SPSS)
– Ratio Scale
71

Parikshit Joshi
• Interval Scale
– Interval scales provide information about order,
and also possess equal intervals.
– the distance between 1 and 2 was the same as
that between 2 and 3
– It use an arbitrary zero point
– E.g. temperature scale (Fahrenheit and Celsius)
72

Parikshit Joshi
• Ratio Scale
– It has an absolute zero point (a point where none
of the quality being measured exists)
– Using a ratio scale permits comparisons such as
being twice as high, or one-half as much
– E.g.
• a response of 24 milliseconds is twice as fast as a
response time of 48 milliseconds
• 100 kg. is twice as heavy as 50kg.
73

Exercise – Measurement Scale

• The order in which participants complete a task is
an example of what level of measurement?
– Nominal Ordinal Ratio Interval
• What level of measurement would be used if
participants were asked to choose their favourite
picture from a set of six?
• Nominal Ordinal Ratio Interval
• What is the difference between data measured on
an interval scale and data measured on a ratio
scale?
– An interval scale has a true zero point, so zero on the scale corresponds to zero of the concept
being measured.
– A ratio scale has equal intervals between the points on the scale, whereas an interval scale does
not.
– A ratio scale has a true zero point, so zero on the scale corresponds to zero of the concept
being measured.
– A ratio scale puts scores into categories, while an interval scale measures on a continuous scale.

Editing, Tabulating & Data Validation

DATA EDITING AND CODING
Dr. Parikshit Joshi

Introduction
 Editing is the process of checking data for errors such
as omissions, illegibility and inconsistency, and
correcting data where and when the need arises
 Example 1: A questionnaire meant to be answered by
adults over the age of 30 years has also been answered
by some persons under the age of 30 years
 Example 2: A respondent gives her birthday as 1865 or
claims to have a car insurance but says she doesn‘t
own a car

Field Editing and In-House Editing
Field Editing is a prelimary form of data editing
which is undertaken by the field supervisor on
the day of the interview with a view to finding
omissions, checking the legibility of handwriting,
and clarifying responses by respondents that are
logically or conceptually inconsistent
In-House Editing is a form of data editing which is
more rigorous than field editing in nature, and
which is performed by a centralized office staff

Data Consistency and Completeness
The data obtained from a questionnaire must
be logically consistent, especially when
questions are related
Sometimes inconsistency of data may not be
readily apparent. In this case, the data editor
must judge what action to take (example:
Salary of the CEO of a big corporation is given
as INR 25,000 per annum)

Non-Responses and Out-Of-Order
Answers
 Often, questions are left unanswered by
respondents (Item Non-Response). In such
cases, where data must be inserted, the data
editor has some options such as using a plug
value according to some prespecified rule
 Sometimes respondents give answers to
(open-ended) questions in other questions. In
such cases, data has to be shifted around the
questions

Some Observations on Editing
Editing of data should be done wih a coloured pencil
and the original data must not be erased in case it is
required for future reference
Data editing should be conducted systematically on
the basis of procedures made by professionals
Data editing should be included in the pretest phase
of a questionnaire, in order to improve the quality of
a questionnaire
Data editing has drawbacks, such as, the editor does
not possess the required level of intelligence,
experience and objectivity

Data Coding
Data Coding follows data editing and is the means
by which data can be converted into a format
that enables its processing and analysis by the
computer
Coding involves assigning numbers or other
symbols to answer so that the responses can be
grouped into a limited numbers of categories.
Data Coding incorporates a number of technical
steps and can be a tedious proceess
• For details of data coding process, refer : Business
Research Methods by Donald S. Cooper, pp. 443 .

Data Coding contd…..
• Codebook Construction: contains each
variable in the study and specifies the
application of coding rules to the variable.

Data Classification
• Large volume of raw data must be reduced
into homogeneous groups
• Data having common characteristics are
placed in one class
• Entire data get divided into a number of
classes
– Classification according to attribute
– Classification according to class-intervals (Tally)

Tabulation
• When a mass of data is assembled, it becomes
necessary to arrange them in some kind of
logical sequence or order.
• Process of summarizing raw data and displaying
it in compact form i.e. in the form of statistical
tables
• Importance
– Less space and reduces explanatory and descriptive
statement to a minimum
– It facilitates the process of comparison
– Facilitates summation of items and detection of
errors

Principles of tabulation
• Every table should have a clear, concise and
adequate title
• Every table should have a distinct number to
facilitate easy reference
• Explanatory footnotes
• Columns are separated with lines
• Those columns whose data are to be compared
should be kept side by side
• Abbreviations should be avoided to the extent
possible
• Total of rows and columns should be placed at
right and bottom respectively

Steps involve in Data Analysis
Very
Limited
Extent
Limited
Extent
Good Extent Very Good
Extent
To what extent organisation
set your Time bound targets.
To what extent goals set by
organisation are challenging
& realistic.

Very Limited
Extent
Limited
Extent
Good
Extent
Very Good
Extent
Total
Managers 2 (4.17%) 13
(27.08%)
21 (43.75%) 12 (25%) 48
Employees 46 (15.33%) 97 (32.33%) 139 (46.33%) 18 (6%) 300
Total 48 110 160 30 348
Table 5.1 Extent to which time bound targets are set.

0.00% 10.00% 20.00% 30.00% 40.00% 50.00%
Managers
E mployees
Very Limited E xtent Limited E xtent Good E xtent Very Good E xtent
Figure 5.1 Extent to which time bound targets are set.

• Interpretation – Both managers and
employees have almost same opinion
regarding the extent to which the organisation
set their time bound targets. Considering good
and very good extent 68.75% of managers are
in strong favor that time bound targets are set
in Mathura Refinery whereas 52.33%
employees support their opinion. Combining
first two columns 31.25% managers give their
opinion that upto limited extent their time
bound targets are set and 47.66% employees
support their outlook.

VARIOUS KINDS OF CHARTS AND
DIAGRAMS

Day Chocolate Strawberry White
Monday 53 78 126
Tuesday 72 97 87
Wednesday 112 73 86
Thursday 33 78 143
Friday 76 47 162
The cafeteria wanted to collect data on how
flavored much milk was sold in 1 week. The table
below shows the results. We are going to take this
data and display it in 3 different types of graphs.

Circle (or Pie) Graph
There are three basic graph forms.
Notice how each of the following examples
are used to illustrate the data.
Choose the best graph form to express
your results.
Bar Graph
Line Graph

Bar Graph
• A bar graph is used to show
relationships between groups.
• The two items being compared do
not need to affect each other.
• It's a fast way to show big
differences. Notice how easy it is
to read a bar graph.
Chocolate Milk Sold
53
72
112
33
76
0
20
40
60
80
100
120
Monday Tuesday Wednesday Thursday Friday
Day
AmountSold
Monday Tuesday
Wednesday Thursday
Friday

Circle Graph or Pie Graph
• A circle graph is used to
show how a part of
something relates to the
whole.
• This kind of graph is
needed to show
percentages effectively.
Chocolate Milk Sold
Monday
Tuesday
Wednesday
Thursday
Friday

Line Graph
• A line graph is used to show continuing data; how one
thing is affected by another.
• It's clear to see how things are going by the rises and
falls a line graph shows.
Chocolate MIlk Sold
0
20
40
60
80
100
120
Day
AmountSold
Chocolate

Chocolate MIlk Sold
0
20
40
60
80
100
120
Day
AmountSold
Chocolate
Chocolate Milk Sold
Monday
Tuesday
Wednesday
Thursday
Friday
Bar Graph
Line Graph
Circle (Pie) Graph
The same data displayed in 3
different types of graphs.
Chocolate Milk Sold
53
72
112
33
76
0
20
40
60
80
100
120
Day
AmountSold
Monday Tuesday
Wednesday Thursday
Friday

On what day did they sell the most chocolate milk?
a. Tuesday b. Friday c. Wednesday
Chocolate Milk Sold
53
72
112
33
76
0
20
40
60
80
100
120
Day
AmountSold
Monday Tuesday
Wednesday Thursday
Friday

Chocolate
Monday
Tuesday
Wednesday
Thursday
Friday
On what day was the least amount of chocolate milk sold?
a. Monday b. Tuesday c. Thursday

Chocolate
0
20
40
60
80
100
120
Day
AmountSold
Chocolate
On what day did they have a drop in chocolate milk sales?
a. Thursday b. Tuesday c. Monday

Validity of measurement
• Reliability refers to consistency
–Are we getting something stable over time?
–Internally consistent?
• Validity refers to accuracy
–Is the measure accurate?
–Are we really measuring what we want?

Validity - Definitions
• The extent to which a test measures what it
was designed to measure.
• Agreement between a test score or measure
and the quality it is believed to measure.
• Proliferation of definitions led to a dilution of
the meaning of the word into all kinds of
“validities”

Internal validity – Cause and effect in experimentation;
high levels of control; elimination of confounding
variables
External validity - to what extent one may safely
generalize the (internally valid) causal inference (a)
from the sample studied to the defined target
population and (b) to other populations (i.e. across
time and space). Generalize to other people
 Population validity – can the sample results be generalized
to the target population
 Ecological validity - whether the results can be applied to
real life situations. Generalize to other (real) situations

• Content validity – when trying to measure a
domain are all sub-domains represented
– When measuring depression are all 16 clinical
criteria represented in the items
– Very complimentary to domain sampling theory
and reliability
– However, often high levels of content validity will
lead to lower internal consistency reliability

Construct validity – overall are you measuring
what you are intending to measure
 Intentional validity – are you measuring what you are
intending and not something else. Requires that
constructs be specific enough to differentiate
 Representation validity or translation validity – how
well have the constructs been translated into
measureable outcomes. Validity of the operational
definitions
 Face validity – Does a test “appear” to be measuring the
content of interest. Do questions about depression
have the words “sad” or “depressed” in them

• Construct Validity
– Observation validity – how good are the measures
themselves. Akin to reliability
– Convergent validity - Convergent validity refers to the
degree to which a measure is correlated with other
measures that it is theoretically predicted to correlate
with.
– Discriminant validity - Discriminant validity describes
the degree to which the operationalization does not
correlate with other operationalizations that it
theoretically should not correlated with.

Criterion-Related Validity - the success of measures
used for prediction or estimation. There are two types:
 Concurrent validity - the degree to which a test correlates
with an external criteria that is measured at the same time
(e.g. does a depression inventory correlated with clinical
diagnoses)
 Predictive validity - the degree to which a test predicts
(correlates) with an external criteria that is measured some
time in the future (e.g. does a depression inventory score
predict later clinical diagnosis)
Social validity – refers to the social importance and
acceptability of a measure

Research intro, sampling, scales, error & validity

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