Questionnaire Validity and

reliability

Department of Social and Preventive Medicine,

Faculty of Medicine

Outlines

Introduction

What is validity and reliability?

Types of validity and reliability. How do you measure them?

Types of Sampling Methods

Sample size calculation

G-Power ( Power Analysis)

Research

• The systematic investigation into and study of

materials and sources in order

to establish facts and reach new conclusions

• In the broadest sense of the word, the research

includes gathering of data in order to generate

information and establish the facts for the

advancement of knowledge.

● Step I: Define the research problem

● Step 2: Developing a research plan & research Design

● Step 3: Define the Variables & Instrument (validity &

Reliability)

● Step 4: Sampling & Collecting data

● Step 5: Analysing data

● Step 6: Presenting the findings

• A technique for collecting data in which a respondent provides answers to a series of questions.

• The vehicle used to pose the questions that the researcher wants respondents to answer.

• The validity of the results depends on the quality of these instruments.

• Good questionnaires are difficult to construct;

• Bad questionnaires are difficult to analyze.

A questionnaire is

Ch 11 6

•Identify the goal of your questionnaire

•What kind of information do you want to gather

with your questionnaire?

• What is your main objective?

• Is a questionnaire the best way to go about

collecting this information?

How To Obtain Valid Information

• Ask purposeful questions

• Ask concrete questions

• Use time periods based on importance of the

questions

• Use conventional language

• Use complete sentences

• Avoid abbreviations

• Use shorter questions

Validity and Reliability

• Validity: How well does the measure or

design do what it purports to do?

• Reliability: How consistent or stable is

the instrument?

– Is the instrument dependable?

Which device can measure the body

temperature ?

1 37.2 37.2

2 37.2 39.3

3 37.1 34.4

4 37.2 28.2

5 37.2 43.3

6 37.2 37

7 37.2 39

Logical Statistical

Face Conten

t

PredictiveConvergent Concurrent

Validity

ConsistencyReliability Objectivity

Content

Construct

Divergent/Discriminant

1. Content - What should be asked?

2. Wording - How should each question be phrased?

3. Sequence - In what order should the questions be

presented?

4. Layout - What layout will best serve the research

objectives?

The Major Decisions in Questionnaire

Design

Face Validity

– Face validity is the extent to which a test is

subjectively viewed as covering the concept it

purports to measure

– As a check on face validity, test/survey items are

sent to experts to obtain suggestions for

modification.

– Because of its vagueness and subjectivity,

psychometricians have abandoned this concept for a

long time.

Content Validity

– In psychometrics, content validity (also known as logical

validity) refers to the extent to which a measure represents all

facets of a given construct.

– Face validity Vs Content validity:

• Face validity can be established by one person

• Content validity should be checked by a panel, and thus

usually it goes hand in hand with inter-rater reliability

(Kappa!)

The Content Validity Index

Content validity has been defined as follows:

• (1) ‘‘...the degree to which an instrument has an appropriate

sample of items for the construct being measured’’ (Polit &

Beck, 2004, p. 423);

• (2) ‘‘...whether or not the items sampled for inclusion on the

tool adequately represent the domain of content addressed

by the instrument’’ (Waltz, Strickland, & Lenz, 2005, p. 155);

• (3) ‘‘...the extent to which an instrument adequately samples the

research domain of interest when attempting to measure

phenomena’’ (Wynd, Schmidt, & Schaefer, 2003, p. 509).

Two types of CVIs.

• content validity of individual items

• content validity of the overall scale.

• Researchers use I-CVI information to guide them in revising,

deleting, or substituting items

• I-CVIs tend only to be reported in methodological studies that

focus on descriptions of the content validation process

• Most often reported in scale development studies is the CVI

CVI

Degree to which an

instrument has an appropriate sample of items

for construct being

measured

S-CVI

Content Validity of the overall scale

S-CVI/UA

Proportion of items on a scale that

achieves a

relevance rating of 3 or 4 by all the experts

S-CVI/Ave

Average of the I-CVIs for

I-CVI

Content Validity of

individual items:

Question

Has each item in the

instruments

consistency?

Are the items

representative of

concepts related to the dissertation

topic?

Are the items relevance to

concepts related to the dissertation

topic?

Are the items clarity in term

of wording comments

Q1 ① ② ③ ④ ① ② ③ ④ ① ② ③ ④ ① ② ③ ④

Q2 ① ② ③ ④ ① ② ③ ④ ① ② ③ ④ ① ② ③ ④

Q3 ① ② ③ ④ ① ② ③ ④ ① ② ③ ④ ① ② ③ ④

Q4 ① ② ③ ④ ① ② ③ ④ ① ② ③ ④ ① ② ③ ④

Q5 ① ② ③ ④ ① ② ③ ④ ① ② ③ ④ ① ② ③ ④

Ratings

1= not relevant 2 =somewhat relevant.

3= quite relevant 4= highly relevant.

I-CVI, item-level content validity index

S-CVI, content validity index for the scale

Acceptable standard for the S-CVI recommended a

minimum S-CVI of .80.

If the I-CVI is higher than 79%, the item will be appropriate.

If it is between 70% and 79%, it needs revision.

If it is less than 70% it is eliminated

Content validity index

The I-CVI expresses the proportion of agreement

on the relevancy of each item

.𝑪𝑽𝑹 =𝒏𝒆−

𝑵

𝟐𝑵

𝟐

In this formula:

• ne = The number of specialists who have chosen the " Necessary "

option

• N = total number of assessors.

Content validity ratio

Kappa statistic is a consensus index of inter-rater

agreement that adjusts for chance agreement and is an

important supplement to CVI because Kappa provides

information about the degree of agreement beyond

chance

Evaluation criteria for Kappa is the values

above 0.74= excellent

between 0.60 and 0.74=good

between 0.40 and 0.59= fair

To calculate modified kappa statistic, the probability of chance

agreement was first calculated for each item by following formula:

PC= [N! /A! (N -A)!]* . 5N.

• N= number of experts in a panel and

• A= number of panelists who agree that the item is relevant.

After calculating I-CVI for all instrument items, finally, kappa was

computed by entering the numerical values of probability of chance

agreement (PC) and content validity index of each item (I-CVI) in

following formula:

K= (I-CVI - PC) / (1- PC).

Logical Statistical

Face Conten

t

PredictiveConvergent Concurrent

Validity

ConsistencyReliability Objectivity

Content

Construct

Divergent/Discriminant

Criterion

Criterion Validity

• This type of validity is used to measure the ability of an instrument to predict future outcomes.

• Validity is usually determined by comparing two instruments ability to predict a similar outcome with a single variable being measured.

• There are two major types of criterion validity:

– predictive

– concurrent

30

Criterion validity

• A test has high criterion validity if

– It correlates highly with some external benchmark

(concurrent) ?

– How well does the test correlated with outcome

criteria (predictive)?

Concurrent Validity

• Concurrent validity is used when the two instruments are used to measure the same event at the same time.

• It correlates highly with some external benchmark (concurrent)

• Example: • DASS -> measuring depression • New instrument -> measuring depression

Predictive Validity

• Predictive validity is used when the instrument is administered then time is allowed to pass and is measured against the another outcome.

• Regression analysis can be applied to establish criterion validity.

• An independent variable could be used as a predictor variable and a dependent variable, the criterion variable.

• The correlation coefficient between them is called validity coefficients.

How is Criterion Validity Measured?

• The statistical measure or correlation coefficient tells the

degree to which the instrument is valid based on the measured

criteria.

• What does it look like in an equation?

– The symbol “r” denotes the correlation coefficient.

– A higher “r” value shows a positive relationship between the

instruments.

– A mix of high and low “r” values shows a negative relationship.

• As a rule of thumb, for absolute value of r:

• 0.00-0.19: very weak

• 0.20-0.39: weak

• 0.40-0.59: moderate

• 0.60-0.79: strong

• 0.80-1.00: very strong.

Predictive Validity Concurrent Validity

Logical Statistical

Face Conten

t

PredictiveConvergent Concurrent

Validity

ConsistencyReliability Objectivity

Content

Construct

Divergent/Discriminant

Criterion

Construct validity

• Measuring things that are in our theory of a domain.

• The construct is sometimes called a latent variable

– You can’t directly observe the construct

– You can only measure its surface manifestations

– it is concerned with abstract and theoretical construct, construct validity is also known as theoretical validity

39

What are Latent Variables?

• Most/all variables in the social world are not directly observable.

• This makes them ‘latent’ or hypothetical constructs.

• We measure latent variables with observable indicators, e.g. questionnaire items.

• We can think of the variance of an observable indicator as being partially caused by:

– The latent construct in question

– Other factors (error)

• I cringe when I have to go to math class.

• I am uneasy about going to the board in a math class.

• I am afraid to ask questions in math class.

• I am always worried about being called on in math class.

• I understand math now, but I worry that it's going to get really difficult soon.

Math anxiety

• Specifying formative versus reflective constructs is a

critical preliminary step prior to further statistical

analysis. Specification follows these guidelines:

• Formative– Direction of causality is from measure to construct

– No reason to expect the measures are correlated

– Indicators are not interchangeable

• Reflective– Direction of causality is from construct to measure

– Measures expected to be correlated

– Indicators are interchangeable

– An example of formative versus reflective constructs is given in the

figure below.

Construct, dimension, subscale, factor,

component

• This construct has eight dimensions (e.g.

Intelligence has eight aspects)

• This scale has eight subscales (e.g. the survey

measures different but weakly related things)

• The factor structure has eight factors/components

(e.g. in factor analysis/PCA)

Exploratory Factor Analysis

• (EFA) is a statistical approach to determining the correlation among the variables in a dataset.

• This type of analysis provides a factor structure (a grouping of variables based on strong correlations).

• EFA is good for detecting "misfit" variables. In general, an EFA prepares the variables to be used for cleaner structural equation modeling. An EFA should always be conducted for new datasets.

. The Kaiser-Meyer-Olkin measure of sampling adequacy tests whether the partial

correlations among variables are small.

KMO Statistics

Marvelous: .90s

Meritorious: .80s

Middling: .70s

Mediocre: .60s

Miserable: .50s

Unacceptable: <.50

Bartlett’s Test of Sphericity

• Tests hypothesis that correlation matrix is an

identity matrix.

• Diagonals are ones

• Off-diagonals are zeros

• A significant result (Sig. < 0.05) indicates matrix

is not an identity matrix; i.e., the variables do

relate to one another enough to run a

meaningful EFA.

Communalities

• A communality is the extent to which an item correlates with all other items.

• Higher communalities are better. If communalities for a particular variable are low (between 0.0-0.4), then that variable will struggle to load significantly on any factor.

• In the table below, you should identify low values in the "Extraction" column. Low values indicate candidates for removal after you examine the pattern matrix.

Parallel analysis

• is a method fordetermining the numberof components or factorsto retain from pca orfactor analysis.Essentially, the programworks by creating arandom dataset with thesame numbers ofobservations andvariables as the originaldata.

https://www.statstodo.com

Factor analysis for dichotomous variables

• Using Factor software and simultaneously Parallel analysis for binary data

Establishing construct validity

• Convergent validity– Agrees with other measures of the same thing

• Divergent/Discriminant validity– Different tests measure different things– Does the test have the “ability to discriminate”?– (Campbell & Fiske, 1959)

61

Construct validity is the extent to which a set of measured items actually

reflected the theoretical latent construct those item are designed to measure.

Thus, it deals with the accuracy of measurement.

Construct validity is made up of TWO important components which they are:

1) Convergent validity: the items that are indicators of a specific construct

should converge or share a high proportion of variance in common,

known as convergent validity. The ways to estimate the relative amount

of convergent validity among item measures:

Construct validity

Discriminant Validity:

the extent to which a construct is truly distinct frame other

construct. To test the discriminant validity the AVE for two

factors should be grater than the square of the correlation

between the two factors to provide evidence of discriminant

validity.

• Discriminant validity can be tested by examining the AVE for

each construct against squared correlations (shared variance)

between the construct and all other constructs in the model.

• A construct will have adequate discriminant validity if the

AVE exceeds the squared correlation among the constructs

(Fornell & Larcker, 1981; Hair et al., 2006).

Factor Loading:

at a minimum, all factor loading should be statistically significant.A good rule of thumb is that standardized loading estimates shouldbe .5 or higher, and ideally .7 or higher.

Average Variance Extracted (AVE):

is the average squared factor loading. A VE of 0.5 or higher is agood rule of thumb suggesting adequate convergence. A VE lessthan .5 indicates that on average, more error remains in the itemsthan variance explained by the latent factor structure impose on themeasure (Haire et al., 2006, p 777).

Construct Reliability: construct reliability should be .7 or higher toindicate adequate convergence or internal consistency.

Individual model

(First order CFA)

Mea surement Model

Structural Equation Modeling (SEM)

Individual

Model

Measurement

Model

Structural

Model

Developing Assessments

If necessary, consider commercial assessments

or create a new assessment

What assessments do you already have that

purport to measure this?

What are you trying to measure? Purpose

Review

Purchase Develop

Considerations

• Using what already have– Is it carefully aligned to your purpose?– Is it carefully matched to your purpose?– Do you have the funds (for assessment, equipment, training)?

• Developing a new assessment– Do you have the in-house content knowledge?– Do you have the in-house assessment knowledge?– Does your team have time for development?– Does your team have the knowledge and time needed for proper scoring?– Identify the goal of your questionnaire.– What kind of information do you want to gather with your questionnaire?– What is your main objective?– Is a questionnaire the best way to go about collecting this information?

Adopting an Instrument

• Adopting an instrument is quite simple and requires very little effort. Even when an instrument is adopted, though, there still might be a few modifications that are necessary

Adapting an Instrument

• Adapting an instrument requires more substantial changes than adopting an instrument. In this situation, the researcher follows the general design of another instrument but adds items, removes items, and/or substantially changes the content of each item

• Whenever possible, it is best for an instrument to be adopted.

• When this is not possible, the next best option is to adapt an instrument.

• However, if there are no other instruments available, then the last option is to develop an instrument.

STEP Type of Validity Development Adaption Adoption

pretest Logical

Face+ +/- +/-

Content+ +

+/-

Pilot /

main

study

Criterion

Concurrent + +-

Predictive + +-

Construct

Convergent + + +

Divergent + + +

Reliability

Types of Reliability

• Test-Retest Reliability: Degree of temporal stability of the instrument.

– Assessed by having instrument completed by same people during two different time periods.

• Alternate-Forms Reliability: Degree of relatedness of different forms of test.

– Used to minimize inflated reliability correlations due to familiarity with test items.

Types of Reliability (cont.)

• Internal-Consistency Reliability: Overall degree of relatedness of all test items or raters.

– Also called reliability of components.

• Item-to-Item Reliability: The reliability of any single item on average.

• Judge-to-Judge Reliability: The reliability of any single judge on average.

• Cronbach’s alpha to evaluate the internal consistency of observed items, and also applies factor analysis to extract latent constructs from these consistent observed variables.

• >0.90, means the questions are asking the same things

• 0.7 to 0.9 is the acceptable range.

Remember!

An assessment that is highly reliable is not necessarily valid. However, for an assessment to be valid, it must also be reliable.

Improving Validity & Reliability

• Ensure questions are based on standards

• Ask purposeful questions

• Ask concrete questions

• Use time periods based on importance of the questions

• Use conventional language

• Use complete sentences

• Avoid abbreviations

• Use shorter questions

Overall Cronbach Coefficient Alpha

• One may argue that when a high Cronbach Alpha indicates a high degree of internal consistency, the test or the survey must be uni-dimensional rather than multi-dimensional. Thus, there is no need to further investigate its subscales. This is a common misconception.

Ch 11 89

Performing the Pilot test

• A pilot test involves conducting the survey on a small, representative set of respondents in order to reveal questionnaire errors before the survey is launched.

• It is important to run the pilot test on respondents that are representative of the target population to be studied.

Cronbach's alpha Measures the

intercorrelations among test items, and is

thus known as an internal

consistency estimate of reliability of test

scores

Test-retest reliability refers to the degree

to which test results are consistent over

time. In order to measure test-retest

reliability, we must first give the

same test to the same individuals on two

occasions and correlate the scores

Thanks for your Attention !!

Thank you

Dr Mahmoud Danaee

mdanaee@um.edu.my

Senior Visiting Research Fellow ,

Department of Social and Preventive Medicine

Faculty of Medicine

)