Visual Analogue Scalesin

Online Surveys

Session 19: Scale Construction & Methods' Effects

Frederik Funke March 23rd, 2005

General Online Research 2005

Objective

1/20

Comparison of 4- & 8-point categorial scales and Visual Analogue Scales (VAS)

Does type of scale affect…… means & variance of responses?… response times?… nonresponse & dropout?

What is the appropriate way of categorizing VAS values?

Overview

• Visual Analogue Scales (VAS)

• Design & sample

• Results

• Conclusion

2/20

• First description in 1921

• Main disadvantages– not suitable for all populations– JavaScript has to be enabled

• Main advantages– high reliability and validity– point of socially desired responses is not easy to

identify

3/20

Visual Analogue Scales

• Scales to be compared:

• VAS (80 discrete values) delivered by JavaScript• Verbal and animated instruction:

inappropriate(„trifft überhaupt

nicht zu“)

appropriate(„trifft voll und ganz zu“)

VAS

inappropriate(„trifft überhaupt

nicht zu“)

appropriate(„trifft voll und ganz zu“)

4-point

inappropriate(„trifft überhaupt

nicht zu“)

appropriate(„trifft voll und ganz zu“)

8-point

4/20

Design

Design• Questionnaire:

– 16 items (behaviour in groups)

– experimental design– dichotomizing of questionnaire 9 groups:

4-point – 4-point 4-point – 8-point 4-point –VAS 8-point – 4-point 8-point – 8-point 8-point –VAS VAS – 4-point VAS – 8-point VAS –VAS

– blind

• Sample– self selected– recruitment via email, websites & newsletters– n = 667 5/20

Results

• Mean, standard deviation

• Response times

• Nonresponse

• Dropout

• Categorizing VAS values

6/20

0

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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

fgdh

dfgh

Rei he1

Results

7/20

VAS8-point4-point

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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

var i abl e

mea

n

4- poi nt 8- poi nt VAS

ResultsMean

differences are significant (p < 0,01)

(4-point) = 58,1

(8-point) = 60,0

(VAS) = 57,5

8/20

0

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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

var i abl e

standard deviation

4- poi nt 8- poi nt VAS

ResultsStandard Deviation

differences are significant (p < 0,01)

s (4-point) = 34,3

s (8-point) = 31,3

s (VAS) = 28,1

9/20

0

2

4

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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

var i abl e

response time (sec)

4- poi nt 8- poi nt VAS

Results

(4-point) = 7,0

( 8-point) = 7,1

(VAS) = 7,8differences between VAS & categorial scales are significant (p < 0,01)

Response Times

10/20

0

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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

var i abl e

standard deviation (sec)

4- poi nt 8- poi nt VAS

Results

s (4-point) = 4,4

s (8-point) = 4,0

s (VAS) = 5,2

differences are significant (p < 0,01)

Standard Deviation (Response Times)

11/20

0

0, 5

1

1, 5

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2, 5

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var i abl e

item nonresponse (%)

4- poi nt 8- poi nt VAS

0

0, 5

1

1, 5

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2, 5

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var i abl e

item nonresponse (%)

4- poi nt 8- poi nt VAS

ResultsItem Nonresponse

12/20

without lurkers

Results

94

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star

tde

mo 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

var i abl e

(%)

4- poi nt 8- poi nt VAS

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star

tde

mo 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

var i abl e

(%)

4- poi nt 8- poi nt VAS

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star

tde

mo 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

var i abl e

(%)

4- poi nt 8- poi nt VAS 13/20

Dropout

How to categorize VAS-Values?

When comparing frequencies, VAS values have to be categorized:

2 ways of categorizing VAS:

(1) linear transformation:

equal intervals form one category

(2) transformation with reduced extremes:

extreme categories’ width is 2/3 of innercategories‘ width

14/20

• Middle categories can be described in 3 ways:

– “fits perfectly” (e.g. “2”)

– “fits, but tendency to the right side” (e.g. “2-”)

– “fits, but tendency to the left side” (e.g. “2+”)

• Extreme categories only can be described in 2 ways:

– “fits perfectly” (e.g. “1”)

– “fits, but tendency to adjoining category” (e.g. “1-”)

Model of Reduced Extremes

1 1- 2+ 2 3+2- 3 3- 4+ 4

2 sections 3 sections 3 sections 2 sections

15/20

• Linear transformation VAS 4-point:

• Transformation with reduced extremes VAS 4-point:

• Linear transformation VAS 8-point:

• Transformation with reduced extremes VAS 8-point:

Transformation of VAS

16/20

1 801-16 17-40 41-64 65-80

1 2 3 4

1 801-7 8-18 19-29 30-40 41-51 52-62 63-73 74-80

87654321

11-20 21-40 41-60 61-80

1 2 3 480

11-10 11-20 21-30 31-40 41-50 51-60 61-70 71-80

86 75432180

• Reducing extreme categories‘ width leads for every category to greater correspondence between 4-point scale and VAS

index of all 16 items

VAS (linearly transformed) 4-pointVAS (reduced extremes)

Transformation of VAS Values

17/20

• Greater correspondence between 8-point and VAS after transformation with reduced extremes

• Only at 1 category advantages for linear transformation

index of all 16 items

VAS (linearly transformed) 8-pointVAS (reduced extremes)

Transformation of VAS Values

18/20

Conclusion• Modest differences in mean• Lower variance for VAS higher reliability

• Increase of response time when using VAS

• Higher dropout rate when using VAS• More lurkers when using VAS• More nonresponse with VAS

• Transforming VAS with reduced extremes leads to greater correspondence with categorial scales

19/20

Thanks for your time!

Further thanks for support:– Ulf-Dietrich Reips (University of Zurich)– Dagmar Krebs (University of Gießen)– Timo Gnambs– gir-l, WEXTOR & Experimental Psychology Lab

this presentation’s URL:http://www.FrederikFunke.de/papers/gor2005.htm