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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
10
20
30
40
50
60
70
80
90
100
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
0
10
20
30
40
50
60
70
80
90
100
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
10
20
30
40
50
60
70
80
90
100
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
6
8
10
12
14
16
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
2
4
6
8
10
12
14
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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
2
2, 5
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
var i abl e
item nonresponse (%)
4- poi nt 8- poi nt VAS
0
0, 5
1
1, 5
2
2, 5
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
var i abl e
item nonresponse (%)
4- poi nt 8- poi nt VAS
ResultsItem Nonresponse
12/20
without lurkers
Results
94
95
96
97
98
99
100
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
94
95
96
97
98
99
100
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
94
95
96
97
98
99
100
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