Autocorrelation Screening : A Potentially Efficient Method for Detecting Repetitive Response Patterns in Questionnaire Data
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14230%2F22%3A00125354" target="_blank" >RIV/00216224:14230/22:00125354 - isvavai.cz</a>
Result on the web
<a href="https://scholarworks.umass.edu/pare/vol27/iss1/2/" target="_blank" >https://scholarworks.umass.edu/pare/vol27/iss1/2/</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.7275/vyxb-gt24" target="_blank" >10.7275/vyxb-gt24</a>
Alternative languages
Result language
angličtina
Original language name
Autocorrelation Screening : A Potentially Efficient Method for Detecting Repetitive Response Patterns in Questionnaire Data
Original language description
Valid data are essential for making correct theoretical and practical implications. Hence, efficient methods for detecting and excluding data with dubious validity are highly valuable in any field of science. This paper introduces the idea of applying autocorrelation analysis on self-report questionnaires with single-choice numbered, preferably Likert-type, scales in order to screen out potentially invalid data, specifically repetitive response patterns. We explain mathematical principles of autocorrelation in a simple manner and illustrate how to efficiently perform detection of invalid data and how to correctly interpret the results. We conclude that autocorrelation screening could be a valuable screening tool for assessing the quality of self-report questionnaire data. We present a summary of the method’s biggest strengths and weaknesses, together with functional tools to allow for an easy execution of autocorrelation screening by researchers, and even practitioners or the broad public. Our conclusions are limited by the current absence of empirical evidence about the practical usefulness of this method.
Czech name
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Czech description
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Classification
Type
J<sub>SC</sub> - Article in a specialist periodical, which is included in the SCOPUS database
CEP classification
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OECD FORD branch
50101 - Psychology (including human - machine relations)
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Practical Assessment, Research, and Evaluation
ISSN
1531-7714
e-ISSN
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Volume of the periodical
27
Issue of the periodical within the volume
February
Country of publishing house
US - UNITED STATES
Number of pages
11
Pages from-to
1-11
UT code for WoS article
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EID of the result in the Scopus database
2-s2.0-85125279591