Autocorrelation Screening : A Potentially Efficient Method for Detecting Repetitive Response Patterns in Questionnaire Data
Identifikátory výsledku
Kód výsledku v 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>
Výsledek na webu
<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>
Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
Autocorrelation Screening : A Potentially Efficient Method for Detecting Repetitive Response Patterns in Questionnaire Data
Popis výsledku v původním jazyce
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.
Název v anglickém jazyce
Autocorrelation Screening : A Potentially Efficient Method for Detecting Repetitive Response Patterns in Questionnaire Data
Popis výsledku anglicky
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.
Klasifikace
Druh
J<sub>SC</sub> - Článek v periodiku v databázi SCOPUS
CEP obor
—
OECD FORD obor
50101 - Psychology (including human - machine relations)
Návaznosti výsledku
Projekt
—
Návaznosti
S - Specificky vyzkum na vysokych skolach
Ostatní
Rok uplatnění
2022
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Údaje specifické pro druh výsledku
Název periodika
Practical Assessment, Research, and Evaluation
ISSN
1531-7714
e-ISSN
—
Svazek periodika
27
Číslo periodika v rámci svazku
February
Stát vydavatele periodika
US - Spojené státy americké
Počet stran výsledku
11
Strana od-do
1-11
Kód UT WoS článku
—
EID výsledku v databázi Scopus
2-s2.0-85125279591