Detecting and Treating Errors in Tests and Surveys

Autor/in	von Davier, Matthias
Titel	Detecting and Treating Errors in Tests and Surveys
Quelle	In: Quality Assurance in Education: An International Perspective, 26 (2018) 2, S.243-262 (20 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0968-4883
DOI	10.1108/QAE-07-2017-0036
Schlagwörter	Statistical Analysis; Surveys; International Assessment; Error Patterns; Identification; Psychometrics; Computer Assisted Testing; Questionnaires; Response Style (Tests); Test Theory + Suchen Sie Ihr Suchwort? Statistische Analyse; Survey; Umfrage; Befragung; Fehlertyp; Identifikation; Identifizierung; Psychometry; Psychometrie; Fragebogen; Antwortverhalten; Testtheorie
Abstract	Purpose: Surveys that include skill measures may suffer from additional sources of error compared to those containing questionnaires alone. Examples are distractions such as noise or interruptions of testing sessions, as well as fatigue or lack of motivation to succeed. This paper aims to provide a review of statistical tools based on latent variable modeling approaches extended by explanatory variables that allow detection of survey errors in skill surveys. Design/methodology/approach: This paper reviews psychometric methods for detecting sources of error in cognitive assessments and questionnaires. Aside from traditional item responses, new sources of data in computer-based assessment are available -- timing data from the Programme for the International Assessment of Adult Competencies (PIAAC) and data from questionnaires -- to help detect survey errors. Findings: Some unexpected results are reported. Respondents who tend to use response sets have lower expected values on PIAAC literacy scales, even after controlling for scores on the skill-use scale that was used to derive the response tendency. Originality/value: The use of new sources of data, such as timing and log-file or process data information, provides new avenues to detect response errors. It demonstrates that large data collections need to better utilize available information and that integration of assessment, modeling and substantive theory needs to be taken more seriously. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01