Proactively balancing the validity and legal compliance of personal background measures in personnel management

Baehr, Melany E.; Jones, John W.; Baydoun, Ramzi; Behrens, Gary M.

doi:10.1007/bf02230377

Cited by 3 publications

(2 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To the extent that judgmental qualities can be used to select items and configure scales with high degrees of face validity, both the reactions of constituencies (e.g., sponsoring organizations) and the motivation of respondents may be enhanced (Bornstein, 1996;Nevo, 1985). Other issues, such as perceived invasiveness of items (Baehr, Jones, Baydoun, & Behrens, 1994;Mael, Connerly, & Morath, 1996;), may also affect respondent motivation. As Rogelberg & Luong, 1998) indicated in their reviews, better respondent motivation translates into higher response rates and better data.…”

Section: Part I: Extant Literature Relevant To Scale Reductionmentioning

confidence: 99%

Issues and Strategies for Reducing the Length of Self‐report Scales

Stanton

Sinar

Balzer

et al. 2002

Personnel Psychology

451

534

View full text Add to dashboard Cite

Greater understanding of the complex interrelationships among workrelevant constructs has increased the number of constructs on organizational surveys. Good psychometric practice also dictates the use of multiple items per construct. The net result has been longer surveys. Longer surveys take more time to complete, tend to have more missing data, and have higher refusal rates than short surveys. Arguably, then, techniques for reducing the length of scales while maintaining psychometric quality are worthwhile. Little guidance exists on how to reduce the length of a multi-item scale and we argue that the most common technique, maximizing internal consistency, is problematic and should be avoided. We present a set of item "quality indices" to help conceptualize the competing issues that influence item retention decisions. Statistical analysis of an example case using these indices suggested that there are 3 key aspects of item quality to consider when reducing a scale. We describe strategies that can assist scale developers in using these 3 aspects of item quality when making scale reduction decisions.

show abstract

Section: Part I: Extant Literature Relevant To Scale Reductionmentioning

confidence: 99%

Issues and Strategies for Reducing the Length of Self‐report Scales

Stanton

Sinar

Balzer

et al. 2002

Personnel Psychology

451

534

View full text Add to dashboard Cite

show abstract

“…One common criticism concerns the potential for faking (Kluger & Colella, 1993). Another criticism focuses on applicant reactions to life history measures (Baehr, Jones, Baydoun, Behrens, 1994;Grey, Mael, & Morath, 1995) and the apparent instability of at least some background data measures (Wernimont, 1962). Perhaps the most common criticism of background data measures is attributed to the limited evidence available for their content and construct validity (Katzell, 1994).…”

mentioning

confidence: 99%

Item Generation Procedures and Background Data Scales: Implications for Construct and Criterion‐related Validity

Mumford

Costanza

Connelly

et al. 1996

Personnel Psychology

View full text Add to dashboard Cite

Background data measures are one of the best predictors of job performance. Nonetheless, questions have been raised about their content and construct validity. The present effort describes a set of procedures for developing construct and content valid background data items. Data gathered in seven field studies and six laboratory studies are presented bearing on the reliability and validity of the measures constructed using these item generation procedures. Findings in these studies indicate that construct‐based item generation procedures yield reliable scales evidencing both content and construct validity. Furthermore, these scales are capable of predicting performance in a variety of settings. Theoretical and practical implications of these findings for the development and validation of background data measures are discussed.

show abstract