Best practice recommendations for data screening

DeSimone, Justin A.; Harms, Peter D.; DeSimone, Alice J.

doi:10.1002/job.1962

Cited by 471 publications

(381 citation statements)

References 17 publications

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“…There are many methods available with the potential to identify various forms of LQD (see, Curran, 2016;DeSimone et al, 2015). Some of these methods involve the direct assessment of response quality, others involve unobtrusive observation of respondent behavior patterns, and others require the calculation of statistical indicators.…”

Section: Methods For Detecting Lqdmentioning

confidence: 99%

“…It is important to emphasize that not all screens are appropriate for use in all studies. The suitability of screening techniques depends on survey design and methodology (Curran, 2016;DeSimone et al, 2015;Dunn et al, in press). For example, time-based screens require survey administrators to measure the time required for each respondent to complete the survey.…”

Section: Selection Of Screening Techniquesmentioning

confidence: 99%

“…With the exception of D, cutoff values for screening techniques are necessarily subjective and arbitrary. Previous literature has used cutoff values for most screening techniques, but none of these values is set in stone and the appropriateness of each may be influenced by aspects of data collection (e.g., length of a survey, balance of positively and negatively worded items; see, DeSimone et al, 2015). This paper is not intended to definitively answer the question of what cutoff scores are appropriate for each screening technique.…”

Section: Research Questionsmentioning

confidence: 99%

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Dirty Data: The Effects of Screening Respondents Who Provide Low-Quality Data in Survey Research

2017

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The purpose of this study is to empirically address questions pertaining to the effects of data screening practices in survey research. This study addresses questions about the impact of screening techniques on data and statistical analyses. It also serves an initial attempt to estimate descriptive statistics and graphically display the distributions of popular screening techniques. Data were obtained from an online sample who completed demographic items and measures of character strengths (N = 307). Screening indices demonstrate minimal overlap and differ in the number of participants flagged. Existing cutoff scores for most screening techniques seem appropriate, but cutoff values for consistency-based indices may be too liberal. Screens differ in the extent to which they impact survey results. The use of screening techniques can impact inter-item correlations, inter-scale correlations, reliability estimates, and statistical results. While data screening can improve the quality and trustworthiness of data, screening techniques are not interchangeable. Researchers and practitioners should be aware of the differences between data screening techniques and apply appropriate screens for their survey characteristics and study design. Low-impact direct and unobtrusive screens such as self-report indicators, bogus items, instructed items, longstring, individual response variability, and response time are relatively simple to administer and analyze. The fact that data screening can influence the statistical results of a study demonstrates that low-quality data can distort hypothesis testing in organizational research and practice. We recommend analyzing results both before and after screens have been applied.

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Section: Methods For Detecting Lqdmentioning

confidence: 99%

Section: Selection Of Screening Techniquesmentioning

confidence: 99%

Section: Research Questionsmentioning

confidence: 99%

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Dirty Data: The Effects of Screening Respondents Who Provide Low-Quality Data in Survey Research

2017

Self Cite

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“…Thus we excluded 106 participants (1.8%) who gave the same response between 2 and 5 to at least 24 RSS for clear IER, but retained 842 participants (14.4%) who chose the lowest response option, not at all/does not apply, for at least 24 questions. Preferring to retain invalid responses rather than exclude valid responses, we implemented this screening technique (long string) [75] more permissively than Johnson [76], who used it to exclude 3.5% of another Web-based survey's participants. Four of our samples would have set a lower threshold by our criterion: absolute minima within samples occurred first at 17 (n = 0), 17 (1), 19 (0), 21 (3), and 24 (0) identical responses.…”

Section: Exclusion Criteriamentioning

confidence: 99%

Bifactor Models of Religious and Spiritual Struggles: Distinct from Religiousness and Distress

et al. 2016

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Abstract:The Religious and Spiritual Struggles Scale (RSS) measures important psychological constructs in an underemphasized section of the overlap between religion and well-being. Are religious/spiritual struggles distinct from religiousness, distress, and each other? To test the RSS' internal discriminant validity, we replicated the original six-factor measurement model across five large samples (N = 5705) and tested the fit of a restricted bifactor model, which supported the mutual viability of multidimensional and unidimensional scoring systems for the RSS. Additionally, we explored a bifactor model with correlated group factors that exhibited optimal fit statistics. This model maintained the correlations among the original factors while extracting a general factor from the RSS. This general factor's strong correlations with religious participation and belief salience suggested that this factor resembles religiousness itself. Estimating this general factor seemed to improve Demonic and Moral struggles' independence from religiousness, but did not change any factor's correlations with neuroticism, depression, anxiety, and stress. These distress factors correlated with most of the independent group factors corresponding to the original dimensions of the RSS, especially Ultimate Meaning and Divine struggles. These analyses demonstrate the discriminant validity of religious/spiritual struggles and the complexity of their relationships with religiousness and distress.

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“…These translations were proofread by native speakers and all ambiguities resolved in collaboration with the translators before the survey was implemented online. The survey employed numerous quality measures to maximize data quality and screen out careless responses, including instruction-based attention filters ("Please select strongly agree"), bogus items ("I always sleep less than one hour per night"), response pattern indicators (e.g., straight-lining), time filters, and self-reported data quality checks (e.g., "I gave this study enough attention") [41,42]. Participants failing the instruction-based attention filters were eliminated automatically while those failing multiple quality checks were replaced.…”

Section: Data Collectionmentioning

confidence: 99%

An Environmental Perspective on Clothing Consumption: Consumer Segments and Their Behavioral Patterns

Gwozdz

Nielsen

Müller³

2017

Sustainability

110

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Efforts to decrease the environmental impact of today's clothing industry across the entire process of production, purchase, maintenance, and disposal can be driven by either suppliers or consumers. Changing the behavior of the latter, however, requires an understanding of current clothing consumption patterns-a currently under-researched area. We therefore shed more light on these patterns in the purchase, use and maintenance, and discard phases by analyzing unique data on 4617 adult consumers (aged 18-65) from Germany, Poland, Sweden, and the U.S., who we divide into five segments based on clothing consumption behavior. At the low end of the spectrum is a consumer segment that earns the least, consumes mostly budget brand clothing, and is the least open to alternative more environmentally friendly business models such as fashion leasing or clothing libraries. At the other extreme lies a small segment that earns the most, engages in high consumption of medium or premium brand clothing, and is most open to alternative business models. Lying between these two is a primarily female segment that purchases an above average amount of clothing from budget brands. In addition to the segments' different reported purchase behavior and a varying openness to alternative business models, we identify differences in willingness to pay for clothing made of material that is more environmentally friendly than conventional fabrics. These observations suggest several promising directions for environmental interventions tailored toward specific consumer segments.

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Best practice recommendations for data screening

Cited by 471 publications

References 17 publications

Dirty Data: The Effects of Screening Respondents Who Provide Low-Quality Data in Survey Research

Dirty Data: The Effects of Screening Respondents Who Provide Low-Quality Data in Survey Research

Bifactor Models of Religious and Spiritual Struggles: Distinct from Religiousness and Distress

An Environmental Perspective on Clothing Consumption: Consumer Segments and Their Behavioral Patterns

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