The Effect of Faking on the Correlation Between Two Ordinal Variables: Some Population and Monte Carlo Results
Correlational measures are probably the most spread statistical tools in psychological research. They are used by researchers to investigate, for example, relations between self-report measures usually collected using paper-pencil or online questionnaires. Like many other statistical analysis, also...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2018-10-01
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| Series: | Frontiers in Psychology |
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| Online Access: | https://www.frontiersin.org/article/10.3389/fpsyg.2018.01876/full |
| _version_ | 1818503704098111488 |
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| author | Marco Bressan Yves Rosseel Luigi Lombardi |
| author_facet | Marco Bressan Yves Rosseel Luigi Lombardi |
| author_sort | Marco Bressan |
| collection | DOAJ |
| description | Correlational measures are probably the most spread statistical tools in psychological research. They are used by researchers to investigate, for example, relations between self-report measures usually collected using paper-pencil or online questionnaires. Like many other statistical analysis, also correlational measures can be seriously affected by specific sources of bias which constitute serious threats to the final observed results. In this contribution, we will focus on the impact of the fake data threat on the interpretation of statistical results for two well-know correlational measures (the Pearson product-moment correlation and the Spearman rank-order correlation). By using the Sample Generation by Replacement (SGR) approach, we analyze uncertainty in inferences based on possible fake data and evaluate the implications of fake data for correlational results. A population-level analysis and a Monte Carlo simulation are performed to study different modulations of faking on bivariate discrete variables with finite supports and varying sample sizes. We show that by using our paradigm it is always possible, under specific faking conditions, to increase (resp. decrease) the original correlation between two discrete variables in a predictable and systematic manner. |
| first_indexed | 2024-12-10T21:27:32Z |
| format | Article |
| id | doaj.art-4ad08ba4016a4889a8869177c2d86bea |
| institution | Directory Open Access Journal |
| issn | 1664-1078 |
| language | English |
| last_indexed | 2024-12-10T21:27:32Z |
| publishDate | 2018-10-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Psychology |
| spelling | doaj.art-4ad08ba4016a4889a8869177c2d86bea2022-12-22T01:32:56ZengFrontiers Media S.A.Frontiers in Psychology1664-10782018-10-01910.3389/fpsyg.2018.01876312432The Effect of Faking on the Correlation Between Two Ordinal Variables: Some Population and Monte Carlo ResultsMarco Bressan0Yves Rosseel1Luigi Lombardi2Department of Psychology and Cognitive Science, University of Trento, Rovereto, ItalyDepartment of Data Analysis, Ghent University, Ghent, BelgiumDepartment of Psychology and Cognitive Science, University of Trento, Rovereto, ItalyCorrelational measures are probably the most spread statistical tools in psychological research. They are used by researchers to investigate, for example, relations between self-report measures usually collected using paper-pencil or online questionnaires. Like many other statistical analysis, also correlational measures can be seriously affected by specific sources of bias which constitute serious threats to the final observed results. In this contribution, we will focus on the impact of the fake data threat on the interpretation of statistical results for two well-know correlational measures (the Pearson product-moment correlation and the Spearman rank-order correlation). By using the Sample Generation by Replacement (SGR) approach, we analyze uncertainty in inferences based on possible fake data and evaluate the implications of fake data for correlational results. A population-level analysis and a Monte Carlo simulation are performed to study different modulations of faking on bivariate discrete variables with finite supports and varying sample sizes. We show that by using our paradigm it is always possible, under specific faking conditions, to increase (resp. decrease) the original correlation between two discrete variables in a predictable and systematic manner.https://www.frontiersin.org/article/10.3389/fpsyg.2018.01876/fullPearson correlationSpearman correlationsample generation by replacement (SGR)fake ordinal/discrete datapopulation analysisMonte Carlo simulations |
| spellingShingle | Marco Bressan Yves Rosseel Luigi Lombardi The Effect of Faking on the Correlation Between Two Ordinal Variables: Some Population and Monte Carlo Results Frontiers in Psychology Pearson correlation Spearman correlation sample generation by replacement (SGR) fake ordinal/discrete data population analysis Monte Carlo simulations |
| title | The Effect of Faking on the Correlation Between Two Ordinal Variables: Some Population and Monte Carlo Results |
| title_full | The Effect of Faking on the Correlation Between Two Ordinal Variables: Some Population and Monte Carlo Results |
| title_fullStr | The Effect of Faking on the Correlation Between Two Ordinal Variables: Some Population and Monte Carlo Results |
| title_full_unstemmed | The Effect of Faking on the Correlation Between Two Ordinal Variables: Some Population and Monte Carlo Results |
| title_short | The Effect of Faking on the Correlation Between Two Ordinal Variables: Some Population and Monte Carlo Results |
| title_sort | effect of faking on the correlation between two ordinal variables some population and monte carlo results |
| topic | Pearson correlation Spearman correlation sample generation by replacement (SGR) fake ordinal/discrete data population analysis Monte Carlo simulations |
| url | https://www.frontiersin.org/article/10.3389/fpsyg.2018.01876/full |
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