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.

The effect of faking on the correlation between two ordinal variables: some population and Monte Carlo results / Bressan, Marco; Rosseel, Yves; Lombardi, Luigi. - In: FRONTIERS IN PSYCHOLOGY. - ISSN 1664-1078. - STAMPA. - 9:10(2018), pp. 1876-1889. [10.3389/fpsyg.2018.01876]

The effect of faking on the correlation between two ordinal variables: some population and Monte Carlo results

Bressan, Marco;Lombardi, Luigi
2018

Abstract

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.
10
Bressan, Marco; Rosseel, Yves; Lombardi, Luigi
The effect of faking on the correlation between two ordinal variables: some population and Monte Carlo results / Bressan, Marco; Rosseel, Yves; Lombardi, Luigi. - In: FRONTIERS IN PSYCHOLOGY. - ISSN 1664-1078. - STAMPA. - 9:10(2018), pp. 1876-1889. [10.3389/fpsyg.2018.01876]
File in questo prodotto:
File Dimensione Formato  
fpsyg-09-01876.pdf

accesso aperto

Descrizione: Articolo principale
Tipologia: Versione editoriale (Publisher’s layout)
Licenza: Creative commons
Dimensione 2.06 MB
Formato Adobe PDF
2.06 MB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione

Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11572/218663
Citazioni
  • ???jsp.display-item.citation.pmc??? 2
  • Scopus 3
  • ???jsp.display-item.citation.isi??? 3
social impact