Contrasts and Correlations in Effect-Size Estimation

Rosnow, Ralph L.; Rosenthal, Robert; Rubin, Donald B.

doi:10.1111/1467-9280.00287

Cited by 449 publications

(284 citation statements)

References 21 publications

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“…To study the construct validity of CBD, subjects with and without CBD were compared on a series of continuous and dichotomous variables, using independent sample t-tests and Chi-square significance tests respectively. Effect sizes were calculated using the Odds Ratio's (ORs) and their 95% CI for categorical variables, and Cohen's d for continuous variables (Rosnow et al, 2000). Cohen's d is defined as the difference between two means divided by the pooled standard deviation.…”

Section: Discussionmentioning

confidence: 99%

Prevalence and construct validity of compulsive buying disorder in shopping mall visitors

Maráz

Brink

Demetrovics

2015

Psychiatry Research

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Compulsive buying is a relatively new psychopathological concept and very few data are currently available regarding the prevalence and validity of compulsive buying disorder. In this cross-sectional study, we establish the prevalence of compulsive buying disorder in shopping mall visitors and explore the construct validity of the concept using the revised version of the Edwards Compulsive Buying Scale in 1,441 shopping mall visitors looking at shopping habits, current substance use (smoking, alcohol and illicit drug) and various psychological characteristics. Overall,3) of our sample was classified as having a compulsive buying disorder. Compulsive buyers were younger, less educated and more likely to be female than non-compulsive buyers. They were also more likely to have used licit and illicit substances. Compulsive buyers also reported higher levels of impulsivity and obsessive-compulsive symptoms, lower levels of well-being and self-esteem and more psychological distress. Finally, compulsive buyers were five times more likely to meet criteria for borderline personality disorder than non-compulsive buyers. Compulsive buying is a frequent disorder in shopping mall visitors and is associated with important and robust indicators of psychopathology thus supporting the validity of the construct. Dear Professor Buchsbaum, E ö t v ö s L or á n d U n i ve r s i t y * I ns t i t ut e of P sy c h ol o g y D E P A R T M E N T O F C L I N I C A L P S Y C H O L O G Y Aniko MarazWe would like the attached manuscript titled 'Prevalence and construct validity of compulsive buying disorder in shopping mall visitors' to be considered for publication in Psychiatry Research. Although there is considerable research regarding in the field of compulsive buying behavior, the prevalence of the validity of the disorder are relatively unknown. In this cross-sectional study, 8.7% of our sample (n=1441) had compulsive buying disorder and we found that compulsive buying is associated with important and robust indicators of psychopathology thus supporting the validity of the construct.The authors (Aniko Maraz, Wim van den Brink, Zsolt Demetrovics) declare that they do not have any interests that could constitute a real, potential or apparent conflict of interest with respect to his/her involvement in the publication. The authors also declare that they do not have any financial or other relations (e.g. directorship, consultancy or speaker fee) with companies, trade associations, unions or groups (including civic associations and public interest groups) that may gain or lose financially from the results or conclusions in the study.The study was exclusively carried out by the investigators. The authors of this manuscript had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. This paper has not been, nor will be, published in whole or in part by any other journal and is under consideration for publication elsewhere. The submission to your journal ha...

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Section: Discussionmentioning

confidence: 99%

Prevalence and construct validity of compulsive buying disorder in shopping mall visitors

Maráz

Brink

Demetrovics

2015

Psychiatry Research

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“…The Pearson correlation coefficient (r) was calculated for each association. The r value is considered to be the preferred index in a correlational design (Durlak, 2009), with r being widely used to represent effect size in terms of the magnitude and direction of the relationship between the variables (Rosnow, Rosenthal, & Rubin, 2000). The R 2 values and the unstandardized coefficients for the y-intercept and learning approach score were also calculated.…”

Section: Discussionmentioning

confidence: 99%

Use of Lecture Capture in Undergraduate Biological Science Education

Wiese

Newton

2013

cjsotl-rcacea

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This study examined the use of lecture capture in students in a large 3rd year undergraduate biological science course at the University of Guelph. Data regarding viewing behaviour, academic performance, and attendance were analyzed in relation to student learning approach (as assessed by the R-SPQ-2F), gender, and year of post-secondary education. It was found that relative to historic controls, students provided lecture capture videos increased their final exam grade by approximately 5%. It was also found that learning approach was significantly related to video viewing behaviour, final exam performance, and attendance, with a deep learning approach being associated with more video views, better performance, and a greater tendency to watch videos to master and review material. A surface approach showed contrasting associations. Moreover, a higher deep approach score was related to fewer absences, while a higher surface approach score was related to more absences and increased the likelihood of a student missing a class. Gender also influenced viewing behaviour, with females being more likely than males to watch videos to generate notes and to review material. This research demonstrates that learning approach and gender are significant predictors of lecture capture behaviour, performance, and/or attendance in biological science education, and provides support for the use of lecture capture as a tool to improve academic performance. Cette étude examine l’utilisation de la capture de cours dans une grande classe d’étudiants de premier cycle inscrits à un cours de sciences biologiques de troisième année. Les données relatives au comportement de visionnement des vidéos, aux résultats académiques et à l’assiduité ont été analysées en relation avec l’approche d’apprentissage des étudiants (telle que mesurée par le R-SPQ-2F), le sexe et l’année d’études post-secondaires. Cette étude a montré que, comparativement aux contrôles historiques, les notes obtenues aux examens finals par les étudiants exposés à des vidéos académiques étaient de 5 % supérieures. L’étude a également indiqué que l’approche d’apprentissage était liée de façon significative au comportement de visionnement, aux résultats obtenus aux examens finals et à l’assiduité, et que l’approche en profondeur était liée à un nombre supérieur de visionnements des vidéos, à de meilleurs résultats et à une tendance accrue à regarder les vidéos afin de maîtriser et de réviser la matière. L’approche en surface a indiqué des associations contrastées. De plus, un score supérieur d’approche en profondeur était lié à un nombre moins élevé d’absences alors que l’approche en surface était liée à davantage d’absences et qu’elle augmentait les possibilités que les étudiants soient absents en classe. Le sexe avait également une incidence sur le comportement de visionnement, les femmes ayant davantage tendance à regarder les vidéos afin de prendre des notes et de réviser la matière que les hommes. Cette recherche a montré que l’approche d’apprentissage et le sexe sont des indicateurs importants de comportement en ce qui concerne la capture de cours, la performance et/ou l’assiduité dans le domaine des sciences biologiques et qu’elle offre un soutien efficace pour l’utilisation de la capture de cours en tant qu’outil pour améliorer la performance académique.

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“…In the context of assessing mean effects, mean differences, and contrasts more generally, r can be interpreted as the partial correlation between the contrast weights and scores on the outcome variable, after having removed the group differences other than those associated with the contrast (Rosnow, Rosenthal, & Rubin, 2000). One benefit of expressing an effect size for a contrast as r is that it scales the effect to have an absolute magnitude between 0 and 1, with the sign indicating whether the trend in the group differences was in the direction predicted by the contrast weights or in the opposite direction.…”

Section: Pearson's Rmentioning

confidence: 99%

“…1. (This is algebraically equivalent to computing r from a t-test of a contrast as illustrated in Rosnow et al, 2000.) For a single mean effect, M, Eq.…”

Section: Pearson's Rmentioning

confidence: 99%

BootES: An R package for bootstrap confidence intervals on effect sizes

2013

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Bootstrap Effect Sizes (bootES; Gerlanc & Kirby, 2012) is a free, open-source software package for R (R Development Core Team, 2012), which is a language and environment for statistical computing. BootES computes both unstandardized and standardized effect sizes (such as Cohen's d, Hedges's g, and Pearson's r) and makes easily available for the first time the computation of their bootstrap confidence intervals (CIs). In this article, we illustrate how to use bootES to find effect sizes for contrasts in between-subjects, withinsubjects, and mixed factorial designs and to find bootstrap CIs for correlations and differences between correlations. An appendix gives a brief introduction to R that will allow readers to use bootES without having prior knowledge of R.

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Contrasts and Correlations in Effect-Size Estimation

Cited by 449 publications

References 21 publications

Prevalence and construct validity of compulsive buying disorder in shopping mall visitors

Prevalence and construct validity of compulsive buying disorder in shopping mall visitors

Use of Lecture Capture in Undergraduate Biological Science Education

BootES: An R package for bootstrap confidence intervals on effect sizes

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