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One-way ANOVA and the Disgust Scale Revised (2012): Disgust Sensitivity and Religion

Dataset
By: Nick Allum Published: 2015 | Product: SAGE Research Methods Datasets
Data Type: Survey Experiment
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Abstract

This example introduces one-way ANOVA. This method allows researchers to compare the means of a single variable for more than two subsets of the data to evaluate whether the means for each subset are statistically significantly different from each other or not. This technique builds on the t-test for difference of means but extends it to the situation where there are more than two groups for which we wish to make comparisons. We illustrate this using a subset of data derived from a psychological study of disgust sensitivity: “Demographic influences on disgust: evidence from a heterogeneous sample”. This experiment was carried out in Israel in 2011 and 2012 by Uri Berger and David Anaki. Henceforth we refer to these data as DISGUST2012. Specifically, we test ...

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About This Dataset
Data Source Citation

Berger, U., & Anaki, D. (2015). Data from “demographic influences on disgust: evidence from a heterogeneoussSample”. Journal of Open Psychology Data, 3: e1. doi: http://dx.doi.org/10.5334/jopd.ag

Full title of originating dataset

Data from “Demographic Influences on Disgust: Evidence from a Heterogeneous Sample”

Data author(s) and affiliations

Uri Berger, Department of Psychology, Bar-Ilan University, Ramat-Gan, Israel

David Anaki, Department of Psychology and Gonda Interdisciplinary Center for Brain Research, Bar-Ilan University, Ramat-Gan, Israel

First publication date

05/09/2014

Funding sources/suppliers

Israeli Science Foundation grant 841/10 to D. Anaki.

Sample/sampling procedures

Convenience

Data collection dates

10/2011-06/2012

Time frame of analysis

2011–12

Unit of analysis

Adult Israeli residents

Location covered by data

Israel (Tel-Aviv, Jerusalem, Haifa, Be'er-Sheba)

Links to SRM content
List of variables

Religion

Mean_general_ds

Mean_Contamination

Methods Map
One-way analysis of variance