| 1. |
- Carlsson, Rickard, 1984-, et al.
(författare)
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A Primer on the benefits of differential treatment analysis when predicting discriminatory behavior
- 2018
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Ingår i: The Quantitative Methods for Psychology. - Ottawa : University of Ottawa. - 2292-1354. ; 14:3, s. 193-198
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Tidskriftsartikel (refereegranskat)abstract
- A central question in social psychology is to what extent individual differences in attitudes, prejudices, and stereotypes can predict discriminatory behavior. This is often studied by simply regressing a measure of behavior toward a single group (e.g., behavior toward Black people only) onto the predictors (e.g., attitude measures). In the present paper, we remind researchers that an analysis focusing on predicting the differential treatment (e.g., behavior towards Black people vs. White people) has a higher conceptual validity and will result in more informative effect sizes. The paper is concluded with a list of suggestions for future research on the link between attitudes, prejudices, stereotypes and discrimination.
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| 2. |
- Johansson, Tobias
(författare)
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Generating artificial social networks
- 2019
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Ingår i: The Quantitative Methods for Psychology. - : The Quantitative Methods for Psychology. - 1017-3455 .- 2292-1354. ; 15:2, s. 56-74
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Tidskriftsartikel (refereegranskat)abstract
- The study of complex social networks is an inherently interdisciplinary research area with applications across many fields, including psychology. Social network models describe, illustrate and explain how people are connected to each other and can, for example, be used to study information spread and interconnectedness of people with different kinds of traits. One approach to social network modelling, originating mainly in the physics literature, is to generate targeted kinds of social networks using models with specialized mechanisms while analyzing and deriving features of the models. Surprisingly though, and despite the popularity of this approach, there is no available functionality for generating a wide variety of social networks from these models. Thus, researchers are left to implement and specify these models themselves, restricting the applicability of these models. In this article, I provide a set of Matlab functions enabling the generation of artificial social networks from 22 different network models, most of them explicitly designed to capture features of social networks. Many of these models originate in the physics literature and may therefore not be familiar to psychological researchers. I also provide an illustration of how these models can be evaluated in terms of a simulated model comparison approach and how they can be applied to psychological research. With the already existing network functionality available in Matlab and other languages, this should provide a useful extension to researchers.
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| 3. |
- Zhao, Xiang, PhD, 1987-
(författare)
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Displaying Latent Classes in Figures : Consideration of Practices
- 2023
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Ingår i: The Quantitative Methods for Psychology. - : Universite de Montreal. - 2292-1354. ; 19:2, s. 165-172
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Tidskriftsartikel (refereegranskat)abstract
- While latent class analysis (LCA) has gained popularity in social sciences, including psychology, the visualization of latent classes has been the subject of limited discussions. This article reviews the elements of LCA figures, covering issues such as graph type, axis labels, and subgroup naming. Bar charts and line graphs have been identified as two major visualization approaches in LCA studies. It is concluded that LCA figures serve as an important visual vehicle to display subgroup characteristics. However, the elements of LCA figures need careful consideration as they could furnish the text with additional information. A checklist is summarized for LCA figure preparation.
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