On the predicted replicability of two decades of experimental research on system justification: A Z‐curve analysis

Sotola, Lukas K.; Credé, Marcus

doi:10.1002/ejsp.2858

Cited by 13 publications

(10 citation statements)

References 78 publications

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“…In the long run, 41% (95% CI [28%; 55%]) of the significant results are expected to be significant in replications (ERR). While these numbers may seem low, they are in line with previous applications of the z-curve (see e.g., Maier et al, 2023;Sotola & Credé, 2022;Syrjänen et al, 2021).…”

Section: Application Examplesupporting

confidence: 90%

A Toolbox to Evaluate the Trustworthiness of Published Findings

Adler¹,

Röseler²

2023

Preprint

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During the past few years, researchers have criticized their professions for providing an entry point for false-positive results arising from publication bias and questionable research practices such as p-hacking (i.e., selectively reporting analyses that yield a p-value below 5 %). Researchers are advocating replication studies and the implementation of open-science practices, like preregistration, in order to identify trustworthy effects. Nevertheless, because such consumer research developments are still emerging, most prior research findings have not been replicated, leaving researchers in the dark as to whether a line of research or a particular effect is trustworthy. We tackle this problem by providing a toolbox containing multiple heuristics to identify data patterns that might, from the information provided in published articles, indicate publication bias and p-hacking. Our toolbox is an easy-to-use instrument with which to initially assess a given set of findings.

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Section: Application Examplesupporting

confidence: 90%

A Toolbox to Evaluate the Trustworthiness of Published Findings

Adler¹,

Röseler²

2023

Preprint

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“…• Inclusion in traditional meta-analyses: With meta-analyses often struggling to include unpublished findings, replications, and null-findings, we believe that the Replication Database as a low-threshold opportunity to publish replication attempts can help researchers find studies that they can include in their meta-analyses and that may correct for the publication bias. • Validation data for bias-correction methods: Methods that predict replication rates or correct meta-analytical effect sizes for publication bias and questionable research practices are often evaluated using simulated data (e.g., Carter et al, 2019) and validations with existing data need to rely on few scattered large-scale projects (e.g., Sotola & Credé, 2022). With the replication database, these proposed methods can easily be tested against a large set of real data.…”

Section: (4) Reuse Potentialmentioning

confidence: 99%

The Replication Database: Documenting the Replicability of Psychological Science

Röseler,

Kaiser,

Doetsch

et al. 2024

Preprint

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In psychological science, replicability—repeating a study with a new sampleachieving consistent results (Parsons et al., 2022)—is critical for affirming the validity of scientific findings. Despite its importance, replication efforts are few and far between in psychological science with many attempts failing to corroborate past findings. This scarcity, compounded by the difficulty in accessing replication data, jeopardizes the efficient allocation of research resources and impedes scientific advancement. Addressing this crucial gap, we present the Replication Database (https://metaanalyses.shinyapps.io/replicationdatabase/), a novel platform hosting 1,239 original findings paired with replication findings. The infrastructure of this database allows researchers to submit, access, and engage with replication findings. The database makes replications visible, easily findable via a graphical user interface, and tracks replication rates across various factors, such as publication year or journal. This will facilitate future efforts to evaluate the robustness of psychological research.

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“…The idea is that supporting the status quo can satisfy people's basic psychological needs (i.e., epistemic, existential and relational), leading them to endorse attitudes that contradict their groups or self‐interest (for a demonstration, Jost et al., 2003). Like many theories, the research examining SJT's assumptions and predictions is sometimes contradictory (e.g., Brandt, 2013, 2020; Owuamalam et al., 2018; Sotola & Credé, 2022; Trump & White, 2018). Typically, contradicting evidence is counted as evidence against the theory, whereas supporting evidence is counted as evidence in favour of the theory.…”

Section: Introductionmentioning

confidence: 99%

“…System justification theory postulates that ‘people are motivated to defend, justify and bolster aspects of the status quo, including existing social economic and political systems, institutions and arrangements’ (Jost et al., 2015, p. 321). In support of this assumption, scholars have found, for example, that people engage in biased information processing in support of the system (Hennes et al., 2012) and that criticism of the system elicits defence mechanisms ((Jost & Hunyady, 2005); but see Sotola & Credé, 2022). Further, justifying prevailing systems has many implications for political attitudes and behaviours.…”

Section: Introductionmentioning

confidence: 99%

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Finding (dis‐)advantaged system justifiers: A bottom‐up approach to explore system justification theory

Kesberg,

Brandt,

Easterbrook

et al. 2023

Euro J Social Psych

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System Justification Theory (SJT) postulates that individuals are motivated to justify the status quo, including the paradoxical prediction that those who are disadvantaged (e.g., lower social status) by the prevailing system will justify the system more than those who are advantaged by the prevailing system. We test if this assumption holds in an entire sample, or only among subsets of a sample using a bottom‐up approach, Correlational Class Analysis. Using a representative sample from four European countries (Ntotal = 5157) we found six subpopulations. The first subpopulation (Justifiers, ntotal = 1256; 24%) was consistent with SJT, that is, system justification and social status were negatively correlated. The second subpopulation (Rejectors, ntotal = 1688; 33%), however, was characterized by a positive correlation between social status and system justification, which contrasts with the prediction of system justification theory. The other four subpopulations (Ntotal = 2211; 43%) were characterized by an ambivalent pattern. That is, at least one social status indicator, but not all, supported the prediction that disadvantaged individuals justified the system more than advantaged individuals. These heterogeneous patterns would be undetected using traditional approaches. Further, our results show that inequality salience is lower, trust in political institutions is higher, and support for political violence is higher among Justifiers compared to Rejectors. We discuss how understanding the interrelations between multiple indicators of social status and how they differ between subpopulations can help us to obtain a more comprehensive picture regarding under which circumstances and for whom system justification theory applies.

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On the predicted replicability of two decades of experimental research on system justification: A Z‐curve analysis

Cited by 13 publications

References 78 publications

A Toolbox to Evaluate the Trustworthiness of Published Findings

A Toolbox to Evaluate the Trustworthiness of Published Findings

The Replication Database: Documenting the Replicability of Psychological Science

Finding (dis‐)advantaged system justifiers: A bottom‐up approach to explore system justification theory

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