scholarly journals Mixing the implicit: A Linear Mixed-Effects Models approach for a Rasch analysis of the Implicit Association Test and the Single Category Implicit Association Test

2021 ◽  
Author(s):  
Ottavia M. Epifania ◽  
Pasquale Anselmi ◽  
Egidio Robusto
Keyword(s):  
Social Sciences ◽  
Implicit Association Test ◽  
Rasch Analysis ◽  
Association Test ◽  
Mixed Effects Models ◽  
Implicit Association ◽  
Implicit Measures ◽  
Linear Mixed Effects Models ◽  
Linear Mixed Effects ◽  
Single Category

<div>The indirect investigation of psychological constructs has become prominent in social sciences thanks to the so-called implicit measures. Different implicit measures can be administered concurrently to the same respondents for obtaining detailed and multifaceted information on the constructs of interest. In this study, a Rasch analysis of accuracy and time responses of two commonly used implicit measures is presented. The focus in on the concurrent administration of the Implicit Association Test (IAT; Greenwald et al., 1998) and the Single Category IAT (SC-IAT; Karpinski & Steinman, 2006). Linear Mixed-Effects Models are used to address the within– and between–measures sources of variability and to obtain a Rasch parametrization of the data. By disentangling the respondent’s contribution from the stimulus contribution to the observed responses, these models allow for delving deeper on the functioning of the IAT and the SC-IAT, as well as for grasping a better understanding of the processes driving a behavioral decision. Implications of the results for social sciences and future research directions are discussed.</div>

scholarly journals Mixing the implicit: A Linear Mixed-Effects Models approach for a Rasch analysis of the Implicit Association Test and the Single Category Implicit Association Test

2021 ◽  
Author(s):  
Ottavia M. Epifania ◽  
Pasquale Anselmi ◽  
Egidio Robusto
Keyword(s):  
Social Sciences ◽  
Implicit Association Test ◽  
Rasch Analysis ◽  
Association Test ◽  
Mixed Effects Models ◽  
Implicit Association ◽  
Implicit Measures ◽  
Linear Mixed Effects Models ◽  
Linear Mixed Effects ◽  
Single Category

<div>The indirect investigation of psychological constructs has become prominent in social sciences thanks to the so-called implicit measures. Different implicit measures can be administered concurrently to the same respondents for obtaining detailed and multifaceted information on the constructs of interest. In this study, a Rasch analysis of accuracy and time responses of two commonly used implicit measures is presented. The focus in on the concurrent administration of the Implicit Association Test (IAT; Greenwald et al., 1998) and the Single Category IAT (SC-IAT; Karpinski & Steinman, 2006). Linear Mixed-Effects Models are used to address the within– and between–measures sources of variability and to obtain a Rasch parametrization of the data. By disentangling the respondent’s contribution from the stimulus contribution to the observed responses, these models allow for delving deeper on the functioning of the IAT and the SC-IAT, as well as for grasping a better understanding of the processes driving a behavioral decision. Implications of the results for social sciences and future research directions are discussed.</div>

Intentional Faking of the Single Category Implicit Association Test and the Implicit Association Test

2011 ◽  
Vol 109 (1) ◽  
pp. 219-230 ◽  
Author(s):  
Stefan Stieger ◽  
Anja S. Göritz ◽  
Andreas Hergovich ◽  
Martin Voracek
Keyword(s):  
Implicit Association Test ◽  
Reaction Times ◽  
Association Test ◽  
Implicit Association ◽  
Implicit Measures ◽  
Specific Instruction ◽  
Single Attribute ◽  
Attribute Category ◽  
Single Category

The Implicit Association Test (IAT) provides a relative measure of implicit association strengths between target and attribute categories. In contrast, the Single Category Implicit Association Test (SC–IAT) measures association strength with a single attribute category. This can be advantageous if a complementary category—as used in the IAT—cannot be composed or is undesired. If the SC–IAT is to be a meaningful supplement to the IAT, it should meet the same requirements. In an online experiment with a large and heterogeneous sample, the fakability of both implicit measures was investigated when measuring anxiety. Both measures were fakable through specific instruction (e.g., “Slow down your reactions”) but unfakable through nonspecific faking instruction even though nonspecific instruction was given immediately before the critical blocks (e.g., “Alter your reaction times”). When comparing the methodological quality of both implicit measures, the SC–IAT had lower internal consistency than the IAT. Moreover, with specific faking instructions, the SC–IAT was possible to fake to a larger extent than the IAT.

How tasty is governance? Understanding the effect of a “cooperative” label on perceived taste of food products

2020 ◽  
Vol 35 (4) ◽  
pp. 23-44
Author(s):  
Axelle Faure-Ferlet ◽  
Sonia Capelli ◽  
William Sabadie
Keyword(s):  
Transfer Process ◽  
Implicit Association Test ◽  
Food Products ◽  
Association Test ◽  
Implicit Association ◽  
Implicit Measures ◽  
Explicit Measures ◽  
Cooperative Governance

This research investigates whether a label on cooperative governance influences the perceived taste of a product through a sensation transfer process. The first study measures perceived taste of unbranded products implicitly (via an Implicit Association Test) and explicitly (via a survey). The label improves the implicitly and explicitly perceived taste. The second study, reproducing the same protocols with branded products, confirms this result for implicitly perceived taste, but the effect of the label on explicitly perceived taste disappears. Because implicit measures are more predictive of routine purchasing than are explicit measures, we recommend spotlighting cooperative governance on food products.

scholarly journals The target/perpetrator brief-implicit association test (B-IAT): an implicit instrument for efficiently measuring discrimination based on race/ethnicity, sex, gender identity, sexual orientation, weight, and age

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Maddalena Marini ◽  
Pamela D. Waterman ◽  
Emry Breedlove ◽  
Jarvis T. Chen ◽  
Christian Testa ◽  
...  
Keyword(s):  
Sexual Orientation ◽  
Gender Identity ◽  
Implicit Association Test ◽  
Mental Representations ◽  
Association Test ◽  
Implicit Association ◽  
Implicit Measures ◽  
Race Ethnicity ◽  
Implicit And Explicit ◽  
Explicit Measures

Abstract Background To date, research assessing discrimination has employed primarily explicit measures (i.e., self-reports), which can be subject to intentional and social desirability processes. Only a few studies, focusing on sex and race/ethnicity discrimination, have relied on implicit measures (i.e., Implicit Association Test, IAT), which permit assessing mental representations that are outside of conscious control. This study aims to advance measurement of discrimination by extending the application of implicit measures to multiple types of discrimination and optimizing the time required for the administration of these instruments. Methods Between September 27th 2019 and February 9th 2020, we conducted six experiments (984 participants) to assess implicit and explicit discrimination based on race/ethnicity, sex, gender identity, sexual orientation, weight, and age. Implicit discrimination was measured by using the Brief-Implicit Association Test (B-IAT), a new validated version of the IAT developed to shorten the time needed (from ≈15 to ≈2 min) to assess implicit mental representations, while explicit discrimination was assessed using self-reported items. Results Among participants (mean age = 37.8), 68.6% were White Non-Hispanic; 69% were females; 76.1% were heterosexual; 90.7% were gender conforming; 52.8% were medium weight; and 41.5% had an advanced level of education. Overall, we found implicit and explicit recognition of discrimination towards all the target groups (stronger for members of the target than dominant groups). Some exceptions emerged in experiments investigating race/ethnicity and weight discrimination. In the racism experiment, only people of Color showed an implicit recognition of discrimination towards the target group, while White people were neutral. In the fatphobia experiment, participants who were not heavy showed a slight implicit recognition of discrimination towards the dominant group, while heavy participants were neutral. Conclusions This study provides evidence that the B-IAT is a valuable tool for quickly assessing multiple types of implicit discrimination. It shows also that implicit and explicit measures can display diverging results, thus indicating that research would benefit from the use of both these instruments. These results have important implications for the assessment of discrimination in health research as well as in social and psychological science.

scholarly journals A Diffusion Model Approach for Understanding the Impact of 17 Interventions on the Race Implicit Association Test

2020 ◽  
Author(s):  
Jessica Röhner ◽  
Calvin K. Lai
Keyword(s):  
Decision Making ◽  
Implicit Association Test ◽  
Association Test ◽  
Implicit Measure ◽  
Implicit Association ◽  
Implicit Measures ◽  
Long Term Effects ◽  
Sham Intervention ◽  
The Impact ◽  
Speed Accuracy

<p>Performance on implicit measures reflects construct-specific and non-construct-specific processes. This creates an interpretive issue for understanding interventions to change implicit measures: change in performance could reflect changes in the constructs-of-interest or changes in other mental processes. We re-analyzed data from six studies (<i>N</i> = 23,342) to examine the process-level effects of 17 interventions and one sham intervention to change race Implicit Association Test (IAT) performance. Diffusion models decompose overall IAT performance (<i>D</i>-scores) into construct-specific (ease of decision-making), and non-construct-specific processes (speed-accuracy tradeoffs, non-decision-related processes like motor execution). Interventions that effectively reduced <i>D-</i>scores changed ease of decision-making on compatible and incompatible trials. They also eliminated differences in speed-accuracy tradeoffs between compatible and incompatible trials. Non-decision-related processes were impacted by two interventions only. There was little evidence that interventions had any long-term effects. These findings highlight the value of diffusion modeling for understanding the mechanisms by which interventions affect implicit measure performance.</p>

Persuading the Implicit Mind: Changing Negative Implicit Evaluations With an 8-Minute Podcast

2021 ◽  
pp. 194855062110371
Author(s):  
Benedek Kurdi ◽  
Thomas C. Mann ◽  
Melissa J. Ferguson
Keyword(s):  
External Validity ◽  
Memory Retrieval ◽  
Implicit Association Test ◽  
Association Test ◽  
Implicit Association ◽  
Implicit Measures ◽  
Trophy Hunting ◽  
American Students

Implicit evaluations can be malleable via reinterpretation of previously encountered evidence. Here, we report three studies ( N = 1,007) investigating the robustness of this updating modality using ecologically realistic materials. Participants were first introduced to a target who killed an endangered black rhino in Namibia. They then listened to a real podcast providing counterattitudinal information on the benefits of trophy hunting. The podcast resulted in considerable revisions of initially negative implicit evaluations toward positivity, consistently across implicit measures (affect misattribution procedures vs. implicit association test), samples (American students vs. nonstudents from various countries), study settings (lab vs. online), and the presence versus absence of a memory retrieval manipulation prompting reflection on participants’ views on trophy hunting. Taken together, these findings suggest that reinterpretation can shift implicit evaluations of even highly negative targets, including under conditions of external validity.

The Brief Implicit Association Test

2009 ◽  
Vol 56 (4) ◽  
pp. 283-294 ◽  
Author(s):  
N. Sriram ◽  
Anthony G. Greenwald
Keyword(s):  
Implicit Association Test ◽  
Association Test ◽  
Implicit Association ◽  
Implicit Measures ◽  
Stimulus Response ◽  
Implicit Stereotypes ◽  
The Subject

The Brief Implicit Association Test (BIAT) consists of two blocks of trials with the same four categories and stimulus-response mappings as the standard IAT, but with 1/3 the number of trials. Unlike the standard IAT, the BIAT focuses the subject on just two of each block’s four categories. Experiments 1 and 2 demonstrated that attitude BIATs had satisfactory validity when good (but not bad) was a focal category, and that identity IATs had satisfactory validity when self (but not other) was a focal category. Experiment 2 also showed that a good-focal attitude BIAT and a self-focal identity BIAT were psychometrically similar to standard IAT measures of the same constructs. Experiment 3 presented each of six BIATs twice, showing that procedural variables had no more than minor influences on the resulting implicit measures. Experiment 4 further demonstrated successful use of the BIAT to measure implicit stereotypes.

scholarly journals Understanding and Using the Brief Implicit Association Test: Recommended Scoring Procedures

2016 ◽  
Author(s):  
Brian A. Nosek ◽  
Yoav Bar-Anan ◽  
Natarajan Sriram ◽  
Jordan Axt ◽  
Anthony G. Greenwald
Keyword(s):  
Implicit Association Test ◽  
Evaluation Criteria ◽  
Association Test ◽  
Group Differences ◽  
Implicit Association ◽  
Implicit Measures ◽  
Average Response Time ◽  
Transformation Algorithms ◽  
Criterion Variables ◽  
Explicit Measures

A brief version of the Implicit Association Test (BIAT) has been introduced. The present research identified analytical best practices for overall psychometric performance of the BIAT. In 7 studies and multiple replications, we investigated analytic practices with several evaluation criteria: sensitivity to detecting known effects and group differences, internal consistency, relations with implicit measures of the same topic, relations with explicit measures of the same topic and other criterion variables, and resistance to an extraneous influence of average response time. The data transformation algorithms D outperformed other approaches. This replicates and extends the strong prior performance of D compared to conventional analytic techniques. We conclude with recommended analytic practices for standard use of the BIAT.

scholarly journals Measure What You are Trying to Predict: Applying the Correspondence Principle to the Implicit Association Test

2019 ◽  
Author(s):  
Louis H. Irving ◽  
Colin Smith
Keyword(s):  
Predictive Validity ◽  
Implicit Association Test ◽  
Correspondence Principle ◽  
Association Test ◽  
Future Research ◽  
Implicit Association ◽  
Implicit Measures ◽  
Total N ◽  
Single Target ◽  
Explicit Measures

The Implicit Association Test (IAT) is nearly synonymous with the implicit attitude construct. At the same time, correlations between the IAT and criterion measures are often remarkably low. Developed within research using explicit measures of attitudes, the correspondence principle posits that measures should better predict criteria when there is a match in terms of the level of generality or specificity at which both are conceptualized (Ajzen &amp; Fishbein, 1977). As such, weak implicit-criterion correlations are to be expected when broad general implicit measures are used to predict highly specific criteria. Research using explicit measures of attitudes consistently supports the correspondence principle, but conceptual correspondence is rarely considered by researchers using implicit measures to predict behavior and other relevant criterion measures. In five experiments (total N = 4650), we provide the first direct evidence demonstrating the relevance of the correspondence principle to the predictive validity of the IAT and Single Target IAT. That said, it is not the case that the IAT always predicts criteria better when correspondence is high. Inconsistency across the pattern of results suggests there is much more that remains to be understood about the relevance of the correspondence principle to the implicit-criterion relationship. Taken together, however, our findings suggest that conceptual correspondence typically increases (and never decreases) the magnitude of implicit-behavior and implicit-explicit relationships. We provide a framework for future research necessary to establish when correspondence is more likely to increase the predictive validity of measures such as the IAT.

scholarly journals A Diffusion Model Approach for Understanding the Impact of 17 Interventions on the Race Implicit Association Test

2020 ◽  
pp. 014616722097448
Author(s):  
Jessica Röhner ◽  
Calvin K. Lai
Keyword(s):  
Decision Making ◽  
Implicit Association Test ◽  
Association Test ◽  
Implicit Measure ◽  
Implicit Association ◽  
Implicit Measures ◽  
Long Term Effects ◽  
Trade Offs ◽  
The Impact ◽  
Speed Accuracy

Performance on implicit measures reflects construct-specific and nonconstruct-specific processes. This creates an interpretive issue for understanding interventions to change implicit measures: Change in performance could reflect changes in the constructs of interest or changes in other mental processes. We reanalyzed data from six studies ( N = 23,342) to examine the process-level effects of 17 interventions and one sham intervention to change race implicit association test (IAT) performance. Diffusion models decompose overall IAT performance ( D-scores) into construct-specific (ease of decision-making) and nonconstruct-specific processes (speed–accuracy trade-offs, non-decision-related processes like motor execution). Interventions that effectively reduced D-scores changed ease of decision-making on compatible and incompatible trials. They also eliminated differences in speed–accuracy trade-offs between compatible and incompatible trials. Non-decision-related processes were affected by two interventions only. There was little evidence that interventions had any long-term effects. These findings highlight the value of diffusion modeling for understanding the mechanisms by which interventions affect implicit measure performance.

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