Replication of Facing Discomfort: Avoided Negative Affect Shapes the Acknowledgment of Systemic Racism by Murray & Koopmann-Holm (2024, Emotion)

Introduction

Murray & Koopmann-Holm (2024) https://doi.org/10.1037/emo0001364 contributed a compelling finding to the broader literature on how cultural norms and emotional processes can impact how Americans make sense of racial inequality. The paper cites previous findings on how culture can influence the types of emotions that are considered socially acceptable or valuable, and in turn, individuals’ emotional processes. In Study 2 of the paper, the authors found that such culturally-driven emotional processes, specifically, the desire to avoid negative emotions (i.e., Avoided Negative Affect, ANA) in the United States, inhibited people’s perception of racism as embedded throughout America’s historical and current policies and practices. The results of this study identified a key barrier toward the development of a systemic understanding of racism in the United States, a type of understanding that has been shown to carry implications for support for racially progressive policies and other social change efforts. As my research focuses on these sorts of barriers, namely, the psychological factors that deter or compel Americans to address racial inequality, including the role of affect and American cultural values, this finding is particularly relevant. Ultimately, I aim to design interventions that incentivize White Americans to participate in racial equity efforts, or at the very least, to see racial inequality as a pressing, enduring issue, and this finding provides some of the foundation for that work, with its emphasis on the power of cultural context and the suppression of particular emotions.

To replicate this study, I will administer an online survey involving 3 conditions, an increased Avoided Negative Affect (ANA) condition, a decreased ANA condition, and a control condition. A copy of the full survey was made available online, and all 3 conditions are brief, written manipulations which did not require any advanced formatting or tools. I will randomly assign participants to one of the three conditions. After they review their assigned manipulation, they will respond to a series of measures, many of which are multi-item scales. They will answer questions about the emotions they feel in the current moment, the extent to which a series of incidents (some interpersonal, some systemic or institutional) reflect racism, how frequently they engage in behaviors or habits that reflect social desirability, their demographics (e.g., political ideology, gender, age, race, major, etc.), and what they thought the study was about. Participants will be debriefed slightly differently based on the condition they were assigned to since the first sentence of the debrief explained the manipulation from the beginning of the survey. The GitHub repository for this replication project can be found here: https://github.com/psych251/murray2024.

I predict a few challenges with this replication. First, for the original study, the authors recruited undergraduate students from one university, which could indicate that effects are constrained to a niche population, and certain aspects of the survey may become inapplicable (e.g., demographic questions having to do with participants’ majors and school years). Also, the sample was racially diverse, and in an online sample, it might be more difficult to obtain the same racial composition. In the original analysis, the authors had to control for many factors, hence the need for so many survey measures. One challenge that could stem from this is that the dataset and statistical models could be cumbersome to handle. Finally, given the current political climate and perceptions of race-focused conversations, some participants in this replication study might be more hostile or more hesitant to respond to measures focused on these topics, which could lead to missing data and the need for exclusions. Further, the sample size is already quite small, so there is a possibility that too many exclusions could leave the replication study under-powered.

Methods

Paradigm link: https://stanforduniversity.qualtrics.com/jfe/form/SV_4IMpjoGrnAre61M

Power Analysis

Original effect size, power analysis for samples to achieve 80%, 90%, 95% power to detect that effect size. Considerations of feasibility for selecting planned sample size.

Planned Sample

Planned sample size and/or termination rule, sampling frame, known demographics if any, preselection rules if any.

Materials

All materials - can quote directly from original article - just put the text in quotations and note that this was followed precisely. Or, quote directly and just point out exceptions to what was described in the original article.

Procedure

Can quote directly from original article - just put the text in quotations and note that this was followed precisely. Or, quote directly and just point out exceptions to what was described in the original article.

Analysis Plan

Can also quote directly, though it is less often spelled out effectively for an analysis strategy section. The key is to report an analysis strategy that is as close to the original - data cleaning rules, data exclusion rules, covariates, etc. - as possible.

Clarify key analysis of interest here You can also pre-specify additional analyses you plan to do.

Differences from Original Study

Explicitly describe known differences in sample, setting, procedure, and analysis plan from original study. The goal, of course, is to minimize those differences, but differences will inevitably occur. Also, note whether such differences are anticipated to make a difference based on claims in the original article or subsequent published research on the conditions for obtaining the effect.

Methods Addendum (Post Data Collection)

You can comment this section out prior to final report with data collection.

Actual Sample

Sample size, demographics, data exclusions based on rules spelled out in analysis plan

Differences from pre-data collection methods plan

Any differences from what was described as the original plan, or “none”.

Results

Data preparation

Data preparation following the analysis plan.

Confirmatory analysis

The analyses as specified in the analysis plan.

#### fit model
conf_model <- lm(
  Systemic_Racism_composite ~ Condition + Isolated_Racism_composite + Pol_num + Age_num,
  data = data
)

summary(conf_model)

Call:
lm(formula = Systemic_Racism_composite ~ Condition + Isolated_Racism_composite + 
    Pol_num + Age_num, data = data)

Residuals:
ALL 3 residuals are 0: no residual degrees of freedom!

Coefficients: (3 not defined because of singularities)
                                Estimate Std. Error t value Pr(>|t|)
(Intercept)                       4.9167        NaN     NaN      NaN
ConditionDecrease ANA Condition   0.9167        NaN     NaN      NaN
ConditionIncrease ANA Condition  -0.3333        NaN     NaN      NaN
Isolated_Racism_composite             NA         NA      NA       NA
Pol_num                               NA         NA      NA       NA
Age_num                               NA         NA      NA       NA

Residual standard error: NaN on 0 degrees of freedom
Multiple R-squared:      1, Adjusted R-squared:    NaN 
F-statistic:   NaN on 2 and 0 DF,  p-value: NA

#### contrasts 

emm_options(nesting = NULL)  # clear any inferred nesting

emm <- emmeans(
  conf_model, ~ Condition,
  at = list(
    Isolated_Racism_composite = mean(data$Isolated_Racism_composite, na.rm = TRUE),
    Pol_num = mean(data$Pol_num, na.rm = TRUE),
    Age_num = mean(data$Age_num, na.rm = TRUE)
  )
)

contrast_list <- list(
  increase_vs_control  = c( 1,  0, -1),
  increase_vs_decrease = c( 1, -1,  0)
)

contrast(emm, contrast_list, adjust = "bonferroni") |> summary(infer = TRUE)

 contrast             estimate SE df asymp.LCL asymp.UCL z.ratio p.value
 increase_vs_control    nonEst NA NA        NA        NA      NA      NA
 increase_vs_decrease   nonEst NA NA        NA        NA      NA      NA

Confidence level used: 0.95 
Conf-level adjustment: bonferroni method for 1 estimates 
P value adjustment: bonferroni method for 1 tests 

# note: model code and contrasts are correct just insufficient observations to run 

Side-by-side graph with original graph is ideal here

Exploratory analyses

Any follow-up analyses desired (not required).

Discussion

Summary of Replication Attempt

Open the discussion section with a paragraph summarizing the primary result from the confirmatory analysis and the assessment of whether it replicated, partially replicated, or failed to replicate the original result.

Commentary

Add open-ended commentary (if any) reflecting (a) insights from follow-up exploratory analysis, (b) assessment of the meaning of the replication (or not) - e.g., for a failure to replicate, are the differences between original and present study ones that definitely, plausibly, or are unlikely to have been moderators of the result, and (c) discussion of any objections or challenges raised by the current and original authors about the replication attempt. None of these need to be long.