The Racial and Ethnic Microaggressions Scale (REMS): construction, reliability, and validity.
01 Oct 2011-Journal of Counseling Psychology (American Psychological Association)-Vol. 58, Iss: 4, pp 470-480
TL;DR: Analysis indicate that the REMS is a valid measure of racial microaggressions, as evidenced by high correlations with existing measures of racism and participants' feedback.
Abstract: Racial microaggressions are subtle statements and behaviors that unconsciously communicate denigrat-ing messages to people of color. In recent years, a theoretical taxonomy and subsequent qualitativestudies have introduced the types of microaggressions that people of color experience. In the presentstudy, college- and Internet-based samples of African Americans, Latina/os, Asian Americans, andmultiracial participants ( N 661) were used to develop and validate the Racial and Ethnic Microag-gression Scale (REMS). In Study 1, an exploratory principal-components analyses ( n 443) yielded a6-factormodel:(a)AssumptionsofInferiority,(b)Second-ClassCitizenandAssumptionsofCriminality,(c) Microinvalidations, (d) Exoticization/Assumptions of Similarity, (e) Environmental Microaggres-sions, and (f) Workplace and School Microaggressions, with a Cronbachs alpha of .912 for the overallmodel and subscales ranging from .783 to .873. In Study 2, a confirmatory factor analysis ( n 218)supported the 6-factor model with a Cronbachs alpha of .892. Further analyses indicate that the REMSis a valid measure of racial microaggressions, as evidenced by high correlations with existing measuresof racism and participants feedback. Future research directions and implications for practice arediscussed.Keywords: microaggressions, racism, discrimination, scale construction
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TL;DR: In this paper, the authors examined the relationship between racial microaggressions (subtle and unintentional forms of racial discrimination) and mental health and found that higher frequencies of racial micro-agggressions negatively predicted participants' mental health.
Abstract: This study examined the relationship between racial microaggressions (subtle and unintentional forms of racial discrimination) and mental health. Results from a large sample (N = 506) indicated that higher frequencies of racial microaggressions negatively predicted participants' mental health and that racial microaggressions were significantly correlated with depressive symptoms and negative affect. Differences in the types of microaggressions experienced by various racial groups (Asian, Latina/o, Black, White, and multiracial) and counseling implications are discussed.
341 citations
Cites background or methods from "The Racial and Ethnic Microaggressi..."
...This coding system was used because previous literature had observed that forcing individuals to choose preset boxes could be viewed as a microaggression in itself (Johnston & Nadal, 2010; Nadal, 2011)....
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...More recently, there has been an increase in research focusing specifically on racial microaggressions, with results showing that these subtle forms of discrimination have a detrimental impact on the mental health of people of color (Nadal, 2011; D. W. Sue, 2010)....
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...86, and it is positively correlated with the Daily Life Experiences–Frequency scale (r = .698, N = 253, p < .001; Nadal, 2011)....
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TL;DR: A review of racial microaggressions research literature in psychology since 2007 suggests that important conceptual and methodological issues remain to be addressed in the three domains.
Abstract: Since the publication of Sue et al. (Am Psychol 62:271–286, 2007a, b) seminal article, there has been an enormous scholarly interest in psychology on this construct of racial microaggressions—subtle everyday experiences of racism. In this paper, we provide a review of racial microaggressions research literature in psychology since 2007, following the publication of the first comprehensive taxonomy of racial microaggressions, which provided a conceptual framework and directions for research related to racial microaggressions. However, our review suggests that important conceptual and methodological issues remain to be addressed in the three domains: (1) what are racial microaggressions and who do they impact; (2) why are racial microaggressions important to examine; and (3) how are racial microaggressions currently studied and how might we improve the methodologies used to study racial microaggressions. We propose recommendations to further facilitate racial microaggressions research, improve the scientific rigor of racial microaggressions research, and contribute toward a more complete and sophisticated understanding of the concept and consequences of racial microaggressions—a construct that is undoubtedly salient and psychologically relevant among many members of racial minority groups.
297 citations
TL;DR: In this paper, the authors used a qualitative method with transgender female and male participants (N = 9) to identify types of microaggressions, or subtle forms of discrimination, that transgender people experience.
Abstract: This study utilized a qualitative method with transgender female and male participants (N = 9) to identify types of microaggressions, or subtle forms of discrimination, that transgender people experience. Twelve categories of microaggressions were identified: (a) use of transphobic and/or incorrectly gendered terminology, (b) assumption of universal transgender experience, (c) exoticization, (d) discomfort/disapproval of transgender experience, (e) endorsement of gender normative and binary culture or behaviors, (f) denial of existence of transphobia, (g) assumption of sexual pathology/abnormality, (h) physical threat or harassment, (i) denial of individual transphobia, (j) denial of bodily privacy, (k) familial microaggressions, and (l) systemic and environmental microaggressions. Implications for counseling are discussed.
286 citations
Cites background from "The Racial and Ethnic Microaggressi..."
...Many of these themes were similar or parallel to microaggressions experienced based on race (e.g., Nadal, 2011; Rivera, Forquer, & Rangel, 2010; Sue, Bucceri, et al., 2007; Sue, Capodilupo, et al., 2007, Sue et al., 2008), gender (Capodilupo et al., 2010), and sexual orientation (Nadal, Issa, et…...
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TL;DR: Data on the prevalence and psychological correlates of everyday racial microaggressions that reflect the Asian American experience and implications for racial microaggression research and clinical practice are presented.
Abstract: Although epidemiological studies and community surveys of Asian Americans have found that lifetime occurrences of racial discrimination are associated with increased risk for psychological morbidity, little is known about how exposure to racial discrimination is patterned in everyday life. Extrapolating from previous qualitative research (Sue, Bucceri, Lin, Nadal, & Torino, 2007), this study presents data on the prevalence and psychological correlates of everyday racial microaggressions that reflect the Asian American experience. Measures of positive affect, negative affect, somatic symptoms, and racial microaggressions were completed by 152 Asian Americans each day for up to 14 consecutive days. Approximately 78% of participants reported some form of racial microaggression within the 2-week study period. Multilevel analyses indicated that elevations in daily microaggressions, as well as greater microaggressions on average, predicted increases in somatic symptoms and negative affect. Implications of these findings for racial microaggression research and clinical practice are discussed.
278 citations
Cites background or result from "The Racial and Ethnic Microaggressi..."
...The effects of microinvalidations observed in the current study are especially significant, given that researchers have suggested that microinvalidations are more detrimental and harmful to people of color than either microassaults or microinsults (Nadal, 2011; Sue, 2010)....
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...In line with the Sue, Bucceri, et al. (2007) and Nadal (2011) findings discussed earlier, we hypothesized that Asian Americans are more likely to encounter microinvalidations that implicate themes of being treated as a perpetual foreigner or as an “alien in one’s own land” than they are other forms…...
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...Despite growing interest, much of the empirical research to date has focused on the assessment of individual differences (e.g., Nadal, 2011; Torres-Harding, Andrade, & Dias, 2012; Yoo, Steger, & Lee, 2010) and situational differences in microaggressions (e.g., Wang, Leu, & Shoda, 2011) and their…...
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...…distributions as a function of content classification revealed that microinvalidations (e.g., the assumption that all Asian Americans are foreign-born) made up the most common class of racial microaggression, a pattern consistent with previous research (Nadal, 2011; Sue, Bucceri, et al., 2007)....
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...Moreover, a recent measurement study by Nadal (2011) provides some evidence that microinvalidations that involve themes of xenophobia or being treated as a “perpetual foreigner” (Liang et al., 2004) are the most common class of microaggression experienced by Asian Americans....
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TL;DR: The Gendered Racial Microaggressions Scale (GRMS), was developed to assess both frequency and stress appraisal of microagressions, and was significantly related to psychological distress, such that greater perceived gendered racial microaggression were related to greater levels of reported psychological distress.
Abstract: The purpose of this study was to develop a measure of gendered racial microaggressions (i.e., subtle and everyday verbal, behavioral, and environmental expressions of oppression based on the intersection of one's race and gender) experienced by Black women by applying an intersectionality framework to Essed's (1991) theory of gendered racism and Sue, Capodilupo, et al.'s (2007) model of racial microaggressions. The Gendered Racial Microaggressions Scale (GRMS), was developed to assess both frequency and stress appraisal of microaggressions, in 2 separate studies. After the initial pool of GRMS items was developed, we received input from a community-based focus group of Black women and an expert panel. In Study 1, an exploratory factor analysis using a sample of 259 Black women resulted in a multidimensional scale with 4 factors as follows: (a) Assumptions of Beauty and Sexual Objectification, (b) Silenced and Marginalized, (c) Strong Black Woman Stereotype, and (d) Angry Black Woman Stereotype. In Study 2, results of confirmatory factor analyses using an independent sample of 210 Black women suggested that the 4-factor model was a good fit of the data for both the frequency and stress appraisal scales. Supporting construct validity, the GRMS was positively related to the Racial and Ethnic Microaggressions Scale (Nadal, 2011) and the Schedule of Sexist Events (Klonoff & Landrine, 1995). In addition, the GRMS was significantly related to psychological distress, such that greater perceived gendered racial microaggressions were related to greater levels of reported psychological distress. Implications for future research and practice are discussed.
273 citations
References
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TL;DR: In this article, the adequacy of the conventional cutoff criteria and several new alternatives for various fit indexes used to evaluate model fit in practice were examined, and the results suggest that, for the ML method, a cutoff value close to.95 for TLI, BL89, CFI, RNI, and G...
Abstract: This article examines the adequacy of the “rules of thumb” conventional cutoff criteria and several new alternatives for various fit indexes used to evaluate model fit in practice. Using a 2‐index presentation strategy, which includes using the maximum likelihood (ML)‐based standardized root mean squared residual (SRMR) and supplementing it with either Tucker‐Lewis Index (TLI), Bollen's (1989) Fit Index (BL89), Relative Noncentrality Index (RNI), Comparative Fit Index (CFI), Gamma Hat, McDonald's Centrality Index (Mc), or root mean squared error of approximation (RMSEA), various combinations of cutoff values from selected ranges of cutoff criteria for the ML‐based SRMR and a given supplemental fit index were used to calculate rejection rates for various types of true‐population and misspecified models; that is, models with misspecified factor covariance(s) and models with misspecified factor loading(s). The results suggest that, for the ML method, a cutoff value close to .95 for TLI, BL89, CFI, RNI, and G...
76,383 citations
Book•
01 Jan 1983
TL;DR: In this Section: 1. Multivariate Statistics: Why? and 2. A Guide to Statistical Techniques: Using the Book Research Questions and Associated Techniques.
Abstract: In this Section: 1. Brief Table of Contents 2. Full Table of Contents 1. BRIEF TABLE OF CONTENTS Chapter 1 Introduction Chapter 2 A Guide to Statistical Techniques: Using the Book Chapter 3 Review of Univariate and Bivariate Statistics Chapter 4 Cleaning Up Your Act: Screening Data Prior to Analysis Chapter 5 Multiple Regression Chapter 6 Analysis of Covariance Chapter 7 Multivariate Analysis of Variance and Covariance Chapter 8 Profile Analysis: The Multivariate Approach to Repeated Measures Chapter 9 Discriminant Analysis Chapter 10 Logistic Regression Chapter 11 Survival/Failure Analysis Chapter 12 Canonical Correlation Chapter 13 Principal Components and Factor Analysis Chapter 14 Structural Equation Modeling Chapter 15 Multilevel Linear Modeling Chapter 16 Multiway Frequency Analysis 2. FULL TABLE OF CONTENTS Chapter 1: Introduction Multivariate Statistics: Why? Some Useful Definitions Linear Combinations of Variables Number and Nature of Variables to Include Statistical Power Data Appropriate for Multivariate Statistics Organization of the Book Chapter 2: A Guide to Statistical Techniques: Using the Book Research Questions and Associated Techniques Some Further Comparisons A Decision Tree Technique Chapters Preliminary Check of the Data Chapter 3: Review of Univariate and Bivariate Statistics Hypothesis Testing Analysis of Variance Parameter Estimation Effect Size Bivariate Statistics: Correlation and Regression. Chi-Square Analysis Chapter 4: Cleaning Up Your Act: Screening Data Prior to Analysis Important Issues in Data Screening Complete Examples of Data Screening Chapter 5: Multiple Regression General Purpose and Description Kinds of Research Questions Limitations to Regression Analyses Fundamental Equations for Multiple Regression Major Types of Multiple Regression Some Important Issues. Complete Examples of Regression Analysis Comparison of Programs Chapter 6: Analysis of Covariance General Purpose and Description Kinds of Research Questions Limitations to Analysis of Covariance Fundamental Equations for Analysis of Covariance Some Important Issues Complete Example of Analysis of Covariance Comparison of Programs Chapter 7: Multivariate Analysis of Variance and Covariance General Purpose and Description Kinds of Research Questions Limitations to Multivariate Analysis of Variance and Covariance Fundamental Equations for Multivariate Analysis of Variance and Covariance Some Important Issues Complete Examples of Multivariate Analysis of Variance and Covariance Comparison of Programs Chapter 8: Profile Analysis: The Multivariate Approach to Repeated Measures General Purpose and Description Kinds of Research Questions Limitations to Profile Analysis Fundamental Equations for Profile Analysis Some Important Issues Complete Examples of Profile Analysis Comparison of Programs Chapter 9: Discriminant Analysis General Purpose and Description Kinds of Research Questions Limitations to Discriminant Analysis Fundamental Equations for Discriminant Analysis Types of Discriminant Analysis Some Important Issues Comparison of Programs Chapter 10: Logistic Regression General Purpose and Description Kinds of Research Questions Limitations to Logistic Regression Analysis Fundamental Equations for Logistic Regression Types of Logistic Regression Some Important Issues Complete Examples of Logistic Regression Comparison of Programs Chapter 11: Survival/Failure Analysis General Purpose and Description Kinds of Research Questions Limitations to Survival Analysis Fundamental Equations for Survival Analysis Types of Survival Analysis Some Important Issues Complete Example of Survival Analysis Comparison of Programs Chapter 12: Canonical Correlation General Purpose and Description Kinds of Research Questions Limitations Fundamental Equations for Canonical Correlation Some Important Issues Complete Example of Canonical Correlation Comparison of Programs Chapter 13: Principal Components and Factor Analysis General Purpose and Description Kinds of Research Questions Limitations Fundamental Equations for Factor Analysis Major Types of Factor Analysis Some Important Issues Complete Example of FA Comparison of Programs Chapter 14: Structural Equation Modeling General Purpose and Description Kinds of Research Questions Limitations to Structural Equation Modeling Fundamental Equations for Structural Equations Modeling Some Important Issues Complete Examples of Structural Equation Modeling Analysis. Comparison of Programs Chapter 15: Multilevel Linear Modeling General Purpose and Description Kinds of Research Questions Limitations to Multilevel Linear Modeling Fundamental Equations Types of MLM Some Important Issues Complete Example of MLM Comparison of Programs Chapter 16: Multiway Frequency Analysis General Purpose and Description Kinds of Research Questions Limitations to Multiway Frequency Analysis Fundamental Equations for Multiway Frequency Analysis Some Important Issues Complete Example of Multiway Frequency Analysis Comparison of Programs
53,113 citations
TL;DR: The authors delineate analytic procedures specific to each approach and techniques addressing trustworthiness with hypothetical examples drawn from the area of end-of-life care.
Abstract: Content analysis is a widely used qualitative research technique. Rather than being a single method, current applications of content analysis show three distinct approaches: conventional, directed, or summative. All three approaches are used to interpret meaning from the content of text data and, hence, adhere to the naturalistic paradigm. The major differences among the approaches are coding schemes, origins of codes, and threats to trustworthiness. In conventional content analysis, coding categories are derived directly from the text data. With a directed approach, analysis starts with a theory or relevant research findings as guidance for initial codes. A summative content analysis involves counting and comparisons, usually of keywords or content, followed by the interpretation of the underlying context. The authors delineate analytic procedures specific to each approach and techniques addressing trustworthiness with hypothetical examples drawn from the area of end-of-life care.
31,398 citations
TL;DR: In this article, a general null model based on modified independence among variables is proposed to provide an additional reference point for the statistical and scientific evaluation of covariance structure models, and the importance of supplementing statistical evaluation with incremental fit indices associated with the comparison of hierarchical models.
Abstract: Factor analysis, path analysis, structural equation modeling, and related multivariate statistical methods are based on maximum likelihood or generalized least squares estimation developed for covariance structure models. Large-sample theory provides a chi-square goodness-of-fit test for comparing a model against a general alternative model based on correlated variables. This model comparison is insufficient for model evaluation: In large samples virtually any model tends to be rejected as inadequate, and in small samples various competing models, if evaluated, might be equally acceptable. A general null model based on modified independence among variables is proposed to provide an additional reference point for the statistical and scientific evaluation of covariance structure models. Use of the null model in the context of a procedure that sequentially evaluates the statistical necessity of various sets of parameters places statistical methods in covariance structure analysis into a more complete framework. The concepts of ideal models and pseudo chi-square tests are introduced, and their roles in hypothesis testing are developed. The importance of supplementing statistical evaluation with incremental fit indices associated with the comparison of hierarchical models is also emphasized. Normed and nonnormed fit indices are developed and illustrated.
16,420 citations
Book•
01 Jan 1991
TL;DR: In this paper, the authors discuss the role of measurement in the social sciences and propose guidelines for scale development in the context of scale-based measurement. But, the authors do not discuss the relationship between scale scores and scale length.
Abstract: Chapter 1: Overview General Perspectives on Measurement Historical Origins of Measurement in Social Science Later Developments in Measurement The Role of Measurement in the Social Sciences Summary and Preview Chapter 2: Understanding the "Latent Variable" Constructs Versus Measures Latent Variable as the Presumed Cause of Item Values Path Diagrams Further Elaboration of the Measurement Model Parallel "Tests" Alternative Models Exercises Chapter 3: Reliability Continuous Versus Dichotomous Items Internal Consistency Relability Based on Correlations Between Scale Scores Generalizability Theory Summary and Exercises Chapter 4: Validity Content Validity Criterion-related Validity Construct Validity What About Face Validity? Exercises Chapter 5: Guidelines in Scale Development Step 1: Determine Clearly What it Is You Want to Measure Step 2: Generate an Item Pool Step 3: Determine the Format for Measurement Step 4: Have Initial Item Pool Reviewed by Experts Step 5: Consider Inclusion of Validation Items Step 6: Administer Items to a Development Sample Step 7: Evaluate the Items Step 8: Optimize Scale Length Exercises Chapter 6: Factor Analysis Overview of Factor Analysis Conceptual Description of Factor Analysis Interpreting Factors Principal Components vs Common Factors Confirmatory Factor Analysis Using Factor Analysis in Scale Development Sample Size Conclusion Chapter 7: An Overview of Item Response Theory Item Difficulty Item Discrimination False Positives Item Characteristic Curves Complexities of IRT When to Use IRT Conclusions Chapter 8: Measurement in the Broader Research Context Before the Scale Development After the Scale Administration Final Thoughts References Index About the Author
11,710 citations