racial discrimination research paper

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Racial discrimination research paper dissertation finance

Racial discrimination research paper

Most Americans say, as a country, we have yet to achieve racial equality.

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Essay college Sense of Coherence Questionnaire One possibility awesome college essays sampling error; however, given the relatively racial discrimination research paper sample sizes, we believe sampling error alone is unlikely to account for the drastic differences. Patterns of perceived discrimination are important findings in their own right, as research shows that those who perceive high levels of discrimination are more likely to experience depression, anxiety, and other negative health outcomes Kessler et al. Drug use, drug arrests, and the question of race: lessons from Seattle. Studies have shown that people exposed to racism have poorer health outcomes particularly for mental healthalongside both reduced access to health care and poorer patient experiences.
Electronic thesis and dissertation A high level of racial centrality i. Perceived discrimination and depression: moderating effects of coping, acculturation, and ethnic support. One such bias may be social desirability, engineering dissertation examples tendency to present oneself favorably. The literature review covered longitudinal studies post-dating the systematic review by Paradies et al. Results For ease of readability, we refer to the outcome variable as simply "experiences" and we will distinguish between each condition in the relevant analyses. The gender composition consisted of Male

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Such racial disparities are pervasive and may be the result of racial prejudice and discrimination, as well as differences in socioeconomic status, differential access to opportunities, and institutional policies and practices. Such racial disparities persist despite the many legal and social changes that have improved opportunities for minority racial and ethnic groups in the United States. Several factors may contribute to racial differences in outcomes, including differences in socioeconomic status, differential access to opportunities, and others.

One factor that should be considered is the role of racial discrimination. Overt discrimination against African Americans and other minority groups characterized much of U. Although researchers in specific disciplines have investigated discrimination in particular domains, there has been little effort to coordinate and.

To address this problem, the Committee on National Statistics convened a panel of scholars in to consider the definition of racial discrimination, assess current methodologies for measuring it, identify new approaches, and make recommendations about the best broad methodological approaches.

In particular, this panel was asked to conduct the following tasks:. Give the policy and scholarly communities new tools for assessing the extent to which discrimination continues to undermine the achievement of equal opportunity by suggesting additional means for measuring discrimination that can be applied not only to the racial question but in other important social arenas as well.

Conduct a thorough evaluation of current methodologies for measuring discrimination in a wide range of circumstances where it may occur. Consider how analyses of data from other sources could contribute to findings from research experimentation, such as the U. Department of Housing and Urban Development paired tests. Recommend further research as well as the development of data to complement research studies. Although there is substantial direct empirical evidence for the prevalence of large disparities among racial and ethnic groups in various domains, it is often difficult to obtain direct evidence of whether and to what extent discrimination may be a contributing factor.

Differential outcomes by race and ethnicity may or may not indicate discrimination. Examples of studies using methods that persuasively measure the presence or absence of discrimination are rare, and appropriate data for measurement are often unobtainable. As a result, there is little scholarly consensus about the extent and frequency of discrimination and how it relates to continuing disadvantages along racial and ethnic lines Fix and Turner, One reason it is difficult to assess discrimination is that changes have occurred in the nature of prejudiced attitudes and discriminatory behaviors.

With the passage of the Civil Rights Act of and other laws that prohibit discrimination because of race in a variety of domains, overt discrimination is less often apparent. However, discrimination may persist in more subtle forms. Indeed, social psychological research suggests that relatively automatic and unexamined cognitive processes, of which the holder and sometimes the target may not be fully aware, can lead to discrimination Devine, ; Fiske, These subtleties make defining and measuring discrimination more difficult.

The report is designed to help social science researchers, policy analysts, federal agencies, and concerned observers better understand how to assess racial discrimination in different domains, drawing on different social science methods and data sources as appropriate. To approach this important but difficult task, the panel focused on defining relevant concepts, examining various methodological approaches and data sources, and considering directions for future research.

In some situations, one approach may be more easily implemented and more credible; in other situations, another approach may be more appropriate. Often, multiple approaches will be needed to provide credible evidence about the prevalence of discrimination in a domain.

Thus, the panel attempts to identify the broad range of approaches for measuring discrimination and to provide a critical review of their relative credibility when applied in different situations. The panel develops a cross-disciplinary research and data collection agenda for action by public and private funding agencies and the research community.

The report makes no attempt to actually measure current or past levels of discrimination in any domain. Our purpose is not to report numbers or impacts but to provide guidance and encouragement to researchers and policy analysts as they work across domains to identify where discrimination may be present and what its effects may be. In the first part of this report, the panel defines the concepts of race and racial discrimination from a social science perspective, which we believe is the appropriate perspective for research and policy analysis on discrimination.

When referring to race in the report, the panel uses the categories established by the federal classification standards U. According to these standards, Hispanics or Latinos are referred to as an ethnic group. Yet, although the panel was asked to consider racial discrimination, Hispanics a rapidly growing ethnic population also face discrimination.

In addition, concepts of race and ethnicity are not clearly defined for many Hispanics, so for these two reasons our discussion often refers to Hispanics as well as to specific racial groups. Throughout the report, the term disadvantaged racial group is used to refer to groups in the United States e.

The panel is concerned with broad types of discriminatory behaviors and processes that have negative consequences for disadvantaged racial groups in various social and economic arenas. We draw on sociological, social psychological, and other literature to develop our definition of racial discrimination. The panel acknowledges that the effect of such cumulative discrimination may not be easily identified or measured. In interpreting that part of its charge to review measurement methods, the panel chose to address broad approaches that could be applied across domains, rather than making recommendations about specific approaches for particular domains.

Therefore, although examples are used throughout the report to illustrate efforts to measure discrimination in particular circumstances, our main focus is on methods e. The examples of disparities and discrimination measurement that we provide come from research in five domains: labor markets and employment, education, housing and mortgage lending, health care, and criminal justice. Although not the only domains of concern, these are key areas of social interaction for which discrimination can seriously limit life opportunities; these are also among the areas for which the federal government regularly collects administrative and survey data long used by researchers to study discrimination and discriminatory effects.

We do not provide an exhaustive set of examples for each of these areas. Rather, a selected bibliography of important literature reviews, major reports, and other work on data collection and analytical methods used in each of these domains is provided at the end of this report. Much of the discussion in this report on such topics as statistical inference, experimental design, and data quality is relatively technical in nature.

Although sometimes dry, the import of this discussion should not be misunderstood by readers who are deeply concerned about the possible extent and continued effects of racial discrimination in American life. It was our shared concern about racial discrimination that drew each member of the panel into the in-depth discussions of measurement reflected in this report.

Because we view racial discrimination as a crucial social issue, we believe it is essential to use the most credible and accurate measurement approaches. In carrying out this study, the panel met and deliberated over a period of almost 2 years. We held meetings, invited speakers, and commissioned several papers see Box ; we requested input from prominent scholars on key issues; reviewed a large body of literature on salient aspects of the law and criminal justice, labor markets, housing markets, education, and.

Smith reviews methods for measuring racial discrimination, focusing primarily on survey-based approaches. Ross and Yinger examine the use and quality of data on race collected for administrative purposes, as well as issues of comparability and interpretation that arise for both enforcement officials and scholars attempting to study discrimination.

These papers are available directly from the authors. The panel also commissioned several papers for a workshop on measuring racial disparities and discrimination in elementary and secondary education see Appendix A. The purpose of the workshop was to expand and improve the statistical capability of the U. Department of Education and other federal agencies to measure and track discrimination.

This report is divided into three parts. The chapters in Part I provide a conceptual framework for thinking about racial discrimination. Chapter 2 explores the meaning of race as a social construct and provides historical background on the complex issues surrounding race in the United States and how it is measured in the decennial census and other federal data collections.

Propensity score methods are increasingly used in observational epidemiology as a robust method for dealing with confounding in the analysis stage [ 32 , 33 , 34 , 35 , 36 ] and have more recently been considered as a useful approach for secondary sampling of participants from existing cohorts for subsequent follow up [ 37 ]. All exposed NZHS respondents will be invited into the follow-up survey. Stratification by ethnicity reflects the differential prevalence of racism by ethnic group, and furthermore allows ethnically-stratified estimates of the impact of racism [ 22 ].

Some additional variables were considered for inclusion in the matching process but were removed prior to finalisation details in Table 2. Within each ethnic group stratum, exposed individuals were matched with unexposed individuals matching based on propensity scores to make these two groups approximately exchangeable confounders balanced between exposure groups.

The matching process [ 41 ] used nearest neighbour matching as implemented in MatchIt [ 42 ] in R 3. Balance between groups was then checked on all matching variables prior to finalisation of the sampling lists. Development of the follow-up questionnaire was informed by a literature review and a conceptual model Figs. The literature review focussed on longitudinal studies of racism and health among adolescents and adults that included health or health service outcomes. The literature review covered longitudinal studies post-dating the systematic review by Paradies et al.

Potential pathways between racism and health outcomes. Direct pathway: Main arrow represents the direct biopsychosocial and trauma pathways between experience of racial discrimination Time 1 and negative health outcomes Time 2 Indirect pathways: Racial discrimination Time 1 can impact negatively on health outcomes Time 2 via healthcare pathways e. Racial discrimination Time 1 can impact negatively on physical health outcomes Time 2 via mental health pathways.

Potential pathways between racism and healthcare utilisation outcomes. Main pathway: Main arrow represents the pathway between experience of racial discrimination Time 1 and negative healthcare measures Time 2 , via negative perceptions and expectations of healthcare providers, organisations, systems and future engagement.

Secondary pathway: Racial discrimination T1 can impact negatively on healthcare Time 2 via negative impacts on health increasing healthcare need. We used several criteria for considering and prioritising variables for the questionnaire. The conceptual model also informed prioritisation of variables for the questionnaire. Mediators and confounders were considered for variables not available in the baseline NZHS survey, as was recent experience of racism following the NZHS interview to provide additional measurement of exposure to recent racism.

A final consideration for prioritising items for inclusion was keeping the length of the questionnaire short in order to maximise response rates while being able to fully address the study aims. The questionnaire was extensively discussed by the research team and reviewed by the study advisors prior to finalisation. Table 3 summarises the outcome measures by topic domain and original source with references. Recruitment is currently underway. Recruitment will take place over three tranches to 1 manage fieldwork capacity and 2 allow tracking of response rates and adaptation of contact strategies if recruitment is sub-optimal.

To maximise response rates, we chose to use a multi-modal survey [ 45 ]. Participants are invited to respond by a paper questionnaire included with the initial invitation letter questionnaire returned by pre-paid post , by self-completed online questionnaire, or by computer-assisted telephone interview CATI, on mobile or landline. A pen is included in the study invitation to improve initial engagement with the paper-based survey [ 46 ].

The contact information contains instructions for opting out of the study. Two weeks after the reminder postcard four weeks post-invitation remaining non-respondents are contacted using CATI processes. For those with mobile phone numbers or email addresses, a text SMS or email reminder is sent two days before the telephone contact phase.

Interviewers make up to seven telephone contact attempts for each participant, using all recorded telephone numbers. The use of a multi-modal survey is also expected to minimise recruitment problems inherent to any single modality e. We have contracted an external research company to co-ordinate recruitment and data collection fieldwork under our supervision covering all contact processes described here , which follows recruitment and data management protocols set by our research team. Propensity score methods for the sampling stage are described above: this section focuses on causal analyses for health outcomes in the achieved sample.

All analyses will account for both the complex survey sampling frame weights, strata and clusters from the NZHS and the secondary sampling phase selection based on propensity scores. Complex survey data will be handled using software to account for these designs e.

Linear regression methods will be used to compare change in continuous outcome measures e. K10 score by estimating mean score at follow-up, adjusted for baseline. Analysis of dichotomous categorical outcomes e. We will conduct analyses stratified by ethnic group to explore whether the impact of racism differs by ethnic group.

Models will adjust for confounders included in creating the propensity scores doubly-robust estimation to address residual confounding not fully covered by the propensity score approach [ 52 ]. Analysis for other outcomes will use similar methods. As we hypothesise that some outcomes e.

These historical and recent experience groups and corresponding unexposed individuals form nested sub-groups of the total cohort, and so analysis will follow the same framework outlined above. While the sampling invitation lists are based on matched samples, we have no control about specific individuals choosing to participate in the follow-up survey, and so the original matching is unlikely to be maintained in the achieved sample.

We will conduct sensitivity analyses using re-matched data based on propensity scores for those participating in follow-up to allow for re-calibration of exposed and unexposed groups in the achieved sample. This response rate includes re-contact and agreement to participate, based on past experience recruiting NZHS participants for other studies and the relative length of the current survey questionnaire.

Stratified estimates for Pacific and Asian groups will have poorer precision, but should still provide valid comparisons. These consent methods were approved by the reviewing Ethics committee [ 53 ]. Ethical approval for the study included using the same consent processes for those participants aged 16 to 18 as for older participants. This study will contribute robust evidence to the limited national and international literature from prospective studies on the causal links between experience of racism and subsequent health.

The use of the NZHS as the baseline for the prospective study capitalises on the inclusion of racism questions in that survey to provide a unique and important opportunity to build on and substantially strengthen the current evidence base for the impact of racism on health using data spanning the entire New Zealand adult population. In addition, our use of propensity scores in the sampling phase is a novel approach to prospective recruitment of participants from the NZHS.

This approach should manage confounding while reducing the need and cost of following up all NZHS participants, without compromising the internal validity of the results. The novel methods developed for using the NZHS as the base for a prospective cohort study will have wider application to other health priority areas.

One general limitation of this approach is that baseline data for both propensity score development and baseline health measures is limited to the data captured in the existing larger survey. We anticipate that this study will assist in prioritising racism as a health determinant and inform the development of anti-racism interventions in health service delivery and policy making. Funding for this project began October 1st The first set of respondent invitations was mailed out on July 12th ; fieldwork for the final tranche of invitations was underway at the time of submission and is expected to be completed by 31 December Analysis and reporting will take place in mid-to-late Commission on Social Determinants of Health.

Closing the gap in a generation: health equity through actions on the social determinants of health. Geneva: World Health Organization; Google Scholar. Ministry of Health. Ministry of Social Development. Wellington: Ministry of Social Development; Racism and health I: pathways and scientific evidence.

Am Behav Sci. Reid P, Robson B. Understanding health inequities. In: Robson B, Harris R, editors. Hauora: Maori standards of health IV a study of the years — Jones C. Confronting institutionalized racism. Article Google Scholar. Garner S. Racisms: An introduction. Book Google Scholar. Soc Sci Med. Paradies YC. Defining, conceptualizing and characterizing racism in health research. Crit Public Health. Krieger N. Methods for the scientific study of discrimination and health: an ecosocial approach.

Am J Public Health. Jones CP. Invited commentary: "race," racism, and the practice of epidemiology. Am J Epidemiol. Racism as a determinant of health: a systematic review and meta-analysis. PLoS One. Self-reported experiences of discrimination and health: scientific advances, ongoing controversies, and emerging issues. Annu Rev Clin Psychol. Racial discrimination and health among Asian Americans: evidence, assessment, and directions for future research.

Epidemiol Rev. Discrimination and racial disparities in health: evidence and needed research. J Behav Med. Article PubMed Google Scholar. Racism and health service utilisation: a systematic review and meta-analysis. The pervasive effects of racism: experiences of racial discrimination in New Zealand over time and associations with multiple health domains.

Wellington: Ministry of Health; Racism and health in New Zealand: prevalence over time and associations between recent experience of racism and health and wellbeing measures using national survey data. Cultural and social factors and quality of life of Maori in advanced age. PubMed Google Scholar. Ethnic discrimination prevalence and associations with health outcomes: data from a nationally representative cross-sectional survey of secondary school students in New Zealand.

BMC Public Health. Ethnic discrimination predicts poor self-rated health and cortisol in pregnancy: insights from New Zealand. Perceived discrimination predicts increased support for political rights and life satisfaction mediated by ethnic identity: a longitudinal analysis. Cult Divers Ethn Minor Psychol.

Becares L, Atatoa-Carr P. The association between maternal and partner experienced racial discrimination and prenatal perceived stress, prenatal and postnatal depression: findings from the growing up in New Zealand cohort study. Int J Equity Health. Robson B, Harris R, editors. Hauora : Maori standards of health IV : a study of the years — UN General Assembly. Accessed 1 Nov Smith L.

Decolonizing methodologies: research and indigenous peoples. London and New York: Zed Books; Racism and health: the relationship between experience of racial discrimination and health in New Zealand. A review of the application of propensity score methods yielded increasing use, advantages in specific settings, but not substantially different estimates compared with conventional multivariable methods.

J Clin Epidemiol. Applying propensity score methods in medical research: pitfalls and prospects. Med Care Res Rev. Principles for modeling propensity scores in medical research: a systematic literature review. Pharmacoepidemiol Drug Saf. Propensity score methods gave similar results to traditional regression modeling in observational studies: a systematic review. Stuart EA.

Matching methods for causal inference: a review and a look forward. Stat Sci. Matching methods for selection of subjects for follow-up. Multivariate Behav Res. Austin PC. An introduction to propensity score methods for reducing the effects of confounding in observational studies.

Caliendo M, Kopeinig S. Some practical guidance for the implementation of propensity score matching.

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The Science of Racism - Creators for Change

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