Currents in a Stream: College Student Identities and Ethnic Identities and Their Relationship with Self‐Esteem,Efficacy, and Grade Point Average in an Urban University*

Objectives. We investigate how college student identities and ethnic identities vary among black, white, and Asian students and among immigrant, second‐, and third‐generation students at a large public urban university (in counterpoint to recent studies at highly selective schools). In addition, we explore how those identities are related to college students' sense of self‐esteem and efficacy and their academic performance. Methods. We use survey data from a sample (N=652) of students attending a large diverse public urban university to create new indexes for several dimensions of college identity and ethnic identity and use existing self‐esteem and efficacy indexes to compare black, white, and Asian students, as well as immigrant, second‐generation, and third‐generation students. Results. Among several significant identity differences, we find: (1) whites are lower than blacks on college identity indexes, and immigrant students are higher than subsequent‐generation students on college student identity measures; (2) whites are lower than blacks and Asians on ethnic identity measures; only the ethnic activities index declines linearly from immigrant to second‐ to third‐generation students; (3) blacks have higher self‐esteem and efficacy than whites or Asians; whites have higher GPAs than blacks or Asians, while immigrant students have higher GPAs than third‐generation students; and (4) at least one college student identity dimension and one ethnic identity dimension is related to self‐esteem, efficacy, and GPA. Conclusions. How young adults conceive of themselves as college students and the way they formulate their own racial‐ethnic identities is related to their self‐esteem, efficacy, and academic performance. Moreover, the pattern that these relationships take is somewhat different at a large diverse public urban university than at highly selective universities.     

Minimizing Social Desirability Bias in Measuring Sensitive Topics: The Use of Forgiving Language in Item Development

Abstract

Social science research has long been concerned with attitudes, beliefs, and behaviors that are potentially objectionable, immoral, or illegal. These types of topics include, for example, racism, ableism, cheating, and stealing, among others. Referred to as “sensitive topics,” their investigation usually involves questions that require respondents to admit to attitudes, beliefs, and behaviors that violate social norms, making their assessment susceptible to error due to social desirability bias. This article describes an empirical investigation of an approach to minimize this bias, the use of “forgiving language” in survey item development and the effect on item variability. Using secondary data initially collected as part of a measurement development study of mental health providers’ stigmatization of service users, 15 pairs of similar, thematically targeted items, varying with respect to wording approach were tested using a purposive sample of mental health providers (N?=?220). Findings indicate that items crafted in a forgiving manner were not significantly influenced by social desirability bias, in contrast to items developed in more traditional language. Additionally, forgiving language-items produced higher levels of agreement, on average, when compared to those written in more traditional language. More research is indicated, including systematic variation of wording approaches, but these results seem promising.     

Crossover designs for comparing test treatments to a control treatment when subject effects are random

We study crossover designs for the comparisons of several test treatments versus a control treatment and partially generalize the results of Hedayat and Yang (2005) to the situation in which subject effects are assumed to be random. More specifically, we establish lower bounds for the trace of the inverse of the information matrix for the test treatments versus control comparisons under a random subject effects model and show that most of the small size (3-, 4- and 5-period) designs introduced by Hedayat and Yang (2005) are highly efficient in the class of designs in which the control treatment appears equally often in all periods and no treatment is immediately preceded by itself.     

‘In a perfect world it would be great if they didn't exist’: How Australians experience payday loans

In the last few decades, payday lending has mushroomed in many developed countries. The arguments for and against an industry which provides small, short‐term loans at very high interest rates have also blossomed. This article presents findings from an Australian study to contribute to the international policy and practice debate about a sector which orients to those on a low income. At the heart of this debate lies a conundrum: Borrowing from payday lenders exacerbates poverty, yet many low‐income households rely on these loans. We argue that the key problem is the restricted framework within which the debate currently oscillates. Key Practitioner Message: ● Framing payday borrowing as a problem of market failure leads to one‐sided and ineffective regulatory responses; ● Until governments instigate real alternatives for cheap and readily available credit, and broader anti‐poverty measures, curbing access to payday lenders can have the perverse effect of increasing privation; ● For practitioners seeking to abolish payday lending, campaigns for higher wages and a liveable social welfare income are central.     

Mixtures of concentrated multivariate sine distributions with applications to bioinformatics

Motivated by examples in protein bioinformatics, we study a mixture model of multivariate angular distributions. The distribution treated here (multivariate sine distribution) is a multivariate extension of the well-known von Mises distribution on the circle. The density of the sine distribution has an intractable normalizing constant and here we propose to replace it in the concentrated case by a simple approximation. We study the EM algorithm for this distribution and apply it to a practical example from protein bioinformatics.     

Analysis of Choice Expectations in Incomplete Scenarios

This paper studies the use of probabilistic expectations data to predict behavior in incomplete scenarios posed by the researcher. The information that respondents have when replying to questions posing incomplete scenarios is a subset of the information that they would have in actual choice settings. Hence such questions do not elicit pure statements of preference; they elicit preferences mixed with expectations of future events that may affect choice behavior. The analysis developed here assumes respondents recognize that their behavior may depend on information they do not have when expectations are elicited, and that they answer coherently and honestly given the information provided. The objective in imagining such ideal respondents is to place a logical upper bound on the predictive content of elicited choice expectations.