Austin Nichols
Dados Biográficos
| ID | 4264997 |
|---|---|
| NOME | Austin Nichols |
| PRENOMES | Austin |
| SOBRENOME | Nichols |
| ASSINATURA | NICHOLS A |
| AFILIAÇÕES | Urban Institute Washington, DC |
| ORCID | 0000-0001-8076-1755 |
| VERIFICADO | Sim |
| TOTAL DE OBRAS | 11 |
| TOTAL DE CITAÇÕES | 74 |
| TOTAL COMO AUTOR | 11 |
| TOTAL COMO EDITOR | 0 |
| PRIMEIRO ANO DE PUBLICAÇÃO | 2007 |
| ANO MAIS RECENTE DE PUBLICAÇÃO | 2021 |
| ÍNDICE H | 3 |
Disability as Status Competition
Many African American and Hispanic children are classified as mildly disabled. Although this makes special education services available to these and other children who need them, contention endures as to whether disability classification also is racially (and ethnically) biased. The authors view disability classification as status competition, in which minorities are overrepresented in low-status categories such as intellectual disability and emo…
Examining the effect of mindfulness on well-being
The current research sought to better understand the effect of mindfulness on well-being by examining self-connection as a potential mediator. We define self-connection as: (1) an awareness of oneself, (2) an acceptance of oneself based on this awareness, and (3) an alignment of one’s behavior with this awareness. Based on this definition, we measured self-connection, mindfulness and well-being using two distinct samples and two different operati…
Fostering Stress Resilience Among Business Students
Educators are becoming increasingly concerned about the high rates of burnout among their students. Although the solution may appear to be reducing the stress their students experience, simply reducing stress is a temporary solution and does not help students when they enter the workforce and encounter increased stressors. A better option may be to consider the ways in which students can increase stress resilience in ways that will help them long…
Why don’t we care more about carelessness? Understanding the causes and consequences of careless participants
Although careless respondents have wreaked havoc on research for decades, the prevalence and implications of these participants has likely increased due to many new methodological techniques currently in use. Across three studies, we examined the prevalence of careless responding in participants, several means of predicting careless respondents, and the implications of careless respondents on data quality and recruitment attempts. At the same tim…
Rising Income Inequality Through a Disability Lens
Retooling Poverty Targeting Using Out-of-Sample Validation and Machine Learning
Proxy means test (PMT) poverty targeting tools have become common tools for beneficiary targeting and poverty assessment where full means tests are costly. Currently popular estimation procedures for generating these tools prioritize minimization of in-sample prediction errors; however, the objective in generating such tools is out-of-sample prediction.We present evidence that prioritizing minimal out-of-sample error, identified through cross-val…
Using Preferred Applicant Random Assignment (Para) to Reduce Randomization Bias in Randomized Trials of Discretionary Programs
Randomization bias occurs when the random assignment used to estimate program effects influences the types of individuals that participate in a program. This paper focuses on a form of randomization bias called “applicant inclusion bias,” which can occur in evaluations of discretionary programs that normally choose which of the eligible applicants to serve. If this nonrandom selection process is replaced by a process that randomly assigns eligibl…
Retooling Poverty Targeting Using Out-of-Sample Validation and Machine Learning
Proxy means test (PMT) poverty targeting tools have become common tools for beneficiary targeting and poverty assessment where full means tests are costly. Currently popular estimation procedures for generating these tools prioritize minimization of in-sample prediction errors; however, the objective in generating such tools is out-of-sample prediction. This paper presents evidence that prioritizing minimal out-of-sample error, identified through…
Predicting policy attitudes from general prejudice versus specific intergroup emotions
The Good-Subject Effect
Although researchers are often concerned with the presence of participant demand, few have directly examined effects of demand on participant behavior. Before beginning the present study, a confederate informed participants (N = 100) of the study's purported hypothesis. Participants then performed a laboratory task designed to evaluate the extent to which they would respond in ways that may confirm or disconfirm the hypothesis of the study. The a…
Causal Inference with Observational Data
Problems with inferring causal relationships from nonexperimental data are briefly reviewed, and four broad classes of methods designed to allow estimation of and inference about causal parameters are described: panel regression, matching or reweighting, instrumental variables, and regression discontinuity. Practical examples are offered, and discussion focuses on checking required assumptions to the extent possible.
The Good-Subject Effect
Although researchers are often concerned with the presence of participant demand, few have directly examined effects of demand on participant behavior. Before beginning the present study, a confederate informed participants (N = 100) of the study's purported hypothesis. Participants then performed a laboratory task designed to evaluate the extent to which they would respond in ways that may confirm or disconfirm the hypothesis of the study. The a…
Predicting policy attitudes from general prejudice versus specific intergroup emotions
Disability as Status Competition
Many African American and Hispanic children are classified as mildly disabled. Although this makes special education services available to these and other children who need them, contention endures as to whether disability classification also is racially (and ethnically) biased. The authors view disability classification as status competition, in which minorities are overrepresented in low-status categories such as intellectual disability and emo…
Rising Income Inequality Through a Disability Lens
Why don’t we care more about carelessness? Understanding the causes and consequences of careless participants
Although careless respondents have wreaked havoc on research for decades, the prevalence and implications of these participants has likely increased due to many new methodological techniques currently in use. Across three studies, we examined the prevalence of careless responding in participants, several means of predicting careless respondents, and the implications of careless respondents on data quality and recruitment attempts. At the same tim…
Causal Inference with Observational Data
Problems with inferring causal relationships from nonexperimental data are briefly reviewed, and four broad classes of methods designed to allow estimation of and inference about causal parameters are described: panel regression, matching or reweighting, instrumental variables, and regression discontinuity. Practical examples are offered, and discussion focuses on checking required assumptions to the extent possible.
The Good-Subject Effect
Although researchers are often concerned with the presence of participant demand, few have directly examined effects of demand on participant behavior. Before beginning the present study, a confederate informed participants (N = 100) of the study's purported hypothesis. Participants then performed a laboratory task designed to evaluate the extent to which they would respond in ways that may confirm or disconfirm the hypothesis of the study. The a…
Predicting policy attitudes from general prejudice versus specific intergroup emotions
Retooling Poverty Targeting Using Out-of-Sample Validation and Machine Learning
Proxy means test (PMT) poverty targeting tools have become common tools for beneficiary targeting and poverty assessment where full means tests are costly. Currently popular estimation procedures for generating these tools prioritize minimization of in-sample prediction errors; however, the objective in generating such tools is out-of-sample prediction. This paper presents evidence that prioritizing minimal out-of-sample error, identified through…
Using Preferred Applicant Random Assignment (Para) to Reduce Randomization Bias in Randomized Trials of Discretionary Programs
Randomization bias occurs when the random assignment used to estimate program effects influences the types of individuals that participate in a program. This paper focuses on a form of randomization bias called “applicant inclusion bias,” which can occur in evaluations of discretionary programs that normally choose which of the eligible applicants to serve. If this nonrandom selection process is replaced by a process that randomly assigns eligibl…
Retooling Poverty Targeting Using Out-of-Sample Validation and Machine Learning
Proxy means test (PMT) poverty targeting tools have become common tools for beneficiary targeting and poverty assessment where full means tests are costly. Currently popular estimation procedures for generating these tools prioritize minimization of in-sample prediction errors; however, the objective in generating such tools is out-of-sample prediction.We present evidence that prioritizing minimal out-of-sample error, identified through cross-val…
Examining the effect of mindfulness on well-being
The current research sought to better understand the effect of mindfulness on well-being by examining self-connection as a potential mediator. We define self-connection as: (1) an awareness of oneself, (2) an acceptance of oneself based on this awareness, and (3) an alignment of one’s behavior with this awareness. Based on this definition, we measured self-connection, mindfulness and well-being using two distinct samples and two different operati…
Fostering Stress Resilience Among Business Students
Educators are becoming increasingly concerned about the high rates of burnout among their students. Although the solution may appear to be reducing the stress their students experience, simply reducing stress is a temporary solution and does not help students when they enter the workforce and encounter increased stressors. A better option may be to consider the ways in which students can increase stress resilience in ways that will help them long…
Why don’t we care more about carelessness? Understanding the causes and consequences of careless participants
Although careless respondents have wreaked havoc on research for decades, the prevalence and implications of these participants has likely increased due to many new methodological techniques currently in use. Across three studies, we examined the prevalence of careless responding in participants, several means of predicting careless respondents, and the implications of careless respondents on data quality and recruitment attempts. At the same tim…
Rising Income Inequality Through a Disability Lens
Disability as Status Competition
Many African American and Hispanic children are classified as mildly disabled. Although this makes special education services available to these and other children who need them, contention endures as to whether disability classification also is racially (and ethnically) biased. The authors view disability classification as status competition, in which minorities are overrepresented in low-status categories such as intellectual disability and emo…
Psychology (7 obras) · Computer Science (6 obras) · Social Psychology (5 obras) · Social Psychology (5 obras) · Economics (4 obras) · Developmental psychology (3 obras) · Income, Poverty, and Inequality (3 obras) · Advanced Causal Inference Techniques (2 obras) · Artificial Intelligence (2 obras) · Clinical Psychology (2 obras)