When Diversity Measures Are Nonequivalent: Advice for Practitioners

When addressing diversity, equity, and inclusion, researchers and organizations often focus on group differences in outcomes of interest. However, groups do not always interpret surveys in the same way, causing measurement nonequivalence. Measurement nonequivalence makes it difficult, if not impossible, to compare group differences presenting a problem for how conclusions are drawn. To better understand group differences in survey responding, the current study assessed measurement invariance across five diversity-related measures using the methods outlined by Nye and colleagues (Nye et al., 2019; Somaraju et al., 2022). Data were collected across three organizations (N = 732) from different industries (i.e., healthcare, construction, information technology). Results indicate that for all five measures, there was significant measurement nonequivalence across organizations such that all but the referent item were found to be nonequivalent. We also examined measurement invariance across race and gender where all measures in all organizations were nonequivalent. Interestingly, these effects were not similar across organizations. The construction company had strong gender effects across measures (dMAC = -.64 to -.13), but weak racial effects (dMAC = -.08 to .34). In contrast, the healthcare company had relatively stronger racial effects (dMAC = -.62 to -.35) than gender (dMAC = -.43 to -.01). The information technology company had low effects for both race (dMAC = -.29 to .04) and gender (dMAC = -.20 to .09). Given these results, there are several implications for both research and practice. Researchers should not assume that samples collected across multiple organizations are equivalent and the use of hierarchically nest models may be necessary to account for group differences. Further, greater attention is needed in measurement development to ensure their validity across groups. For practitioners, we recommend utilizing open-ended survey items to better capture group differences due to the prevalence of high measurement nonequivalence in closed-items.https://digitalcommons.odu.edu/gradposters2023_sciences/1001/thumbnail.jp

Does Team Leader Gender Matter? A Bayesian Reconciliation of Leadership and Patient Care During Trauma Resuscitations

OBJECTIVE: Team leadership facilitates teamwork and is important to patient care. It is unknown whether physician gender-based differences in team leadership exist. The objective of this study was to assess and compare team leadership and patient care in trauma resuscitations led by male and female physicians. METHODS: We performed a secondary analysis of data from a larger randomized controlled trial using video recordings of emergency department trauma resuscitations at a Level 1 trauma center from April 2016 to December 2017. Subjects included emergency medicine and surgery residents functioning as trauma team leaders. Eligible resuscitations included adult patients meeting institutional trauma activation criteria. Two video-recorded observations for each participant were coded for team leadership quality and patient care by 2 sets of raters. Raters were balanced with regard to gender and were blinded to study hypotheses. We used Bayesian regression to determine whether our data supported gender-based advantages in team leadership. RESULTS: A total of 60 participants and 120 video recorded observations were included. The modal relationship between gender and team leadership (β = 0.94, 95% highest density interval [HDI], -.68 to 2.52) and gender and patient care (β = 2.42, 95% HDI, -2.03 to 6.78) revealed a weak positive effect for female leaders on both outcomes. Gender-based advantages to team leadership and clinical care were not conclusively supported or refuted, with the exception of rejecting a strong male advantage to team leadership. CONCLUSIONS: We prospectively measured team leadership and clinical care during patient care. Our findings do not support differences in trauma resuscitation team leadership or clinical care based on the gender of the team leader

What Makes a Biased Selection Process? Longitudinal and Measurement Concerns

Woo and colleagues (2022) provided a review of literature in support of using scores from the Graduate Record Examination (GRE) to select students into graduate programs. Multiple responses to their article highlight the use of alternate predictors and discuss the limitations of GRE scores with respect to content and construct validity. We introduce two additional concerns related to measurement equivalence of cognitive ability testing and its ability to consistently predict performance over time. Based on these deficiencies, we also highlight the need to revisit the fundamental criteria for defining fair predictors for individual selection decisions

A Review of Measurement Equivalence in Organizational Research: What's Old, What's New, What's Next?

The study of measurement equivalence has important implications for organizational research. Nonequivalence across groups or over time can affect the results of a study and the conclusions that are drawn from it. As a result, the review paper by Vandenberg and Lance (2000) has been highly cited and has played an important role in understanding the measurement of organizational constructs. However, that paper is now 20 years old, and a number of advances have been made in the application and interpretation of measurement equivalence (ME) since its publication. Therefore, the goal of the present paper is to provide an updated review of ME techniques that describes recent advances in testing for ME and proposes a taxonomy of potential sources of nonequivalence. Finally, we articulate recommendations for applying these newer methods and consider future directions for measurement equivalence research in the organizational literature

The Dynamic Nature of Interpersonal Conflict and Psychological Strain in Extreme Work Settings

Humanity will mount interplanetary exploration missions within the next two decades, supported by a growing workforce operating in isolated, confined, and extreme (ICE) conditions of space. How will future space workers fare in a closed social world while subjected to persistent stressors? Using a sample of 32 participants operating in ICE conditions over the course of 30-45 days, we developed and tested a dynamic model of conflict and strain. Drawing on conservation of resources (COR) theory, we investigated reciprocal relationships between different forms (i.e., task and relationship) of conflict, and between conflict and strain. Results demonstrated evidence for a resource threat feedback loop as current-day task conflict predicted next-day relationship conflict and current-day relationship conflict predicted next-day task conflict. Additionally, results indicated support for a resource loss feedback loop as current-day relationship conflict predicted next-day strain, and current-day strain predicted next-day relationship conflict. Moreover, we found that job conditions affected these associations as current-day relationship conflict was more associated with next-day task conflict when next-day workload was high, but not when next-day workload was low. Similarly, current-day relationship conflict was more associated with next-day strain when next-day workload was high; however, this association decreased when next-day workload was low. Therefore, the results suggest that workload plays a critical role in weakening the effect of these spirals over time, and suggests that targeted interventions (e.g., recovery days) can help buffer against the negative impact of relationship conflict on strain and decrease the extent that relationship conflict spills over into task disputes

The I in Team: Mining Personal Social Interaction Routine with Topic Models from Long-Term Team Data

Social interaction plays a key role in assessing teamwork and collaboration. It becomes particularly critical in team performance when coupled with isolated, confined, and extreme conditions such as undersea missions. This work investigates how social interactions of individual members in a small team evolve during the course of a long duration mission. We propose to use a topic model to mine individual social interaction patterns and examine how the dynamics of these patterns have an effect on self-assessment of mood and team cohesion. Specifically, we analyzed data from a 6-person crew wearing Sociometric badges over a 4-month mission. Our results show that our method can extract the latent structure of social contexts without supervision. We demonstrate how the extracted patterns based on probabilistic models can provide insights on common behaviors at various temporal resolutions and exhibit links with self-report affective states and team cohesion.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Pattern Recognition and Bioinformatic

Supplemental Material, dMACS_Effect_Magnitudes_Supplemental_Materials-011418-Conditional_Accept-Final - How Big Are My Effects? Examining the Magnitude of Effect Sizes in Studies of Measurement Equivalence

<p>Supplemental Material, dMACS_Effect_Magnitudes_Supplemental_Materials-011418-Conditional_Accept-Final for How Big Are My Effects? Examining the Magnitude of Effect Sizes in Studies of Measurement Equivalence by Christopher D. Nye, Jacob Bradburn, Jeffrey Olenick, Christopher Bialko and Fritz Drasgow in Organizational Research Methods</p

TeamSense: Assessing Personal Affect and Group Cohesion in Small Teams through Dyadic Interaction and Behavior Analysis with Wearable Sensors

Continuous monitoring with unobtrusive wearable social sensors is becoming a popular method to assess individual affect states and team effectiveness in human research. A large number of applications have demonstrated the effectiveness of applying wearable sensing in corporate settings; for example, in short periodic social events or in a university campus. However, little is known of how we can automatically detect individual affect and group cohesion for long duration missions. Predicting negative affect states and low cohesiveness is vital for team missions. Knowing team members' negative states allows timely interventions to enhance their effectiveness. This work investigates whether sensing social interactions and individual behaviors with wearable sensors can provide insights into assessing individual affect states and group cohesion. We analyzed wearable sensor data from a team of six crew members who were deployed on a four-month simulation of a space exploration mission at a remote location. Our work proposes to recognize team members' affect states and group cohesion as a binary classification problem using novel behavior features that represent dyadic interaction and individual activities. Our method aggregates features from individual members into group levels to predict team cohesion. Our results show that the behavior features extracted from the wearable social sensors provide useful information in assessing personal affect and team cohesion. Group task cohesion can be predicted with a high performance of over 0.8 AUC. Our work demonstrates that we can extract social interactions from sensor data to predict group cohesion in longitudinal missions. We found that quantifying behavior patterns including dyadic interactions and face-to-face communications are important in assessing team process.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Pattern Recognition and Bioinformatic

Discovering Digital Representations for Remembered Episodes from Lifelog Data

Combining self-reports in which individuals reflect on their thoughts and feelings (Experience Samples) with sensor data collected via ubiquitous monitoring can provide researchers and applications with detailed insights about human behavior and psychology. However, meaningfully associating these two sources of data with each other is difficult: while it is natural for human beings to reflect on their experience in terms of remembered episodes, it is an openchallenge to retrace this subjective organization in sensor datareferencing objective time. Lifelogging is a specific approach to the ubiquitous monitoring of individuals that can contribute to overcoming this recollection gap. It strives to create a comprehensive timeline of semantic annotations that reflect the impressions of the monitored person from his or her own subjective point-of-view. In this paper, we describe a novel approach for processing such lifelogs to situate remembered experiences in an objective timeline. It involves the computational modeling of individuals’ memory processes to estimate segments within a lifelog acting as plausible digital representations for their recollections. We report about an empirical investigation in which we use our approach to discover plausible representations for remembered social interactions between participants in a longitudinal study. In particular, we describe an exploration of the behavior displayed by our model for memory processes in this setting. Finally, we explore the representations discovered for this study and discuss insights that might be gained from them.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Interactive IntelligencePattern Recognition and Bioinformatic