Claremont Graduate University
  • Claremont, California, United States
Recent publications
Video modeling was used to teach children with autism spectrum disorder how to respond to taped stranger lure scenarios and in-situ stranger lures. A multiple baseline design across participants was used to assess treatment effects. Measures consisted of reported verbal and motor responses to three abduction scenarios and actual responses to stranger lures planted near the children’s therapy program and within the children’s communities. Each child displayed increases in appropriate responses to taped abduction scenarios and in-situ stranger lures post-treatment. One year following the intervention 90% of the participants maintained and generalized the skills. This study indicated that children with ASD could learn to respond to taped stranger lure scenarios and correspondingly demonstrate these skills in situ and maintain these skills for at least one year following treatment.
A growing body of evidence suggests positive psychological capital (PsyCap) strongly predicts well-being and performance at work. However, most of this empirical research has used self-report survey designs, increasing the possibility of self-report and mono-method bias. The current study used a multitrait-multimethod (MTMM) research design and condition-based regression analysis to examine the effect of PsyCap on job well-being and work role performance beyond self-report bias. Findings from 416 full-time employees within 208 coworker pairs showed that self-reported and informant-reported PsyCap were predictive of job well-being and work role performance. However, multitrait-multimethod analyses showed monomethod measures may inflate the strength of association between PsyCap and work outcomes. Implications for future applied positive psychology research controlling for self-report and monomethod bias with multiple data sources are discussed.
Flow experience is a psychological state characterized by simultaneous absorption, concentration, and enjoyment. Examining the change and continuity of the flow experience––an optimal state that contributes to well-being––is critical to the understanding of the lifelong trajectory of human flourishing. Nevertheless, to date there has been no systematic investigation of the relationship between age and flow experiences across adulthood. Developmental models of flow experiences suggest the continuity hypothesis that people are motivated to sustain a high level of flow experiences as long as conditions permit. We conducted two studies to investigate flow experiences among adults of different ages. Study 1 ( N = 1,162; age range 30–80) used longitudinal data from the Midlife in the United States (MIDUS) project, investigating the changes in flow experiences at work over a 10-year span. Study 2 ( N = 393; age range 20–82) was an online survey that examined age-related differences in flow experiences. Both studies revealed minimal relationships between age and flow experiences. Post-hoc analyses revealed no significant moderating effect of common demographics including gender, race, and education on the age–flow relationship. Taken together, these studies elucidate the “flow profile” in adulthood that is consistent with the continuity hypothesis. We discuss relations of the findings to the literature on flow experiences and well-being.
The topic of this study is in vitro proton magnetic resonance spectroscopy (MRS). The theme is on theoretical analysis of time signals encoded at a high magnetic field 14.1T, using a Bruker spectrometer, operating at a Larmor frequency of 600 MHz. The samples, dissolved in a D $${}_2$$ 2 O buffer, are from histopathologically analyzed ovarian cyst fluid from two patients. The benign and malignant diagnoses were serous cystadenoma and serous cystadenocarcinoma, respectively. It is of vital clinical importance to determine whether certain specific patterns, inferred from the analyzed/interpreted MRS data could be correlated with this and similar histopathologic findings for other patients. Encoded time signals contain the fingerprint of the examined sample, its metabolic content. Therefore, to detect the sought patterns from MRS data, the salient characteristics of a malignant tumor, implied by the diagnostically most relevant metabolites (including recognized cancer biomarkers, e.g. lactic acids, cholines, ...), need to be unambiguously identified by their significant departures from the associated control data of benign biomaterial, ovarian cyst fluid (serous cystadenoma) in the diagnostic problem under the present consideration. Such identifications are unfeasible by visualization in the domain of encoding (time domain). A direct inspection of the graphed waveforms of an encoded time signal would give no clue about its structure nor about the sample content. However, merely visualizing the plots of the equivalent, information-preserving spectral lineshape profiles in the frequency domain would make transparent at least some of clinically useful, discernible features of MRS data, a number of resonances assignable to the known and unknown metabolites. For instance, the size of each resonance (peak area) is proportional to the concentration of the given metabolite. This is a key quantitative measure, which could help differentiate a malignant from a benign specimen by reference to the normal standards. A number of metabolites (choline, alanine, lactate, threonine, $$\beta $$ β -hydroxybuturate, valine, isolecine, leucine, ...) have substantially different concentrations in the malignant compared with normal samples. Time signals can be processed by two substantially different categories of mathematical transforms, shape and parameter estimators. The former processors are alternatively called nonparametric estimators. They have been employed for envelopes in our recent study on this problem, which will presently be addressed with the prime focus on reconstructions of the corresponding components. Components and envelopes are partial and total shape spectra, respectively. The sum of all the component lineshapes (one per metabolite) yields the envelope nondegenerate spectrum representation of the entire sample. Presently, a deeper diagnostically valid insight is gained about the metabolic content of the scanned sample through the reported exact component spectra. The employed parameter estimators are the high-resolution, noise-suppressing nonderivative and derivative fast Padé transforms. Detailed are several critical achievements by the parametric Padé processing of direct clinical relevance. Importantly, all the accomplishments are shared by the nonparametric derivative Padé estimations. Three examples are highlighted here as follows. Confirmation of our recent nonparametric derivative detection of an unassigned metabolite (a singlet peak) co-resonating with free choline near chemical shift 3.19 ppm (parts per million). Therein, with the nonderivative envelope, only one compound peak usually appears and is conventionally assigned to a free choline singlet. However, such an oversight would yield about twice larger value of the true concentration of this key cancer biomarker. The concentration level of another cancer biomarker (lactate) is also overestimated by any nonparametric nonderivative envelope. In sharp contrast, the parametric nonderivative Padé estimation unequivocally detects six usually invisible resonances (assignable to other metabolites) beneath the lactate doublet, around chemical shift 1.41 ppm. At least two of the strongest among these invisible six resonances can be also identified in the nonparametric fourth derivative Padé envelope. Regularization of the spectral compound for the water residual (4.71 ppm), which deforms the neighboring resonance lineshapes and impacts adversely on the concentration assessments of other nearby metabolites. This is accomplished by the fourth derivative envelope (coincident with the components) whose narrowing of the widths, cutting off the long tails and the background flattening generate a quantifiable singlet of water. This can serve as a reliable calibration reference resonance. After such a localization, no distortion appears around water, so that even very near 4.71 ppm, several smaller resonances are detected (assignable to a multiplet of nitrogen acetyl asparate), totally invisible in the nonparametric nonderivative envelope.
We explored the question of how employees react to the perceived fairness of their manager’s treatment of customers. Current models of third-party justice reactions produce two tenable but mutually exclusive responses to observed manager-customer treatment: Relational affirmation, in which service employees engage customers in ways perceived to be congruent with manager-customer treatment, and relational restoration, in which they attempt to rebalance or restore their perceived relationship with customers to counteract observed treatment. Hypothesizing both effects, we explore the role of service employees’ relational identification with customers as a moderator of these two employee reactions. Across a field study of nursing assistants (n = 107) and their supervisors (n = 7), and a laboratory study with a simulated customer service help desk (n = 100), employee-customer identification was found to interact with supervisor-customer interpersonal justice such that the relationship between perceived justice and pro-customer behavior was positive when employee-customer identification was low (consistent with relational affirmation) and negative when employee-customer identification was high (consistent with relational restoration) (Study 1: β = −.22; Study 2: η² = .07).
The Context Comparison Model (CCM) provides a promising avenue to guide persuasive communication development by highlighting the features of the communication context that require consideration, including source, target, and task variables. The model was tested in a study of global climate change. American participants read a text outlining scientific evidence for global climate change and a policy proposal to mitigate future climate change. Prior to reading the text, participants’ completed measures of their political affiliation (Republican, Democrats, Independent or Other) to render their group memberships salient. They were randomly assigned to one of four source conditions: (a) ingroup minority; (b) ingroup majority; (c) outgroup minority; or (d) outgroup minority. Participants completed pre- and post-measures of attitudes and the plausibility of climate change. Pretest scores revealed that global climate change attitudes were held less strongly by Republicans than Democrats. In line with expectations, participants’ subjective attitudes were more influenced by ingroup sources, and larger persuasive effects were obtained for ingroup minorities. For the plausibility of climate change, participants were more persuaded by an outgroup source, and larger effects were evident for outgroup majorities. Results were precisely predicted by the CCM. Their implications for science communication were discussed.
There is vigorous debate as to whether influential social media platforms like Twitter and Facebook should censor objectionable posts by prominent individuals in the United States and elsewhere. A tentative middle ground is employing content moderation to signal to social media audiences that certain posts may contain objectionable information through the mechanism of flagging. Existing studies have mainly examined the effect of flagging regular users' content. To add to this emerging literature stream, we examine the effect of flagging when the underlying content is produced by a prominent individual. Leveraging Twitter's moderation activities on former U.S. President Donald Trump's tweets as our empirical setting, we employ three machine learning algorithms to estimate the effect of flagging Trump's tweets. We explore preliminary evidence as to whether these posts were retweeted less or more than expected. Our results indicate that the flagged tweets were retweeted at higher rates than expected. Our findings suggest that flagging content of prominent individuals on social media might be ineffective or even counterproductive in curbing the spread of content deemed objectionable by social media companies.
Current studies on the effect of thank-you gifts on charitable giving are primarily based on the conclusion of a milestone paper, “The counterintuitive effects of thank-you gifts on charitable giving” which argued that thank-you gifts are mainly driven by lower feelings of altruism. This article argues that the question design in “The counterintuitive effects of thank-you gifts on charitable giving” may lead to a biased conclusion. This article added an extra treatment group to the original study and found that the authors neglected the critical impact of participants’ inference about the usage of the money.
Cognitive control serves a crucial role in human higher mental functions. The Dual Mechanisms of Control (DMC) account provides a unifying theoretical framework that decomposes cognitive control into two qualitatively distinct mechanisms – proactive control and reactive control. While prior behavioral and neuroimaging work has demonstrated the validity of individual tasks in isolating these two mechanisms of control, there has not been a comprehensive, theoretically-guided task battery specifically designed to tap into proactive and reactive control across different domains of cognition. To address this critical limitation and provide useful methodological tools for future investigations, the Dual Mechanisms of Cognitive Control (DMCC) task battery was developed to probe these two control modes, as well as their intra-individual and inter-individual differences, across four prototypical domains of cognition: selective attention, context processing, multi-tasking, and working memory. We present this task battery, along with detailed descriptions of the experimental manipulations used to encourage shifts to proactive or reactive control in each of the four task domains. We rigorously evaluate the group effects of these manipulations in primary indices of proactive and reactive control, establishing the validity of the DMCC task battery in providing dissociable yet convergent measures of the two cognitive control modes.
Online learning has emerged as a widely used learning mode and will likely supplement traditional learning in the post-pandemic era. The purpose of this study is to present student voices of online school education by investigating students’ online learning experiences during the COVID-19 pandemic in various contexts, and explain why the impacts are important to student learning and well-being. Semi-structured in-depth interviews were conducted with nine students from China, Lebanon, and the United States to gain direct insight into students’ perceptions of each country. The results showed that the online learning environment provided at the national level, such as social conflicts, and the facilities provided at the individual level, such as information access, increase the educational inequity. High-school students experienced numerous psychological changes and encountered academic cheating issues in the home online-learning environment. We recommend that online school education should make significant improvements in pedagogy, students’ mental health, and learning assessment, and consider factors beyond technology solutions.
Prices respond to equate supply and demand. However, price-setting in low-volume or “thin” markets is a challenge as is determining which items to carry. We present an algorithm that takes into account a store’s fixed costs, the cost of goods sold, prices, and listing duration to determine the portfolio of items to maximize profits. Prices can then be assigned as a mark-up over cost. The usefulness of this approach is demonstrated by applying it to a store on eBay in which the seller needs to meet a profit threshold. The findings identify how sellers of unusual items can effectively determine which items to list and how to set price to reach profit goals.
Motorcycle riders and passengers are much more likely to be killed or severely injured in a crash, and on average about 15% of all traffic fatalities include motorcyclists. Between 2008 and 2019, the average motorcycle crash frequency in Wyoming was 286 crashes/year, 17 of those being fatal. This paper assesses injury severity of motorcycle-related crashes in Wyoming using 12 years of motorcycle crash data and applying multinomial logistic regression modeling to determine the odds ratios for injury severity. Four models were developed and analyzed, based on the setting and the number of vehicles involved. The most common factors affecting injury severity include vehicle maneuver, driver action, junction relation, alcohol, animal and speed involvement, and helmet use. The vicinity of intersections significantly increases the odds of injury crashes in urban areas, and in rural areas with multi-vehicle involvement. Certain vehicle maneuvers are also associated with a more severe crash outcome.
Despite the availability of online educational resources about human papillomavirus (HPV), many women around the world may be prevented from obtaining the necessary knowledge about HPV. One way to mitigate the lack of HPV knowledge is the use of auto-generated text summarization tools. This study compares the level of HPV knowledge between women who read an auto-generated summary of HPV made using the BERT deep learning model and women who read a long-form text of HPV. We randomly assigned 386 women to two conditions: half read an auto-generated summary text about HPV (n = 193) and half read an original text about HPV (n = 193). We administrated measures of HPV knowledge that consisted of 29 questions. As a result, women who read the original text were more likely to correctly answer two questions on the general HPV knowledge subscale than women who read the summarized text. For the HPV testing knowledge subscale, there was a statistically significant difference in favor of women who read the original text for only one question. The final subscale, HPV vaccination knowledge questions, did not significantly differ across groups. Using BERT for text summarization has shown promising effectiveness in increasing women’s knowledge and awareness about HPV while saving their time.
Operationalizing social group identification as political partisanship, we examine followers’ (i.e., US residents’) affective experiences and behavioral responses during the initial COVID‐19 outbreak in the United States (March to May 2020). In Study 1, we conducted content analyses on major news outlets’ coverage of COVID‐19 (N = 4319) to examine media polarization and how it plays a role in shaping followers’ perceptions of the pandemic and leadership. News outlets trusted by Republicans portrayed US President Donald Trump as more effective, conveyed a stronger sense of certainty with less negative affective tone, and had a lower emphasis on COVID‐19 prevention compared to outlets trusted by Democrats. We then conducted a field survey study (Study 2; N = 214) and found that Republicans perceived Trump as more effective, experienced higher positive affect, and engaged in less COVID‐19 preventive behavior compared to Democrats. Using a longitudinal survey design in Study 3 (N = 251), we examined how emotional responses evolved in parallel with the pandemic and found further support for Study 2 findings. Collectively, our findings provide insight into the process of leadership from a social identity perspective during times of crisis, illustrating how social identity can inhibit mobilization of united efforts. The findings have implications for leadership of subgroup divides in different organizational and crisis contexts.
Impulsivity is a multidimensional construct. The UPPS-P model of impulsivity differentiates five distinct dimensions: negative urgency, positive urgency, lack of premeditation, lack of perseverance, and sensation seeking. The present study, reports the first translation and validation of the recently revised short form of the UPPS-P scale (S-UPPS-P) on a Persian-speaking sample, examining the relationship between impulsivity and working memory. who also completed the Positive and Negative Affect Scale (PANAS), the Buss and Perry Aggression Questionnaire, the Behavioral Inhibition and Activation Scales (BIS/BAS), and the Wechsler Digit Span Task (WDST). A series of confirmatory factor analyses, and Cronbach's alpha results supported the factor structure of the scale. The findings supported the S-UPPS-P model's hypothesized correlations with PANAS, aggressiveness, and the construct validity of the model. The results of hierarchical regression analysis showed that backward and forward digit span scores of the WDST predicted the S-UPPS-P impulsivity scores over the portion explained by BIS/BAS, PANAS, and aggression scores. To conclude, the revised S-UPPS-P Impulsive Behavior Scale was well supported even in a very different population than usually sampled, adding to growing evidence that it assesses distinct but interrelated aspects of the impulsivity construct. Our findings also suggest that attentional capacities and working memory play important roles in the prediction of impulsivity.
We investigated collective efficacy as a key predictor of team effectiveness (i.e., satisfaction and performance) and examined three behavioral team process dimensions (i.e., transition, action, and interpersonal processes) as novel mediators. Based on survey data from 160 project teams, we found a positive linear relation between collective efficacy and team effectiveness. In addition, we found that a higher frequency of action and interpersonal processes partially explains the positive benefits of collective efficacy on team effectiveness. Our study has unique practical and theoretical implications as it provides empirical evidence for distinct mechanisms of the collective efficacy-team effectiveness relation.
One of the most important units of analysis for positive organizational psychology research is leaders and future leaders in the workplace. Leaders often have a large responsibility for and influence on the well-being and performance of their followers. They also face the unique challenge of serving their followers and the organization while needing to maintain their own vitality and well-being. Vitality can provide a foundation of energy resources to a leader to serve at their full capacity. This study develops and empirically examines a new three factor scale to measure leader vitality which includes physical, psychological, and emotional components. In study 1, a total of 175 participants (including n = 128 leaders) completed the Leader Vitality Scale (LVS) and other positive psychology related measures. Exploratory factor analysis and then confirmatory factor analysis showed that the LVS is hierarchical with three distinct factors, with overall vitality as the higher-order factor. Correlational tests with two established vitality scales for general use showed that the LVS is positively related to existing scales, demonstrating convergent validity. In study 2, data was gathered from 92 top level leaders in the C-Suite ( n = 25), vice presidents ( n = 23), directors ( n = 21), and managers ( n = 23) of organizations across the United States. Results showed that LVS scores significantly correlated with life satisfaction, positive emotions, positive functioning at work, and psychological capital. Overall, these findings suggest that the LVS is a valid measure for assessing leader vitality, and can used in future studies of well-being and positive functioning at work.
Prior research has established the key impact of customers' Big Five personality traits (e.g., agreeableness/conscientiousness) on their brand engagement, suggesting that individuals exhibiting differing personality traits engage differently with brands. In parallel, extending influential customer engagement research, stakeholder engagement, which covers any stakeholder's (e.g., a customer's, supplier's, employee's, or competitor's) engagement in his/her role‐related interactions, activities, and relationships, is rapidly gaining momentum. However, despite existing acumen in both areas, little remains known regarding the effect of stakeholders' antisocial or maladaptive dark triad‐based personality traits, including machiavellianism, narcissism, and psychopathy, on the focal antisocial stakeholder's, and his/her interactee', role‐related engagement, as therefore explored in this paper. To address these issues, we develop a conceptual model and an associated set of propositions that outline the nature of a stakeholder's machiavellian, narcissistic, and psychopathic role‐related engagement and its effect on his/her interactee's engagement. We conclude by outlining pertinent theoretical and managerial implications that arise from our analyses.
Over the last several decades, Gallup data shows an increased willingness among members of the public to support presidential candidates from a wide range of religious backgrounds, though a nontrivial proportion of the public is still unwilling to vote for an Atheist, Mormon, or Muslim. What underlies this opposition? We argue that voters evaluate candidates from religious out-groups more negatively on a wide range of dimensions considered desirable for political office, and that this bias should be more pronounced among the highly religious. We show support for these arguments using a survey experiment fielded with YouGov. Atheists and Muslim candidates were perceived more negatively on a large set of traits considered desirable for political office compared to candidates from religious in-groups, and Mormon candidates fall somewhere in between. The Atheist and Muslim candidates were also perceived as less competent on a diverse set of issues.
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1,231 members
Dale E. Berger
  • Department of Psychology
Lorne Olfman
  • School of Information Systems and Technology
Thomas A Horan
  • School of Information Systems and Technology
Jacek Kugler
  • School of Politics and Economics
William D Crano
  • Department of Applied Social Psychology
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