Booth Attractiveness: Scale Development and Model Testing from a Mental Budgeting Perspective

Given the importance of booth attractiveness at trade expositions, this study sets out to develop a scale measuring booth attractiveness (Study 1) and to examine its effectiveness in motivating attendees’ purchasing behavior (Study 2). Study 1 includes three steps: (1) item generation through a thorough review of the literature, focus group, and comments from experts, (2) item purification with exploratory factor analysis using 122 samples, and (3) reevaluating items with confirmatory factor analysis using 129 samples. A six-dimensional scale of booth attractiveness was developed in Study 1. Based on the theory of mental budgeting, Study 2 was conducted to examine the effects of booth attractiveness on the mechanism of attendees’ purchasing behavior using 323 samples. Results of Study 2 suggest that booth attractiveness could directly motivate impulse buying or indirectly through mental budgeting. Impulse buying, then, results in post-purchase guilt and anticipated satisfaction. Meanwhile, postpurchase guilt reduces anticipated satisfaction.

Transformer-Based Deep Neural Language Modeling for Construct-Specific Automatic Item Generation

Algorithmic automatic item generation can be used to obtain large quantities of cognitive items in the domains of knowledge and aptitude testing. However, conventional item models used by template-based automatic item generation techniques are not ideal for the creation of items for non-cognitive constructs. Progress in this area has been made recently by employing long short-term memory recurrent neural networks to produce word sequences that syntactically resemble items typically found in personality questionnaires. To date, such items have been produced unconditionally, without the possibility of selectively targeting personality domains. In this article, we offer a brief synopsis on past developments in natural language processing and explain why the automatic generation of construct-specific items has become attainable only due to recent technological progress. We propose that pre-trained causal transformer models can be fine-tuned to achieve this task using implicit parameterization in conjunction with conditional generation. We demonstrate this method in a tutorial-like fashion and finally compare aspects of validity in human- and machine-authored items using empirical data. Our study finds that approximately two-thirds of the automatically generated items show good psychometric properties (factor loadings above .40) and that one-third even have properties equivalent to established and highly curated human-authored items. Our work thus demonstrates the practical use of deep neural networks for non-cognitive automatic item generation.

Transformer-Based Deep Neural Language Modeling for Construct-Specific Automatic Item Generation

Algorithmic automatic item generation can be used to obtain large quantities of cognitive items in the domains of knowledge and aptitude testing. However, conventional item models used by template-based automatic item generation techniques are not ideal for the creation of items for non-cognitive constructs. Progress in this area has been made recently by employing long short-term memory recurrent neural networks to produce word sequences that syntactically resemble items typically found in personality questionnaires. To date, such items have been produced unconditionally, without the possibility of selectively targeting personality domains. In this article, we offer a brief synopsis on past developments in natural language processing and explain why the automatic generation of construct-specific items has become attainable only due to recent technological progress. We propose that pre-trained causal transformer models can be fine-tuned to achieve this task using implicit parameterization in conjunction with conditional generation. We demonstrate this method in a tutorial-like fashion and finally compare aspects of validity in human- and machine-authored items using empirical data. Our study finds that approximately two-thirds of the automatically generated items show good psychometric properties (factor loadings above .40) and that one-third even have properties equivalent to established and highly curated human-authored items. Our work thus demonstrates the practical use of deep neural networks for non-cognitive automatic item generation.

Applying Behaviour Change Models to Policymaking: Development and Validation of the Policymakers’ Information Use Questionnaire (POLIQ)

BackgroundThe purpose of this study was to develop and validate the Policymakers’ Information Use Questionnaire (POLIQ) to capture the intention of individuals in decision-making position, such as health policymakers, to act on research-based evidence, in order to inform theory and the application of behaviour change models to decision-making. MethodsThe development and validation comprised three steps: item generation, qualitative face validation, and factorial construct validation. Confirmatory factor analysis was applied to estimate item-domain correlations for five pre-defined constructs relating to content, beliefs, behaviour, control and intent. Cronbach’s alpha coefficient was calculated to assess overall consistency of questionnaire items with the pre-defined constructs. Participants in the item generation and face validation were health and policy researchers and two former decision-makers (former assistant deputy ministries) from Canadian provincial level. Participants in the construct validation were 39 Canadian decision-makers at various positions of municipal, provincial, and federal jurisdiction who participated in a series of policy dialogues focused on childhood disability research. ResultsInternal consistency of items belonging to the respective questionnaire domains was moderate to high with estimated Cronbach’s α values ranging from 0.67 to 0.84. Estimated item-domain correlations indicated moderate to high measurement performance for the domains norm, control and beliefs, whereas weak to moderate correlations resulted for the constructs content and intent. Estimate imprecisions of factor loadings (95% confidence interval widths) were considerable for the questionnaire domains content and intent. ConclusionThe study findings provide initial evidence on face validity and appropriate measurement properties of the POLIQ based on a convenient sample of decision-makers in social and health policy. Larger validation studies in relevant populations are needed to further establish psychometric properties and utility of the POLIQ.

Cough symptom severity in patients with refractory or unexplained chronic cough: a systematic survey and conceptual framework

BackgroundCough severity represents an important subjective endpoint in assessing the effectiveness of therapies for patients with chronic cough. Although cough-specific quality of life questionnaires exist, a widely available cough severity instrument with established measurement properties remains unavailable.AimsTo identify and summarise the results of studies reporting on the experience of patients with chronic cough and, in the process, develop a conceptual framework to inform development of a patient-reported outcome measurement (PROM) addressing cough severity.ResultsWe identified 61 eligible studies reporting on patient experience with chronic cough. Studies provided 82 potential items, of which 43 proved unique and relevant to cough severity. The urge-to-cough sensation and the cough symptom itself represented broad domains of cough severity. Two subdomains under urge-to-cough included frequency (1 item) and intensity (1 item). Five subdomains under cough symptoms included control (2 items), frequency (6 items), bout duration (1 item), intensity (8 items), and associated features/sequelae (24 items).ConclusionsOur systematic survey and conceptual framework identified items and domains of cough severity in patients with refractory or unexplained chronic cough. The results support item generation and content validity for a PROM assessing cough severity.

EXPRESS: Working On My Own: Measuring the Challenges of Gig Work

Gig workers commonly face challenges that differ in nature or intensity from those experienced by traditional organizational workers. To better understand and support gig workers, we sought to develop a measure that reliably and validly assesses these challenges. We first define gig work and specify its core characteristics. We then provide an integrated conceptual framework for a measure of six challenges commonly faced by gig workers—viability, organizational, identity, relational, emotional, and career-path uncertainty. We then present five studies: Item generation in Study 1; item reduction, exploratory assessment of the factor structure of these items, and initial tests of convergent validity in Study 2; and in the remaining three studies, we draw from different gig worker populations to accumulate evidence for the convergent, discriminant, and criterion validity of our gig work challenges inventory, and present initial tests of the universality of the gig challenge inventory across a range of socio-demographic, job type, and regional factors. Our findings establish the reliability and validity of a gig work challenge inventory (GWCI) that can aid researchers seeking to better understand the types and impact of stressors gig workers face, which in turn can help to inform theory, practice, and public policy.

Development and validation of a patient-centered, meningioma-specific quality-of-life questionnaire

OBJECTIVE Meningiomas can have significant impact on health-related quality of life (HRQOL). Patient-centered, disease-specific instruments for assessing HRQOL in these patients are lacking. To this end, the authors sought to develop and validate a meningioma-specific HRQOL questionnaire through a standardized, patient-centered questionnaire development methodology. METHODS The development of the questionnaire involved three main phases: item generation, item reduction, and validation. Item generation consisted of semistructured interviews with patients (n = 30), informal caregivers (n = 12), and healthcare providers (n = 8) to create a preliminary list of items. Item reduction with 60 patients was guided by the clinical impact method, multiple correspondence analysis, and hierarchical cluster analysis. The validation phase involved 162 patients and collected evidence on extreme-groups validity; concurrent validity with the SF-36, FACT-Br, and EQ-5D; and test-retest reliability. The questionnaire takes on average 11 minutes to complete. RESULTS The meningioma-specific quality-of-life questionnaire (MQOL) consists of 70 items representing 9 domains. Cronbach’s alpha for each domain ranged from 0.61 to 0.91. Concurrent validity testing demonstrated construct validity, while extreme-groups testing (p = 1.45E-11) confirmed the MQOL’s ability to distinguish between different groups of patients. CONCLUSIONS The MQOL is a validated, reliable, and feasible questionnaire designed specifically for evaluating QOL in meningioma patients. This disease-specific questionnaire will be fundamentally helpful in better understanding and capturing HRQOL in the meningioma patient population and can be used in both clinical and research settings.