Different Order, Different Results? The Effects of Dimension Order in Factorial Survey Experiments

Düval, Sabine; Hinz, Thomas

doi:10.1177/1525822x19886827

Cited by 13 publications

(7 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Three responses were required, and a fourth response was optional. This question was placed at the beginning of the questionnaire to avoid priming and order effects [ 27 , 28 ].…”

Section: Methodsmentioning

confidence: 99%

Psychological impacts from COVID-19 among university students: Risk factors across seven states in the United States

et al. 2021

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Background University students are increasingly recognized as a vulnerable population, suffering from higher levels of anxiety, depression, substance abuse, and disordered eating compared to the general population. Therefore, when the nature of their educational experience radically changes—such as sheltering in place during the COVID-19 pandemic—the burden on the mental health of this vulnerable population is amplified. The objectives of this study are to 1) identify the array of psychological impacts COVID-19 has on students, 2) develop profiles to characterize students' anticipated levels of psychological impact during the pandemic, and 3) evaluate potential sociodemographic, lifestyle-related, and awareness of people infected with COVID-19 risk factors that could make students more likely to experience these impacts. Methods Cross-sectional data were collected through web-based questionnaires from seven U.S. universities. Representative and convenience sampling was used to invite students to complete the questionnaires in mid-March to early-May 2020, when most coronavirus-related sheltering in place orders were in effect. We received 2,534 completed responses, of which 61% were from women, 79% from non-Hispanic Whites, and 20% from graduate students. Results Exploratory factor analysis on close-ended responses resulted in two latent constructs, which we used to identify profiles of students with latent profile analysis, including high (45% of sample), moderate (40%), and low (14%) levels of psychological impact. Bivariate associations showed students who were women, were non-Hispanic Asian, in fair/poor health, of below-average relative family income, or who knew someone infected with COVID-19 experienced higher levels of psychological impact. Students who were non-Hispanic White, above-average social class, spent at least two hours outside, or less than eight hours on electronic screens were likely to experience lower levels of psychological impact. Multivariate modeling (mixed-effects logistic regression) showed that being a woman, having fair/poor general health status, being 18 to 24 years old, spending 8 or more hours on screens daily, and knowing someone infected predicted higher levels of psychological impact when risk factors were considered simultaneously. Conclusion Inadequate efforts to recognize and address college students’ mental health challenges, especially during a pandemic, could have long-term consequences on their health and education.

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“…Three responses were required, and a fourth response was optional. This question was placed at the beginning of the questionnaire to avoid priming and order effects [ 27 , 28 ].…”

Section: Methodsmentioning

confidence: 99%

Psychological impacts from COVID-19 among university students: Risk factors across seven states in the United States

et al. 2021

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show abstract

“…Assuming that these two factors are not independent, violating one of the basic tenets of information theory (i.e., that this information is not i.i.d. ; for a review, see [ 20 ]), then

This last Equation ( 4 ) could represent an ordering effect commonly observed with questionnaires [ 99 ]. Instead, with it, we derive an uncertainty relation for a team between the interdependent factors

and

, creating a tradeoff between these two operators.…”

Section: Interdependence: the Mathematics Of Non-factorability Leads ...mentioning

confidence: 99%

A Quantum-like Model of Interdependence for Embodied Human–Machine Teams: Reviewing the Path to Autonomy Facing Complexity and Uncertainty

Lawless,

Moskowitz,

Doctor

2023

Entropy

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In this review, our goal is to design and test quantum-like algorithms for Artificial Intelligence (AI) in open systems to structure a human–machine team to be able to reach its maximum performance. Unlike the laboratory, in open systems, teams face complexity, uncertainty and conflict. All task domains have complexity levels—some low, and others high. Complexity in this new domain is affected by the environment and the task, which are both affected by uncertainty and conflict. We contrast individual and interdependence approaches to teams. The traditional and individual approach focuses on building teams and systems by aggregating the best available information for individuals, their thoughts, behaviors and skills. Its concepts are characterized chiefly by one-to-one relations between mind and body, a summation of disembodied individual mental and physical attributes, and degrees of freedom corresponding to the number of members in a team; however, this approach is characterized by the many researchers who have invested in it for almost a century with few results that can be generalized to human–machine interactions; by the replication crisis of today (e.g., the invalid scale for self-esteem); and by its many disembodied concepts. In contrast, our approach is based on the quantum-like nature of interdependence. It allows us theorization about the bistability of mind and body, but it poses a measurement problem and a non-factorable nature. Bistability addresses team structure and performance; the measurement problem solves the replication crisis; and the non-factorable aspect of teams reduces the degrees of freedom and the information derivable from teammates to match findings by the National Academies of Science. We review the science of teams and human–machine team research in the laboratory versus in the open field; justifications for rejecting traditional social science while supporting our approach; a fuller understanding of the complexity of teams and tasks; the mathematics involved; a review of results from our quantum-like model in the open field (e.g., tradeoffs between team structure and performance); and the path forward to advance the science of interdependence and autonomy.

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“…The questionnaire started with the ten vignettes presented in a randomized order. The group memberships within each vignette were always presented in the same order, since evidence suggests that order effects have little to no impact on judgments in factorial survey experiments with design parameters similar to ours (Auspurg & Jäckle, 2017;Düval & Hinz, 2020). Example of a vignette: "A young Tamil man.…”

Section: Procedures and Measuresmentioning

confidence: 99%

Differentiating Between Belief-Indicative and Status-Indicative Groups Improves Predictions of Intergroup Attitudes

Grigoryan

Jones

Cohrs

et al. 2022

Pers Soc Psychol Bull

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Ingroup bias is often treated as the default outcome of intergroup comparisons. We argue that the mechanisms of impression formation depend on what information people infer from groups. We differentiate between belief-indicative groups that are more informative of beliefs and affect attitudes through ingroup bias and status-indicative groups that are more informative of status and affect attitudes through a preference for higher status. In a cross-cultural factorial experiment ( Ntotal = 1,281), we demonstrate that when information about targets’ multiple group memberships is available, belief-indicative groups affect attitudes via ingroup bias, whereas status-indicative groups—via preference for higher status. These effects were moderated by social-structural context. In two follow-up studies ( Ntotal = 451), we develop and validate a measure of belief- and status-indicativeness (BISI) of groups. BISI showed expected correlations with related constructs of entitativity and essentialism. Belief-indicativeness of groups was a better predictor of ingroup bias than entitativity and essentialism.

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Different Order, Different Results? The Effects of Dimension Order in Factorial Survey Experiments

Cited by 13 publications

References 19 publications

Psychological impacts from COVID-19 among university students: Risk factors across seven states in the United States

Psychological impacts from COVID-19 among university students: Risk factors across seven states in the United States

A Quantum-like Model of Interdependence for Embodied Human–Machine Teams: Reviewing the Path to Autonomy Facing Complexity and Uncertainty

Differentiating Between Belief-Indicative and Status-Indicative Groups Improves Predictions of Intergroup Attitudes

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