Showing papers by "Erin Michelle Buchanan published in 2018"
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Eindhoven University of Technology1, Max Planck Society2, National Scientific and Technical Research Council3, University of Groningen4, Flinders University5, University of Oxford6, Illinois Institute of Technology7, Nottingham Trent University8, Bielefeld University9, University of Nevada, Las Vegas10, University of Wisconsin-Madison11, Missouri State University12, University of Arkansas13, Katholieke Universiteit Leuven14, Leiden University15, Linnaeus University16, Tzu Chi University17, University of British Columbia18, University of Cambridge19, University of Edinburgh20, University of Glasgow21, Bangor University22, Linköping University23, Florida State University24, Yale University25, University of Louisiana at Lafayette26, University of Texas at Austin27, St. Edward's University28, West Virginia University29, Rutgers University30, Indiana University31, RWTH Aachen University32, Keele University33, University of Tübingen34, Radboud University Nijmegen35, University of Chester36, New York University37, University of Nottingham38, Erasmus University Rotterdam39, University of Bristol40, Sahlgrenska University Hospital41, Adam Mickiewicz University in Poznań42, University of Connecticut43, Charité44, Humboldt University of Berlin45, University of Fribourg46, University of Kent47, Academy of Sciences of the Czech Republic48, RAND Corporation49, Baylor University50, Virginia Tech51, Northern Illinois University52, Open University53, King's College London54, Stockholm University55, Stanford University56, Karolinska Institutet57, Universidade Federal de Santa Catarina58, University of Tromsø59, DePaul University60, Boğaziçi University61, University of Cologne62, King Abdulaziz City for Science and Technology63, University of Leeds64, Center for Open Science65, University of Virginia66, National Institutes of Health67, University of Southern Indiana68, Autonomous University of Madrid69, Utrecht University70, Tilburg University71, Massey University72, Saint Louis University73, University of California, Davis74, Ghent University75
TL;DR: In response to recommendations to redefine statistical significance to P ≤ 0.005, it is proposed that researchers should transparently report and justify all choices they make when designing a study, including the alpha level.
Abstract: In response to recommendations to redefine statistical significance to P ≤ 0.005, we propose that researchers should transparently report and justify all choices they make when designing a study, including the alpha level.
296 citations
TL;DR: This algorithm can be a promising tool to identify low quality or automated data via AMT or other online data collection platforms and be used as part of sensitivity analyses to warrant exclusion from further analyses.
Abstract: Web-based data collection methods such as Amazon's Mechanical Turk (AMT) are an appealing option to recruit participants quickly and cheaply for psychological research. While concerns regarding data quality have emerged with AMT, several studies have exhibited that data collected via AMT are as reliable as traditional college samples and are often more diverse and representative of noncollege populations. The development of methods to screen for low quality data, however, has been less explored. Omitting participants based on simple screening methods in isolation, such as response time or attention checks may not be adequate identification methods, with an inability to delineate between high or low effort participants. Additionally, problematic survey responses may arise from survey automation techniques such as survey bots or automated form fillers. The current project developed low quality data detection methods while overcoming previous screening limitations. Multiple checks were employed, such as page response times, distribution of survey responses, the number of utilized choices from a given range of scale options, click counts, and manipulation checks. This method was tested on a survey taken with an easily available plug-in survey bot, as well as compared to data collected by human participants providing both high effort and randomized, or low effort, answers. Identified cases can then be used as part of sensitivity analyses to warrant exclusion from further analyses. This algorithm can be a promising tool to identify low quality or automated data via AMT or other online data collection platforms.
103 citations
TL;DR: The findings obtained confirmed that suitability of the Polish IGDS9-SF to assess IGD amongst Polish gamers given the adequate levels of validity and reliability found, and suggest that some of the diagnostic criteria may present with a different clinical weighting towards final diagnosis of IGD.
71 citations
TL;DR: Overall, researchers can expect to find medium to large survival-processing effects, with selective reporting and bias-correcting techniques typically estimating lower effect sizes than traditional meta-analytic techniques.
Abstract: The survival-processing advantage occurs when processing words for their survival value improves later performance on a memory test. Due to the interest in this topic, we conducted a meta-analysis to review the literature regarding the survival-processing advantage, in order to estimate a bias-corrected effect size. Traditional meta-analytic methods were used, as well as the test of excess significance, p-curve, p-uniform, trim and fill, PET-PEESE, and selection models, to reevaluate previous effect sizes while controlling for forms of small-study-size effects. The average effect sizes for survival processing ranged between η p2 = .06 and .09 for between-subjects experiments and between η p2 = .15 and .18 for within-subjects experiments, after correcting for potential bias and selective reporting. Overall, researchers can expect to find medium to large survival-processing effects, with selective reporting and bias-correcting techniques typically estimating lower effect sizes than traditional meta-analytic techniques.
31 citations
TL;DR: In this paper, the authors examined the differences in scale relationships for randomized and nonrandomized computer delivery for two scales measuring meaning/purpose in life, i.e., suicidality, depression, and life goals.
Abstract: Scales that are psychometrically sound, meaning those that meet established standards regarding reliability and validity when measuring one or more constructs of interest, are customarily evaluated based on a set modality (i.e., computer or paper) and administration (fixed-item order). Deviating from an established administration profile could result in non-equivalent response patterns, indicating the possible evaluation of a dissimilar construct. Randomizing item administration may alter or eliminate these effects. Therefore, we examined the differences in scale relationships for randomized and nonrandomized computer delivery for two scales measuring meaning/purpose in life. These scales have questions about suicidality, depression, and life goals that may cause item reactivity (i.e., a changed response to a second item based on the answer to the first item). Results indicated that item randomization does not alter scale psychometrics for meaning in life scales, which implies that results are comparable even if researchers implement different delivery modalities.
3 citations
23 Jan 2018
TL;DR: The authors used Latent Semantic Analysis to determine whether similarities in personality predicted similarities in responses to a romantic writing prompt (Landauer & Dumais, 1997) and calculated thematic cosines (a measure of relatedness) between each male and female participant.
Abstract: This study utilized Latent Semantic Analysis to determine whether similarities in personality predicted similarities in responses to a romantic writing prompt (Landauer & Dumais, 1997). From participants’ writing samples, we calculated thematic cosines (a measure of relatedness) between each male and female participant. Participants also completed the Big Five Personality Questionnaire Short Form (Morizet, 2014). Extraversion, agreeableness, and conscientiousness were related to cosines, which suggested small-medium relationships from personality traits to written responses. This relationship was consistent with previous studies; therefore, Latent Semantic Analysis may be useful in quantifying mate preference, especially when alongside traditional survey methods. We conclude with a discussion of the compatibility of ordinal measures (survey data) and continuous measures in examining complex phenomena in the Behavioral Sciences.
1 citations
26 Jan 2018
TL;DR: In this paper, the authors investigated the role of anxiety buffering in posttraumatic stress disorder (PTSD) and found that meaning in life, self-esteem, and social intimacy were significant predictors of lower levels of PTSD.
Abstract: Background and ObjectivesAnxiety buffer disruption theory (ABDT) is an application of terror management theory to posttraumatic stress disorder (PTSD). ABDT predicts that posttraumatic stress reactions occur when buffers of awareness of death, such as meaning in life, self-esteem, and social intimacy, fail to suppress overwhelming death-anxiety. In this study, we hypothesized that generativity may also serve as an effective buffer of awareness of death and PTSD. Design The present study investigated the presence of anxiety buffering disruption in first responders with a spectrum of posttraumatic stress via a mediation path model of self-report measures of PTSD symptoms, anxiety buffer variables, and death-though accessibility. MethodsTo investigate the role of anxiety buffering in PTSD, a sample of 986 first responders completed self-report measures of PTSD symptoms and anxiety buffer variables in randomized order, and a death-thought accessibility measure following random assignment to mortality salience (n = 290) or control (n = 302) conditioning. Results and ConclusionWhile results indicate PTSD symptoms have a small relation to increased awareness of death, results indicate anxiety buffering variables did not mediate the relation between PTSD symptoms and awareness of death. Nonetheless, generativity and meaning in life, self-esteem, and social support were significant predictors of lower levels of PTSD.
1 citations
04 Jul 2018
TL;DR: In this paper, a questionnaire containing a set of hard and easy general knowledge questions followed by the Metacognitive Awareness Inventory was used to investigate the relationship between metacognitive awareness and confidence.
Abstract: Making judgments is an important part of everyday life, and overconfidence in these judgments can lead to serious consequences. Two potential factors influencing overconfidence are metacognitive awareness, or the awareness of one’s own learning, and the hard-easy effect, which states that overconfidence is more prevalent in difficult tasks while underconfidence is more prevalent in easy tasks. Overall, we hypothesized that participants’ metacognitive awareness would significantly relate to their overconfidence levels. Specific hypotheses were that those participants who display higher levels of metacognitive awareness will have lower levels of overconfidence, that harder questions will elicit higher levels of overconfidence and easy questions will elicit underconfidence (congruent with the hard-easy effect), and that the lower range and upper range will on average be equal, with the exact estimate as the midpoint. Participants (N = 49) completed a questionnaire containing a set of hard and easy general knowledge questions followed by the Metacognitive Awareness Inventory. The correlation between metacognitive awareness and confidence was negative for hard questions and positive for easy questions. Furthermore, the ranges for easy questions were smaller, resulting in more overconfidence, and the ranges for the hard questions were larger, resulting in underconfidence, thus, showing the opposite of our expected hypotheses.