Erin Michelle Buchanan

Bio: Erin Michelle Buchanan is an academic researcher from Harrisburg University of Science and Technology. The author has contributed to research in topics: Psychology & Medicine. The author has an hindex of 19, co-authored 73 publications receiving 1326 citations. Previous affiliations of Erin Michelle Buchanan include Missouri State University & University of Cape Town.

Justify your alpha

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.

The Purpose in Life Test-Short Form: Development and Psychometric Support

Abstract: This study’s purpose was to examine the psychometric properties for a brief, four-item form of the 20-item Purpose in Life test (PIL-SF). Confirmatory factor-analytic procedures were used to demonstrate how well the items (3, 4, 8, and 20) fit together. Reliability and descriptive data for the PIL-SF are provided, as well as correlations with other measures of well-being and psychological distress. Data are provided with respect to whether the four items are administered independently or within the larger parent form. The final aspect of the study evaluated whether the PIL-SF was of utility in predicting psychological distress, above and beyond other measures of meaning. This study utilized data from undergraduates (N = 298) from a medium-sized university located in the southern United States. The PIL-SF was supported via confirmatory factor analysis, the measure yields reliable scores, and these scores correlate significantly and as expected with the other measures administered. Data were similar regardless of whether items were administered independently or embedded in the 20-item PIL. Finally, hierarchical regression demonstrated that PIL-SF scores are useful in predicting psychological distress, offering a psychometric contribution beyond other measures of meaning.

The semantic priming project

Abstract: Speeded naming and lexical decision data for 1,661 target words following related and unrelated primes were collected from 768 subjects across four different universities. These behavioral measures have been integrated with demographic information for each subject and descriptive characteristics for every item. Subjects also completed portions of the Woodcock–Johnson reading battery, three attentional control tasks, and a circadian rhythm measure. These data are available at a user-friendly Internet-based repository ( http://spp.montana.edu ). This Web site includes a search engine designed to generate lists of prime–target pairs with specific characteristics (e.g., length, frequency, associative strength, latent semantic similarity, priming effect in standardized and raw reaction times). We illustrate the types of questions that can be addressed via the Semantic Priming Project. These data represent the largest behavioral database on semantic priming and are available to researchers to aid in selecting stimuli, testing theories, and reducing potential confounds in their studies.

Methods to detect low quality data and its implication for psychological research

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.

Using Multivariate Statistics

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Applied Missing Data Analysis

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Ethical Principles of Psychologists and Code of Conduct

Abstract: 1. Resolving Ethical Issues 1.01 Misuse of Psychologists’ Work 1.02 Conflicts Between Ethics and Law, Regulations, or Other Governing Legal Authority 1.03 Conflicts Between Ethics and Organizational Demands 1.04 Informal Resolution of Ethical Violations 1.05 Reporting Ethical Violations 1.06 Cooperating With Ethics Committees 1.07 Improper Complaints 1.08 Unfair Discrimination Against Complainants and Respondents

Why most published research findings are false

Abstract: There is increasing concern that most current published research findings are false. The probability that a research claim is true may depend on study power and bias, the number of other studies on the same question, and, importantly, the ratio of true to no relationships among the relationships probed in each scientific field. In this framework, a research finding is less likely to be true when the studies conducted in a field are smaller; when effect sizes are smaller; when there is a greater number and lesser preselection of tested relationships; where there is greater flexibility in designs, definitions, outcomes, and analytical modes; when there is greater financial and other interest and prejudice; and when more teams are involved in a scientific field in chase of statistical significance. Simulations show that for most study designs and settings, it is more likely for a research claim to be false than true. Moreover, for many current scientific fields, claimed research findings may often be simply accurate measures of the prevailing bias. In this essay, I discuss the implications of these problems for the conduct and interpretation of research.