1,721,235 research outputs found
sj-docx-1-ssc-10.1177_0894439320928242 – Supplemental Material for Analyzing Nonresponse in Longitudinal Surveys Using Bayesian Additive Regression Trees: A Nonparametric Event History Analysis
No full textSupplemental Material, sj-docx-1-ssc-10.1177_0894439320928242 for Analyzing Nonresponse in Longitudinal Surveys Using Bayesian Additive Regression Trees: A Nonparametric Event History Analysis by Sabine Zinn and Timo Gnambs in Social Science Computer Review</p
Online_Supplement_1Feb19 – Supplemental material for Are Social Media Ruining Our Lives? A Review of Meta-Analytic Evidence
No full textSupplemental material, Online_Supplement_1Feb19 for Are Social Media Ruining Our Lives? A Review of Meta-Analytic Evidence by Markus Appel, Caroline Marker and Timo Gnambs in Review of General Psychology</p
sj-pdf-1-jpa-10.1177_07342829221077503 – Supplemental Material for Parent and Teacher Assessments of Social-emotional Competence in Three-Year-Old Children: Does Sibling Status Matter?
No full textSupplemental Material, sj-pdf-1-jpa-10.1177_07342829221077503 for Parent and Teacher Assessments of Social-emotional Competence in Three-Year-Old Children: Does Sibling Status Matter? in Carina Schönmoser, Claudia Karwath, and Timo Gnambs in Journal of Psychoeducational Assessment</p
sj-docx-1-asm-10.1177_10731911221149949 – Supplemental material for The Predictive Validity of Item Effect Variables in the Satisfaction With Life Scale for Psychological and Physical Health
No full textSupplemental material, sj-docx-1-asm-10.1177_10731911221149949 for The Predictive Validity of Item Effect Variables in the Satisfaction With Life Scale for Psychological and Physical Health by Marie-Ann Sengewald, Tina H. Erhardt and Timo Gnambs in Assessment</p
sj-docx-1-asm-10.1177_10731911211052483 – Supplemental material for The Dimensionality of the Brief COPE Before and During the COVID-19 Pandemic
No full textSupplemental material, sj-docx-1-asm-10.1177_10731911211052483 for The Dimensionality of the Brief COPE Before and During the COVID-19 Pandemic by Barbara Hanfstingl, Timo Gnambs, Christian Fazekas, Katharina Ingrid Gölly, Franziska Matzer and Matias Tikvić in Assessment</p
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
A brief note on the standard error of the Pearson correlation
No full textThe product-moment correlation is a central statistic in exploratory and confirmatory research including longitudinal and meta-analytic applications. Unfortunately, it has a rather complex sampling distribution which leads to sample correlations that are biased indicators of the respective population correlations. Moreover, there seems to be some uncertainty on how to properly calculate the standard error of these correlations. Because no simple analytical solution exists, several approximations have been previously introduced. This note aims to briefly summarize 10 different ways to calculate the standard error of the Pearson correlation. Moreover, a simulation study on the accuracy of these estimators compared their relative percentage biases for different population correlations and sample sizes. The results showed that all estimators were largely unbiased for sample sizes of at least 50. For smaller samples, a simple approximation by Bonnett and Wright (2000) led to the least biased results. Based on these results, it is recommended to use the expression \sfrac{\left(1-r^2\right)}{\sqrt{N-3}} for the calculation of the standard error of the Pearson correlation
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