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Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe 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
Get PDF“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
Robust Standard Errors and Confidence Intervals for Standardized Mean Difference
No full textThe Student's t-test assumes common population variance for testing raw score mean difference (RSMD). The Cohen-Glass-Hedges standardized mean difference (SMD) align with this assumption and are primarily applicable to experimental designs where an intervention affect mean but not variance. However, the intervention may increase or decrease variance due to differential responses driven by individual differences. Furthermore, variances may differ between natural groups in observational data, a phenomenon that is well-documented in various fields of research. One prevalent suggestion is to use the Welch-Satterthwaite t-test for RSMD. Nonetheless, SMD counterparts has been lagging behind. Various methods has been offered in the literature but they often either fail to preserve the definitions of Cohen-Glass-Hedges SMD or are inconsistent with ordinary least square (OLS) framework. This study proposes a consistent framework between RSMD tests and SMDs using OLS sandwich estimators (one of which is precisely same as the Welch-Satterthwaite t-test). For each proposed RSMD testing procedure, we derive a consistent SMD counterpart using the delta method and construct confidence intervals that are based on lambda-prime distribution. Compared to conventional Cohen-Glass-Hedges SMD, these new indices offer efficiency gains, allowing direct application in hypothesis testing of point null, non-inferiority, superiority, equivalence, and minimal effect testing even without raw data. They also enable more precise power calculations. Finally, they improve efficiency of between-study heterogeneity indices in meta-analysis. Implications are discussed and some guidelines are provided
Sample Size Determination and Optimal Design of Randomized/Non-equivalent Pretest-posttest Control-group Designs
No full textA recent systematic review of experimental studies conducted in Turkey between 2010 and 2020 reported that small sample sizes had been a significant drawback (Bulus & Koyuncu, 2021). A small chunk of the studies were randomized pretest-posttest control-group designs, whereas the overwhelming majority were non-equivalent pretest-posttest control-group designs (no randomization). They had an average sample size below 70 for different domains and outcomes. Designing experimental studies with such small sample sizes implies a strong (and perhaps erroneous) assumption about the minimum relevant effect size (MRES) of an intervention; that is, a standardized treatment effect of Cohen’s d < 0.50 is not relevant to education policy or practice. Thus, an introduction to sample size determination for randomized/non-equivalent pretest-posttest control group designs is warranted. This study describes nuts and bolts of sample size determination, derives expressions for optimal design under differential cost per treatment and control units, provides convenient tables to guide sample size decisions for MRES values between 0.20 ≤ Cohen’s d ≤ 0.50
Randomize/Eşdeğer Olmayan Öntest-Sontest Kontrol Gruplu Tasarımların Örneklem Büyüklüğünün Belirlenmesi ve Optimal Tasarımı
No full textAppropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe 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
Consequences of Ignoring a Level of Nesting on Design and Analysis of Blocked Three-level Regression Discontinuity Designs: Power and Type I Error Rates
No full textMultilevel regression discontinuity designs have been increasingly used in education research to evaluate the effectiveness of policy and programs. It is common to ignore a level of nesting in a three-level data structure (students nested in classrooms/teachers nested in schools), whether unwittingly during data analysis or due to resource constraints during the planning phase. This study investigates the consequences of ignoring intermediate or top level in blocked three-level regression discontinuity designs (BIRD3; treatment is at level 1) during data analysis and planning. Monte Carlo simulation results indicated that ignoring a level during analysis did not affect the accuracy of treatment effect estimates; however, it affected the precision (standard errors, power, and Type I error rates). Ignoring the intermediate level did not cause a significant problem. Power rates were slightly underestimated, whereas Type I error rates were stable. In contrast, ignoring a top-level resulted in overestimated power rates; however, severe inflation in Type I error deemed this strategy ineffective. As for the design phase, when the intermediate level was ignored, it is viable to use parameters from a two-level blocked regression discontinuity model (BIRD2) to plan a future BIRD3 design. However, level 2 parameters from the BIRD2 model should be substituted for level 3 parameters in the BIRD3 design. When the top level was ignored, using parameters from the BIRD2 model to plan a future BIRD3 design should be avoided
Sample Size Determination and Optimal Design of Randomized/Non-equivalent Pretest-posttest Control-group Designs
No full textA recent systematic review of experimental studies conducted in Turkey between 2010 and 2020 reported that small sample sizes had been a significant drawback (Bulus & Koyuncu, 2021). A small chunk of the studies were randomized pretest-posttest control-group designs, whereas the overwhelming majority were non-equivalent pretest-posttest control-group designs (no randomization). They had an average sample size below 70 for different domains and outcomes. Designing experimental studies with such small sample sizes implies a strong (and perhaps erroneous) assumption about the minimum relevant effect size (MRES) of an intervention; that is, a standardized treatment effect of Cohen’s d < 0.50 is not relevant to education policy or practice. Thus, an introduction to sample size determination for randomized/non-equivalent pretest-posttest control group designs is warranted. This study describes nuts and bolts of sample size determination, derives expressions for optimal design under differential cost per treatment and control units, provides convenient tables to guide sample size decisions for MRES values between 0.20 ≤ Cohen’s d ≤ 0.50
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