Communication Methods and Measures 

About: Communication Methods and Measures is an academic journal published by Taylor & Francis. The journal publishes majorly in the area(s): Computer science & Argument. It has an ISSN identifier of 1931-2458. Over the lifetime, 289 publications have been published receiving 14789 citations. The journal is also known as: CMM.

Answering the Call for a Standard Reliability Measure for Coding Data

Abstract: In content analysis and similar methods, data are typically generated by trained human observers who record or transcribe textual, pictorial, or audible matter in terms suitable for analysis. Conclusions from such data can be trusted only after demonstrating their reliability. Unfortunately, the content analysis literature is full of proposals for so-called reliability coefficients, leaving investigators easily confused, not knowing which to choose. After describing the criteria for a good measure of reliability, we propose Krippendorff's alpha as the standard reliability measure. It is general in that it can be used regardless of the number of observers, levels of measurement, sample sizes, and presence or absence of missing data. To facilitate the adoption of this recommendation, we describe a freely available macro written for SPSS and SAS to calculate Krippendorff's alpha and illustrate its use with a simple example.

Advantages of Monte Carlo Confidence Intervals for Indirect Effects

Abstract: Monte Carlo simulation is a useful but underutilized method of constructing confidence intervals for indirect effects in mediation analysis. The Monte Carlo confidence interval method has several distinct advantages over rival methods. Its performance is comparable to other widely accepted methods of interval construction, it can be used when only summary data are available, it can be used in situations where rival methods (e.g., bootstrapping and distribution of the product methods) are difficult or impossible, and it is not as computer-intensive as some other methods. In this study we discuss Monte Carlo confidence intervals for indirect effects, report the results of a simulation study comparing their performance to that of competing methods, demonstrate the method in applied examples, and discuss several software options for implementation in applied settings.

Use Omega Rather than Cronbach’s Alpha for Estimating Reliability. But…

Abstract: Cronbach’s alpha (α) is a widely-used measure of reliability used to quantify the amount of random measurement error that exists in a sum score or average generated by a multi-item measurement scal...

Item Parceling in Structural Equation Modeling: A Primer

Abstract: This article provides a primer of the statistical technique called parceling, or aggregating items and using those aggregates as indicators of latent constructs, for structural equation modeling (SEM). First, two major types of parceling (subset-item-parcel and all-item-parcel approaches), alongside the traditional item-based approach, are illustrated. Second, both pros and cons of parceling are explicated. Parceling provides psychometric and modeling-related benefits. Risks associated with parceling are also noted. Particularly, potential induction of estimation bias and model misspecification are pointed out. In relation to the latter problem, unidimensionality of the scale is highlighted as an important prerequisite for the use of parceling. Finally, issues of the number of parcels per factor and parcel-building algorithms are discussed. Forming three parcels per factor by the random algorithm is recommended. Suggestions and general guidelines for the use of item parceling for SEM are also provided.

Goodbye, Listwise Deletion: Presenting Hot Deck Imputation as an Easy and Effective Tool for Handling Missing Data

Abstract: Missing data are a ubiquitous problem in quantitative communication research, yet the missing data handling practices found in most published work in communication leave much room for improvement. In this article, problems with current practices are discussed and suggestions for improvement are offered. Finally, hot deck imputation is suggested as a practical solution to many missing data problems. A computational tool for SPSS (Statistical Package for the Social Sciences) is presented that will enable communication researchers to easily implement hot deck imputation in their own analyses.