Analysis of Categorical Data with the R Package confreq

The person-centered approach in categorical data analysis is introduced as a complementary approach to the variable-centered approach. The former uses persons, animals, or objects on the basis of their combination of characteristics which can be displayed in multiway contingency tables. Configural Frequency Analysis (CFA) and log-linear modeling (LLM) are the two most prominent (and related) statistical methods. Both compare observed frequencies (foi…k) with expected frequencies (fei…k). While LLM uses primarily a model-fitting approach, CFA analyzes residuals of non-fitting models. Residuals with significantly more observed than expected frequencies (foi…k>fei…k) are called types, while residuals with significantly less observed than expected frequencies (foi…k<fei…k) are called antitypes. The R package confreq is presented and its use is demonstrated with several data examples. Results of contingency table analyses can be displayed in tables but also in graphics representing the size and type of residual. The expected frequencies represent the null hypothesis and different null hypotheses result in different expected frequencies. Different kinds of CFAs are presented: the first-order CFA based on the null hypothesis of independence, CFA with covariates, and the two-sample CFA. The calculation of the expected frequencies can be controlled through the design matrix which can be easily handled in confreq.

Configural analysis of oscillating progression

Oscillating series of scores can be approximated with locally optimized smoothing functions. In this article, we describe how such series can be approximated with locally estimated (loess) smoothing, and how Configural Frequency Analysis (CFA) can be used to evaluate and interpret results. Loess functions are often hard to describe because they cannot be represented by just one function that has interpretable parameters. In this article, we suggest that specification of the CFA base model be based on the width of the window that is used for local curve optimization, the weight given to data points in the neighborhood of the approximated one, and by the function that is used to locally approximate observed data. CFA types indicate that more cases were found than expected from the local optimization model. CFA antitypes indicate that fewer cases were found. In a real-world data example, the development of Covid-19 diagnoses in France is analyzed for the beginning period of the pandemic.

A multivariate approach to English Clippings

This paper addresses the morphological word formation process that is known as clipping. In English, that process yields shortened word forms such as lab (< laboratory), exam (< examination), or gator (< alligator). It is frequently argued (Davy 2000, Durkin 2009, Haspelmath & Sims 2010, Don 2014) that clipping is highly variable and that it is difficult to predict how a given source word will be shortened. We draw on recent work (Lappe 2007, Jamet 2009, Berg 2011, Alber & Arndt-Lappe 2012, Arndt-Lappe 2018) in order to challenge that view. Our main hypothesis is that English clipping follows predictable tendencies, that these tendencies can be captured by a probabilistic, multifactorial model, and that the features of that model can be explained functionally in terms of cognitive, discourse-pragmatic, and phonological factors. Cognitive factors include the principle of least effort (Zipf 1949), an important discourse-pragmatic factor is the recoverability of the source word (Tournier 1985), and phonological factors include issues of stress and syllable structure (Lappe 2007). While the individual influence of these factors on clipping has been recognized, their interaction and their relative importance remains to be fully understood. The empirical analysis in this paper will use Hierarchical Configural Frequency Analysis (Krauth & Lienert 1973, Gries 2008) on the basis of a large, newly compiled database of more than 2000 English clippings. Our analysis allows us to detect regularities in the way speakers of English create clippings. We argue that there are several English clipping schemas that are optimized for processability.

Person-centered data analysis with covariates and the R-package confreq

Configural Frequency Analysis (CFA) is a useful statistical method for the analysis of multiway contingency tables and an appropriate tool for person-oriented or person-centered methods. In complex contingency tables, patterns or configurations are analyzed by comparing observed cell frequencies with expected frequencies. Significant differences between observed and expected frequencies lead to the emergence of Types and Antitypes. Types are patterns or configurations which are significantly more often observed than the expected frequencies; Antitypes represent configurations which are observed less frequently than expected. The R-package confreq is an easy-to-use software for conducting CFAs; another useful shareware to run CFAs was developed by Alexander von Eye. Here, CFA is presented based on the log-linear modeling approach. CFA may be used together with interval level variables which can be added as covariates into the design matrix. In this article, a real data example and the use of confreq are presented. In sum, the use of a covariate may bring the estimated cell frequencies closer to the observed cell frequencies. In those cases, the number of Types or Antitypes may decrease. However, in rare cases, the Type-Antitype pattern can change with new emerging Types or Antitypes.

Configural frequency trees

Although variable-oriented analyses are dominant in developmental psychopathology, researchers have championed a person-oriented approach that focuses on the individual as a totality. This view has methodological implications and various person-oriented methods have been developed to test person-oriented hypotheses. Configural frequency analysis (CFA) has been identified as a prime method for a person-oriented analysis of categorical data. CFA searches for configurations in cross-classifications and asks whether the number of observed cases is larger (CFA type) or smaller (CFA antitype) than expected under a probability model. The present study introduces a combination of CFA and model-based recursive partitioning (MOB) to test for type/antitype heterogeneity in the population. MOB CFA is well suited to detect complex moderation processes and can distinguish between subpopulation and population types/antitypes. Model specifications are discussed for first-order CFA and prediction CFA. Results from two simulation studies suggest that MOB CFA is able to detect moderation processes with high accuracy. Two empirical examples are given from school mental health research for illustrative purposes. The first example evaluates heterogeneity in student behavior types/antitypes, the second example focuses on the effect of a teacher classroom management intervention on student behavior. An implementation of the approach is provided in R.

Snus cessation patterns - a long-term follow-up of snus users in Sweden

Background Several studies have investigated the role of snus as an aid to become smoke-free, but few have focused on who use snus, how they perceive snus use, why and how they quit, and their perception of being non-snus users. The purpose of this paper is to describe snus cessation patterns. Methods Respondents are part of a 7-year follow-up of former smokers in Sweden. Initially, 1400 respondents were contacted regarding participation and 705 answered a web-based survey (response rate 50%). Out of them, 118 had used snus. The analyses include percentage distributions, as well as factor analyses of inventories, and configural frequency analysis in order to examine configurations of snus-related patterns. Results Over 80% found snus of great importance to succeed with smoking cessation and half of them continued to use snus on a long term. Those who experienced both physical and psychological effects of switching to snus were the ones who continued and vice versa; those who did not experience such effects quit using snus. None made use of professional help but had their own strategies (60%), and most respondents who quit obtained psychological benefits (68%). Conclusions The distinction between the concepts smoke-free, tobacco-free, and nicotine-free contributes to nuances in the debate on snus as harm reduction. Continued snus use does not mean that snus is not an effective aid to become smoke-free. Snus cessation is mostly mentioned in relation to advices on how to succeed, but the cessation process has rarely been described; therefore, this study expands the knowledge on this quite neglected topic and contributes to a more nuanced picture of snus cessation.