Thomas Rusch

Capturing the Familiness of Family Businesses: Development of the Family Influence Familiness Scale (FIFS)

This article develops a familiness scale measuring the family influence on the business via decision premises that express familiness. In three studies, we examine familiness with qualitative and quantitative approaches based on new systems theory. Narrative interviews are employed to generate items. Exploratory and confirmatory factor analyses led to a multidimensional scale (Family Influence Familiness Scale [FIFS]) comprising six dimensions: (1) ownership, management, and control; (2) proficiency level of active family members; (3) sharing of information between active family members; (4) transgenerational orientation; (5) family–employee bond; and (6) family business identity. Results indicate high reliability and validity levels.

Goodness-of-Fit Assessment in Multidimensional Scaling and Unfolding

ABSTRACT Judging goodness of fit in multidimensional scaling requires a comprehensive set of diagnostic tools instead of relying on stress rules of thumb. This article elaborates on corresponding strategies and gives practical guidelines for researchers to obtain a clear picture of the goodness of fit of a solution. Special emphasis will be placed on the use of permutation tests. The second part of the article focuses on goodness-of-fit assessment of an important variant of multidimensional scaling called unfolding, which can be applied to a broad range of psychological data settings. Two real-life data sets are presented in order to walk the reader through the entire set of diagnostic measures, tests, and plots. R code is provided as supplementary information that makes the whole goodness-of-fit assessment workflow, as presented in this article, fully reproducible.

Gaining insight with recursive partitioning of generalized linear models

Recursive partitioning algorithms separate a feature space into a set of disjoint rectangles. Then, usually, a constant in every partition is fitted. While this is a simple and intuitive approach, it may still lack interpretability as to how a specific relationship between dependent and independent variables may look. Or it may be that a certain model is assumed or of interest and there is a number of candidate variables that may non-linearly give rise to different model parameter values. We present an approach that combines generalized linear models (GLM) with recursive partitioning that offers enhanced interpretability of classical trees as well as providing an explorative way to assess a candidate variables influence on a parametric model. This method conducts recursive partitioning of a GLM by (1) fitting the model to the data set, (2) testing for parameter instability over a set of partitioning variables, (3) splitting the data set with respect to the variable associated with the highest instability. The outcome is a tree where each terminal node is associated with a GLM. We will show the methods versatility and suitability to gain additional insight into the relationship of dependent and independent variables by two examples, modelling voting behaviour and a failure model for debt amortization, and compare it to alternative approaches.

Breaking free from the limitations of classical test theory

Information systems (IS) research frequently uses survey data to measure the interplay between technological systems and human beings. Researchers have developed sophisticated procedures to build and validate multi-item scales that measure latent constructs. The vast majority of IS studies uses classical test theory (CTT), but this approach suffers from three major theoretical shortcomings: (1) it assumes a linear relationship between the latent variable and observed scores, which rarely represents the empirical reality of behavioral constructs; (2) the true score can either not be estimated directly or only by making assumptions that are difficult to be met; and (3) parameters such as reliability, discrimination, location, or factor loadings depend on the sample being used. To address these issues, we present item response theory (IRT) as a collection of viable alternatives for measuring continuous latent variables by means of categorical indicators (i.e., measurement variables). IRT offers several advantages: (1) it assumes nonlinear relationships; (2) it allows more appropriate estimation of the true score; (3) it can estimate item parameters independently of the sample being used; (4) it allows the researcher to select items that are in accordance with a desired model; and (5) it applies and generalizes concepts such as reliability and internal consistency, and thus allows researchers to derive more information about the measurement process. We use a CTT approach as well as Rasch models (a special class of IRT models) to demonstrate how a scale for measuring hedonic aspects of websites is developed under both approaches. The results illustrate how IRT can be successfully applied in IS research and provide better scale results than CTT. We conclude by explaining the most appropriate circumstances for applying IRT, as well as the limitations of IRT.

Linear Logistic Models with Relaxed Assumptions in R

Linear logistic models with relaxed assumptions (LLRA) are a flexible tool for item-based measurement of change or multidimensional Rasch models. Their key features are to allow for multidimensional items and mutual dependencies of items as well as imposing no assumptions on the distribution of the latent trait in the population. Inference for such models becomes possible within a framework of conditional maximum likelihood estimation. In this paper we introduce and illustrate new functionality from the R package eRm for fitting, comparing and plotting of LLRA models for dichotomous and polytomous responses with any number of time points, treatment groups and categorical covariates.

Assessing and Quantifying Clusteredness: The OPTICS Cordillera

ABSTRACT This article provides a framework for assessing and quantifying “clusteredness” of a data representation. Clusteredness is a global univariate property defined as a layout diverging from equidistance of points to the closest neighboring point set. The OPTICS algorithm encodes the global clusteredness as a pair of clusteredness-representative distances and an algorithmic ordering. We use this to construct an index for quantification of clusteredness, coined the OPTICS Cordillera, as the norm of subsequent differences over the pair. We provide lower and upper bounds and a normalization for the index. We show the index captures important aspects of clusteredness such as cluster compactness, cluster separation, and number of clusters simultaneously. The index can be used as a goodness-of-clusteredness statistic, as a function over a grid or to compare different representations. For illustration, we apply our suggestion to dimensionality reduced 2D representations of Californian counties with respect to 48 climate change related variables. Online supplementary material is available (including an R package, the data and additional mathematical details).

Measuring the Stability of Results from Supervised Statistical Learning

ABSTRACT Stability is a major requirement to draw reliable conclusions when interpreting results from supervised statistical learning. In this article, we present a general framework for assessing and comparing the stability of results, which can be used in real-world statistical learning applications as well as in simulation and benchmark studies. We use the framework to show that stability is a property of both the algorithm and the data-generating process. In particular, we demonstrate that unstable algorithms (such as recursive partitioning) can produce stable results when the functional form of the relationship between the predictors and the response matches the algorithm. Typical uses of the framework in practical data analysis would be to compare the stability of results generated by different candidate algorithms for a dataset at hand or to assess the stability of algorithms in a benchmark study. Code to perform the stability analyses is provided in the form of an R package. Supplementary material for this article is available online.