Configural frequency analysis

Configural frequency analysis (Lienert, 1969) is a method of explorative data analysis. The goal of a configural frequency analysis (short CFA) is to detect patterns in the data that occur significantly more (such patterns are called Types) or significantly less often (such patterns are called Antitypes) than expected by chance. Thus, the idea of a CFA is to provide by the identified types and antitypes some insight into the structure of the data. Types are interpreted as concepts which are constituted by a pattern of variable values. Antitypes are interpreted as patterns of variable values that do in general not occur together.

Basic idea of the CFA algorithm

We explain the basic idea of CFA by a simple example. Assume that we have a data set that describes for each of n patients if they show certain symptoms s₁, ..., s_m. We assume for simplicity that a symptom is shown or not, i.e. we have a dichotomous data set.
Each record in the data set is thus an m-tuple (x₁, ..., x_m) where each x_i is either equal to 0 (patient does not show symptom i) or 1 (patient does show symptom i). Each such m-tuple is called a configuration. Let C be the set of all possible configurations, i.e. the set of all possible m-tuples on {0,1}^m. The data set can thus be described by listing the observed frequencies f(c) of all possible configurations in C.
The basic idea of CFA is now to estimate the frequency of each configuration under the assumption that the m symptoms are independent from the data. Let e(c) be this estimated frequency under the assumption of independence. Now f(c) and e(c) can be compared by a statistical test. If this test suggests for a given Alpha-Level that the difference between f(c) and e(c) is significant then c is called a Type if f(c) > e(c) and is called an anti-type if f(c) < e(c). If there is no significant difference between f(c) and e(c), then c is neither a type nor an antitype. Thus, each c can have in principle three different states. It can be a type, an antitype, or not classified.
Types and antitypes are defined symmetrically. But in practical applications researchers are mainly interested to detect types. For example, clinical studies are typically interested to detect symptom combinations that are indicators for a disease. These are by definition symptom combinations which occur more often as expected by chance, i.e. types.
Since in CFA a significance test is applied in parallel for each configuration c there is a high risk to commit a Type I error (i.e. to detect a type or antitype when the null hypothesis is true). The currently most popular method to control this is to use the Bonferroni-adjustment for the the alpha–level (see, for example, Krauth & Lienert, 1973). There are a number of alternative methods to control the Alpha–level. One alternative (Holm, 1979) considers the number of tests already finished when the i-th test is performed. Thus, in this method the alpha–level is not constant for all tests.

Chance model

The assumption of the independence of the symptoms can be replaced by another method to calculate the expected frequencies e(c) of the configurations. Such a method is called a chance model. In most applications of CFA the assumption that all symptoms are independent is used as the chance model. A CFA using that chance model is called first-order CFA. This is the classical method of CFA that is in many publications even considered to be the only CFA method. An example of an alternative chance model is the assumption that all configurations have the same probability. A CFA using that chance model is called zero-order CFA.

References

Holm, S. (1979). A simple sequential rejective multiple test procedure. Scandinavian Journal of Statistics, 6, 65-70.
Krauth, J. (1993). Einführung in die Konfigurationsfrequenzanalyse (KFA). [Introduction to Configural Frequency Analysis (CFA).] Weinheim: Beltz, Psychologie Verlags Union.
Krauth, J. & Lienert, G.A. (1973). KFA. Die Konfigurationsfrequenzanalyse und ihre Anwendungen in Psychologie und Medizin [CFA. Configural frequency analysis and its application in psychology and medicine]. Freiburg: Alber.
Lautsch, E. & Weber, S. (1990). Konfigurationsfrequenzanalyse (KFA). Berlin: Volk und Wissen.
Lehmacher, W. (1981). A more powerful simultaneous test procedure in configural frequency analysis. Biometrical Journal, 23, 429-436.
Lienert, G.A. (1969). Die Konfigurationsfrequenzanalyse als Klassifikationsmethode in der klinischen Psychologie [Configural frequency analysis as a classification method in clinical psychology]. In Irle, M. (Ed.), Bericht über den 26. Kongress der Deutschen Gesellschaft für Psychologie in Tübingen 1968, 244-253. Göttingen: Hogrefe.
Schrepp, M. (2006). The use of configural frequency analysis for exploratory data analysis. British Journal of Mathematical and Statistical Psychology, Vol. 59/1, 59-73.
von Eye, A. (1990). Introduction to Configural Frequency Analysis: The search for types and antitypes in cross-classifications. Cambridge, UK: Cambridge University Press.
von Eye, A. (2002). The odds favour antitypes - A comparison of tests for the identification of configural types and antitypes. Methods of Psychological Research, Vol. 7, No. 3.
von Eye, A. & Rovine, M.J. (1988). A comparison of significance tests for Configural Frequency Analysis. EDP in Medicine and Biology, 19, 6-13.
von Eye, A.; Spiel, C. & Wood, P.K. (1996). Configural frequency analysis in applied psychological research. Applied Psychology: An international review, 45(4), 301-352.
von Weber, S. (2000). A comparison of tests used in CFA by simulation. Psychologische Beiträge, Bd. 42, 3.