Configural Frequency Analysis: Methods, Models, and Applications
Citation Tools
Tips on citation download. A pattern-oriented approach to studying individual development: A personoriented approach in research on developmental psychopathology.
Development and Psychopathology, 9, - Pattern discovery and detection: A unified statistical methodology. Journal of Applied Statistics, 31 8 , - The individual as the organizing principle in psychological inquiry: Introduction to Configural Frequency Analysis: The search for types and antitypes in cross-classification. Google Scholar , Crossref.
Version program for 32 bit operating systems. Methods of Psychological Research: Online, 6, - Antitypes are interpreted as patterns of variable values that do in general not occur together.
- What is Kobo Super Points?;
- Configural frequency analysis - Wikipedia.
- CMA Part 1 - Financial Reporting, Planning, Performance, and Control Exam Secrets Study Guide: CMA T.
- My Weird School #10: Mr. Docker Is off His Rocker! (My Weird School series).
- Supplemental Content.
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 s 1 , We assume for simplicity that a symptom is shown or not, i.
Principles of Practical Psychology: Applied and or person-oriented researchers, statisticians, and advanced students interested in CFA and categorical and longitudinal data will find this book to be a valuable resource. Science, , - Applied Longitudinal Data Analysis. This page was last edited on 13 November , at Now f c and e c can be compared by a statistical test typical tests applied in CFA are Pearson's chi-squared test , the binomial test or the hypergeometric test of Lehmacher. Would you like us to take another look at this review?
Each record in the data set is thus an m -tuple x 1 , Each such m -tuple is called a configuration. Let C be the set of all possible configurations, i.
Reward Yourself
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 to estimate the frequency of each configuration under the assumption that the m symptoms are statistically independent.
Let e c be this estimated frequency under the assumption of independence. Let p i 1 be the probability that a member of the investigated population shows symptom s i and p i 0 be the probability that a member of the investigated population does not show symptom s i. Now f c and e c can be compared by a statistical test typical tests applied in CFA are Pearson's chi-squared test , the binomial test or the hypergeometric test of Lehmacher.
If there is no significant difference between f c and e c , then c is neither a type nor an antitype.
Configural frequency analysis
Thus, each configuration 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.
- What is Kobo Super Points?.
- Book Review: Configural Frequency Analysis: Methods, Models, and Applications.
- Idas und Mycon (German Edition)!
- Book Review: Configural Frequency Analysis: Methods, Models, and Applications - Eun Young Mun, .
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 than expected by chance, i. 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.