Finding Structures of Interest in a Large Data Set Using Factor Analysis
نویسنده
چکیده
Abstract: In this paper we introduce a statistical method which can be used in combination with principal component analysis or factor analysis. Certain variables of a large data set which are of interest can be selected in order to calculate loadings and scores of these variables. We describe how the remaining variables of the data set can be presented in the previously extracted factor space. Furthermore, a possibility for the representation of the results is shown which is helpful for the interpretation.
منابع مشابه
Who Should be Interviewed? A Response from Cluster Analysis
Objective: This article presents an application of cluster analysis for social sciences researches especially those studies that have an interview as part of their data collection. This application is more suitable for sequential mixed method researchers who use quantitative data to frame subsequent qualitative subsamples for conducting interviews. Methods: In more detail, the algorithm (i....
متن کاملA nonlinear model for common weights set identification in network Data Envelopment Analysis
In the Data Envelopment Analysis (DEA) the efficiency of the units can be obtained by identifying the degree of the importance of the criteria (inputs-outputs).In DEA basic models, challenges are zero and unequal weights of the criteria when decision- making units are evaluated. One of the strategies applied to deal with these problems is using common weights of the each input...
متن کاملFinding a Common Set of Weights by the Fuzzy Entropy Compared with Data Envelopment Analysis - A Case Study
A data envelopment analysis (DEA) method can be regarded as a useful management tool to evaluate decision making units (DMUs) using multiple inputs and outputs. In some cases, we face with imprecise inputs and outputs, such as fuzzy or interval data, so the efficiency of DMUs will not be exact. Most researchers have been interested in getting efficiency and ranking DMUs recently. Models of th...
متن کاملSpatial Design for Knot Selection in Knot-Based Low-Rank Models
Analysis of large geostatistical data sets, usually, entail the expensive matrix computations. This problem creates challenges in implementing statistical inferences of traditional Bayesian models. In addition,researchers often face with multiple spatial data sets with complex spatial dependence structures that their analysis is difficult. This is a problem for MCMC sampling algorith...
متن کاملCorrelation Pattern between Temperatur, Humidity and Precipitaion by using Functional Canonical Correlation
Understanding dependence structure and relationship between two sets of variables is of main interest in statistics. When encountering two large sets of variables, a researcher can express the relationship between the two sets by extracting only finite linear combinations of the original variables that produce the largest correlations with the second set of variables. When data are con...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2001