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Factor Analysis of Crime Data

Md.Siraj- Ud- Doulah, Charls Darwin, Ummi Salma, Md.Abdul Hamid, Mosfaka Aktar

Abstract


Crime analysis is an act implementation function that includes organized investigation for increasing clarifications to crime problems, and articulating crime stoppage outlines. The main part of crime analysis process is the quantitative social science data analysis methods, though qualitative methods for instance inspecting police report tales also performance a character. This paper analyses US crime data by applying multivariate factor analysis techniques that was engaged to describe the association between the crimes and to define the classification of the crimes variables. The outcome has exposed a strong positive association among EX0, EX1 and W variables; strong negative association between X and W variables as well as others four variables, ED with W revealed moderate positive correlation as well as ED with X demonstrated moderate negative correlation. The experimental results of various factors analysis methods to form three groups, group one consist of EX0, EX1, W, R and ED variables, group two consist of U1, U2, M, N and LF variables, group three consist of Age, NW, S and X variables are depicted various table and graphs. The factor analyses have recommended recollecting 4 elements that describe approximately 84.40 % of the entire unpredictability of crime data.

Keywords


Factor Analysis, KMO test, Bartlett’s test, Factor plot, Clustering Diagram, Omega Analysis, Factor Map.

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References


Shehu U., et al. (2012). Analysis of Crime data using Principal Component Analysis. Journal of Applied Statistics. 3(2), 39–49.

Fabrigar L., Wegener D., MacCallum R., Strahan E. (1999). Evaluating the use of exploratory factor analysis in psychological research. Psychological Methods. 4 272–299.

McDonald, R. P. (1985). Factor analysis and related methods. Hillsdale, NJ: Lawrence Erlbaum Associates.

Cerny C.A., Kaiser H.F. (1977). A study of a measure of sampling adequacy for factor-analytic correlation matrices. Multivariate Behavioral Research, 12(1), 43–47.

Hair J.F. (1998). Multivariate data analysis. New Jersey: Prentice Hall.

Thompson B. (2004). Exploratory and confirmatory factor analysis: understanding concepts and applications. Washington, DC.

Venables W.N., Ripley B.D. (2002). Modern Applied Statistics with S. Springer, New York.

Velicer W. (1976). Determining the number of components from the matrix of partial correlations. Psychometrika, 41(3), 321–327.

Rencher A.C. (2002). Methods of Multivariate Analysis. 2nd edn, John Wiley & Son, New York.

Snook S.C., Gorsuch R.L. (1989). Component analysis versus common factor analysis. Psychological Bulletin, 115, 148–154.

Widaman K.F. (1993). Common factor analysis versus principal component analysis: Differential bias in representing

model parameters? Multivariate Behavioral Research. 28, 263–311.

Peres-Neto P.R., Jackson D.A. (2005). Somers KM. How many principal components? Stopping rules for determining the number of non-trivial axes revisited. Comput Stat Data Anal, 49, 974–997.

Olufolabo O.O., et al. (2015). Analyzing the Distribution of Crimes in Oyo State Using Principal Component Analysis. IOSR Journal of Mathematics. 11(3), 90–96.

Edward C. (1983). Factor analysis: an applied approach. Hillsdale, N.J.: Erlbaum.

Cattell R.B. (1966). The Scree test for the number of factors. Multivariate Behavioral Research, 1, 245–276.

Grice J. (2001). Computing and evaluating factor scores. Psychological Methods, 6, 430–450.

Gujarati D.N., Sangeetha (2010). Basic Econometrics, 5th ed. McGraw-Hill, New York, 394–395.

Doulah M.S.U., Islam M.N., (2019). Defining Homogenous Climate zones of Bangladesh using Cluster Analysis, International Journal of Statistics and Mathematics. 6(1): 119–129.


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