Tuesday, 6 September 2011

Partial Summary of Uses of SPSS in B.A


SPSS is very similar to Microsoft Excel in layout. There is a menu and a tool bar option at the top of every window and some of its functions are just like an Excel. It is very easy to use and best studied by doing. SPSS interface uses two windows: one is Program Editor and second is Viewer. A Program Editor is where the data files are seen and manipulated. A Viewer is where an output of the statistical analyses is seen and manipulated.
Data Files used in SPSS -
There are two types of basic files. First is the data file (.sav). This is the place where all data for your analysis exists. While you open up the data file, it emerges in the Program Editor window. Format is same like a spreadsheet with grid of rows and columns. Columns symbolize variables and rows symbolize observations. You can also place the cursor on column heading to acquire a lengthier description of every variable. To get total information on variable, go to Utilities menu and then click on variables. The data can be entered physically or imported from the database, spreadsheet, or else text file.
Output files

Second type of file is the output file (.spo) and when a statistical process is run, output is created. Viewer window automatically will open to display the production. The left side pane will have an outline view of an output. The right side pane will have the contents of an output that include charts, tables, and text. There are some book icons in outline view subsequent to various objects of output.
Statistical Analysis with SPSS –
Factor Analysis: Factor analysis attempts to identify underlying variables, or factors, that explain the pattern of correlations within a set of observed variables. Factor analysis is often used in data reduction to identify a small number of factors that explain most of the variance observed in a much larger number of manifest variables. Factor
analysis can also be used to generate hypotheses regarding causal mechanisms or to screen variables for subsequent analysis.
Tables such as Total Variance Explained, Rotated Component Matrix and graphs like the Scree Plot helps better draw conclusions under Factor Analysis.
Crosstabs: Crosstabs is an SPSS procedure that cross-tabulates two variables, thus displaying their relationship in tabular form. In contrast to Frequencies, which summarizes information about one variable, Crosstabs generates information about bivariate relationships.
Crosstabs creates a table that contains a cell for every combination of categories in the two variables.
  • Inside each cell is the number of cases that fit that particular combination of responses.
  • SPSS can also report the row, column, and total percentages for each cell of the table.
Because Crosstabs creates a row for each value in one variable and a column for each value in the other, the procedure is not suitable for continuous variables that assume many values. Crosstabs is designed for discrete variables--usually those measured on nominal or ordinal scales.
Frequencies: The SPSS procedure FREQUENCIES reports a table of frequency counts (number of cases with each unique value of a variable) and percentages for the selected variables. It will also produce bar charts and histograms. Like the DESCRIPTIVES procedure, the FREQUENCIES procedure also computes univariate statistics
Cluster Analysis: SPSS offers two separate approaches to cluster analysis, K-Means clustering (also called Quick clustering) and Hierarchical (or agglomerative) clustering.
·         K-Means cluster analysis - K-means clustering was originally designed as a method that allowed very large data sets to be clustered in a feasible amount of time, when computers were rather slower than they are today. This explains its other name of "quick clustering". It requires the number of clusters to be specified in advance, and the initial number chosen may split natural groupings or combine two or more groups that are rather different from each other. When used with ecological data, it has the advantage of producing nice discrete groups that are usually easy to interpret.
·         Hierarchical Cluster Analysis - There are a huge range of hierarchical cluster analysis methods available, which give different results depending upon which you choose. The two basic choices that need making are how you assess the similarity between samples, and how you combine the samples into clusters. As with PCA, you have a choice on whether to standardise the data to give all species equal weights. The analysis outlined here uses a distance method that measures similarity between samples in a way that is consistent with the way that PCA treats distances. It also uses the most pessimistic clustering method, which will only identify nice clean clusters if these really exist in the data.

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