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.

Discriminant Function Analysis in the field of Supply Chain

Discriminant function analysis is used to determine which variables discriminate between two or more naturally occurring groups. For example, an educational researcher may want to investigate which variables discriminate between high school graduates who decide - to go to college, to attend a trade or professional school or to seek no further training or education For that purpose the researcher could collect data on numerous variables prior to students' graduation. After graduation, most students will naturally fall into one of the three categories. Discriminant Analysis could then be used to determine which variable(s) are the best predictors of students' subsequent educational choice. Suppose we have two groups of high school graduates: Those who choose to attend college after graduation and those who do not. We could have measured students' stated intention to continue on to college one year prior to graduation. If the means for the two groups (those who actually went to college and those who did not) are different, then we can say that intention to attend college as stated one year prior to graduation allows us to discriminate between those who are and are not college bound (and this information may be used by career counselors to provide the appropriate guidance to the respective students). Therefore, the basic idea underlying discriminant function analysis is to determine whether groups differ with regard to the mean of a variable, and then to use that variable to predict group membership

Such an analysis could also be used in the field of Supply Chain management. Supposing a company wants to determine the real impact of implementing Radio Frequency Indentification (RFID) in its business, it would have to investigate the various determinants on the adoption of such a technology. For example, the determinants can be taken as technological, organizational, environmental factors and product factors. The methodology adopted for a discriminant function would be to investigate the influential factors (independent variables) that may contribute to the RFID adoption (dependent variable). Discriminant analysis would be to determine whether statistically significant differences exist between the average score profile on a set of variables for prior defined groups and thereby enable those variables to be classified. It would also help to determine which of the independent variables account the most for the differences in the average score profiles of the two groups .  

If we code the two groups in the analysis as 1 and 2, and use that variable as the dependent variable in a multiple regression analysis, then we would get results that are analogous to those we would obtain via Discriminant Analysis. In general, in the two-group case we fit a linear equation of the type:

Y = a + b₁*x₁ + b₂*x₂ + ... + b_m*x_m

where a is a constant and b₁ through b_m are regression coefficients. The interpretation of the results of a two-group problem is straightforward and closely follows the logic of multiple regression: Those variables with the largest (standardized) regression coefficients are the ones that contribute most to the prediction of group membership.