Day 2: Team A

Day 2

After an introduction to SPSS in the first class today it was time to brush up our skills and get to know more features of the software.

We studied concepts of Scale, Ordinal value Nominal value and Likert scale. 

In SPSS you can specify the level of measurement as scale (numeric data on an interval or ratio scale), ordinal, or nominal. Nominal and ordinal data can be either string alphanumeric or numeric.

Nominal:
A variable can be treated as nominal when its values represent categories with no intrinsic ranking; for example, the department of the company in which an employee works. Examples of nominal variables include region, zip code, or religious affiliation.

Ordinal:
A variable can be treated as ordinal when its values represent categories with some intrinsic ranking; for example, levels of service satisfaction from highly dissatisfied to highly satisfy. Examples of ordinal variables include attitude scores representing degree of satisfaction or confidence and preference rating scores.

For ordinal string variables, the alphabetic order of string values is assumed to reflect the true order of the categories. For example, for a string variable with the values of low, medium, high, the order of the categories is interpreted as high, low, medium which is not the correct order. In general, it is more reliable to use numeric codes to represent ordinal data.

Scale:
A variable can be treated as scale when its values represent ordered categories with a meaningful metric, so that distance comparisons between values are appropriate. Examples of scale variables include age in years and income in thousands of dollars.

(Source: SPSS User Guide)

CROSSTABS: It is an SPSS procedure that CROSS-TABULATES two variables, thus displaying their relationship in tabular form. While FREQUENCIES is a useful procedures for summarizing information about one variable, CROSSTABS generates information about bivariate relationships.

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.

CROSSTABS creates a table that contains a cell for every combination of the 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.

Procedure included:

Crosstabs can be found within the Descriptive Menu of SPSS. Click Analyse -- Descriptive Statistics -- Crosstabs.

Select variable for your column. Click the purple arrow to move the variable to the Column(s) box.

Select a variable for your row(s) by clicking on it. Click the purple arrow to move the selected variable to the Row(s) box.

Click the Statistics box.

Consult the SPSS manual when choosing inferential statistics for your crosstabs. In this example, the sample size is small and non-random. Therefore, we will simply run cross-tabs or in our case we choose chi square. Click Continue.

Click Cells. Here one can choose row, columns for percentages as per requirement

Click Continue.

In today’s class with the help of retail case we studied cross tabs for Control Variable and Select cases. SPSS allows us to select part of the data set for further analysis, while excluding the remaining cases from these analyses. The procedure is found by choosing Select from the Data Menu.

We analysed different conditions to get a more realistic approach. First we studied cross tab with a third variable. Like for example instead of direct relation between two variables there could be a third dependent variable too which would give us a more clarified analysis. In the case of ants and tourist there was no direct relation but it was actually dependant on weather conditions. So we used the same principle and analysed the cases in cross tab for three variables and chi square

We applied the same principle for the retail case.  We tried to analyse the case that people entered the store because they did not have a good experience in other stores.

By:

Ria Sarwal