## Statistics

Statistics

SPSS

Project description
1. What were the mean, mode, median, range, and standard deviation computed to be for the Santa Fe SPSS data set? Interpret those numbers within the context of the Santa Fe questionnaire used in the research. What does a mean of 3 tell us about the frequency overall at Santa Fe Grill? What does a standard deviation of 1.1 tell us regarding the Santa Fe research? What does a variance of 1.25 reveal to us in terms of the Santa Fe research? You must explain what those numbers tell the managers. In other words, what are the research findings or conclusions.
2. Compare the means of three or more groups. Are households with more children more likely OR less likely to recommend their favorite Mexican restaurant than are households with fewer children? Explain fully your conclusion. Don’t guess. Support your answer by providing the mean that was computed.
3. Does the Pearson Correlation reveal that there is a high or low correlation between the level of satisfaction and the likelihood to recommend? What was the Pearson Correlation computed to be .4, .6, .8, or 1.0? Don’t guess. Explain fully.
4. Recall we talked about predictive modeling in class during the first two weeks. This was also discussed in the HBAT example. For Santa Fe Grill, what does the multiple regression reveal about the ability of fresh food and friendly service to predict customer satisfaction? Don’t guess. What are the beta coefficients for each? Explain fully.

One-Way Tabulation – Frequency Table
The SPSS “click-through” sequence is
ANALYZE  DESCRIPTIVE STATISTICS  FREQUENCIES.
Let’s use X25 – Frequency of Patronage of Santa Fe Grill as a variable to examine.    On the left side of the Frequencies box on the screen is a list of the Santa Fe variables.  Scroll down and click on X25 to highlight it, and then on the arrow box to move X25 into the Variables box.  Next click on OK to get the results.

Mean, Median and Mode – Measures of Central Tendency
The SPSS “click-through” sequence is
ANALYZE  DESCRIPTIVE STATISTICS  FREQUENCIES.
Let’s use X25 – Frequency of Patronage of Santa Fe Grill as a variable to examine.  On the left side of the Frequencies box on the screen is a list of the Santa Fe variables.  Scroll down and click on X25 to highlight it, and then on the arrow box to move X25 into the Variables box.    Next click on the Statistics box and then click on Mean, Median, and Mode.    Now click Continue and OK to get the results.
If you want to do a chart, then look at the bottom of the Frequencies box next to the Statistics box and click on Charts.   If you select bar charts, and continue, and then OK you will get a chart too.

Range, Standard Deviation and Variance
The Santa Fe Grill database can be used with the SPSS software to calculate measures of dispersion, just as we did with the measures of central tendency. The SPSS click-through sequence is
ANALYZE  DESCRIPTIVE STATISTICS  FREQUENCIES.
Let’s use X22 – Satisfaction as a variable to examine.  Click on X22 to highlight it and then on the arrow box to move X22 to the Variables box.  Next open the Statistics box, go to the Dispersion box in the lower-left-hand corner, and click on Standard deviation, Variance, Range, Minimum and Maximum.  Now click Continue and OK to get the results.
If you want to do a chart, then click on Charts to get the Frequencies: Charts dialog box.   If you select bar charts, and continue, and then OK you will get a chart too.

Sample Subgroups
To split the sample into groups, the click-through sequence is:  Data  Split File.   First click on the Data pull-down menu and scroll down and highlight and click on Split File.  You will now see in the Split File dialog box where the default is Analyze all cases.  Click on the Compare groups option, highlight the variable you want to split the groups with (e.g., screening variable X_s4 – Favorite Mexican Restaurant), and click on the arrow box to move it into the Groups Based on:  window.     Next click on “OK” and you will be analyzing the Santa Fe Grill customers and Jose’s customers separately.   That is, your output will have the results for the two competitors separately.

Summated Scores
The restaurant perceptions variables include three measures related to satisfaction.  They are variables X22, X23 and X24.  To calculate the summated score, the click-through sequence is:  TRANSFORM  COMPUTE.   First type a variable name in the Target Variable box.  In this case we are calculating a summated score for the satisfaction variables so let’s use the abbreviation Sum_Sat for Summated Satisfaction.  Next click on the Numeric Expression box to move the cursor there.   Look below at the buttons and click on the parenthesis to place it in the Numeric Expression box (make sure cursor is between parentheses).    Now highlight variable X22 and click on the arrow box to move it into the parenthesis.   Go to the buttons below and click on the plus (+) sign.  Go back and highlight variable X23 and click on the arrow box to move it into the parenthesis.   Again click on the plus (+) sign.  Finally, go back and highlight variable X24 and click on the arrow box to move it into the parenthesis.  Now put the cursor at the right end of the parentheses and click on the divide sign (/) and then 3 to get the average.   Next click on “OK” and you will get the average summated score for the three variables.  You can find the new variable at the far right hand side of your data editor screen.

Crosstabulation

The SPSS click-through sequence is ANALYZE  DESCRIPTIVE STATISTICS  Crosstabs.    Use X31 – Ad Recall and X32 – Gender as variables to examine.  Click on X31 to highlight it and then on the arrow button to move X31 to the Row(s): box.  Now click on X32 to highlight it and then on the arrow button to move X32 to the Column(s):  box.    Now click OK to get the results.

Crosstabulation – Testing for Differences with Chi-Square
First, enter variables X31 and X32 into the Row and Column boxes as described above (X31 in Row box and X32 in Column box).   Next open the Statistics button in the top-right-hand corner, and click on Chi-square (upper left corner), and then Continue.  Next go to the Cells button (top right) and click on it to get the Crosstabs:  Cell Display box.  In upper left corner click on Expected (Observed already has a check), and just below in the Percentages box click on Column.    Now click Continue and then OK to get the results.

Compare Means of Two Groups
The click-through sequence is ANALYZE   COMPARE MEANS   One-Way ANOVA.   Highlight the dependent variable X24 – Likely to Recommend by clicking on it and move it into the Dependent List window.  Next, highlight X32 – Gender and move it into the Factor window.   Now click on the Options box and then click Descriptives and Continue.  Then click OK to get the results.
The results show you that males are?

Compare Means of Three of More Groups
The click-through sequence is ANALYZE   COMPARE MEANS   One-Way ANOVA.   Highlight the dependent variable X24 – Likely to Recommend by clicking on it and move it to the Dependent List window. Next, highlight X33 – Number of Children at Home and move it to the Factor window.   Now click on the Options button and then click Descriptives.   Next click on the Post hoc button and check Scheffe and then Continue.   Then click OK to get the results.

Pearson Correlation
The SPSS click-through sequence is ANALYZE  CORRELATE  BIVARIATE, which leads to a dialog box where you select the variables.  Transfer variables X22 and X24 into the Variables window. Note that we will use all three default options shown below: Pearson correlation, two-tailed test of significance, and flag significant correlations.  Next go to the Options box, and after it opens click on Means and Standard Deviations and then continue.  Finally, when you click on OK (bottom left of dialog box) it will execute the Pearson correlation.

Multiple Regression
The SPSS click-through sequence to examine this relationship is ANALYZE  REGRESSION  LINEAR.    Highlight X22 and move it to the Dependent Variables box.  Highlight X12, X15, X16 and X17 and move them to the Independent Variables box.  We will use the defaults for the other options so click OK to run the multiple regression.

Exhibit 1    SPSS Data Editor Window with No Data

Across the top of the screen is a toolbar with a series of pull down menus.  Each of these menus leads you to several functions.  An overview of these menu functions is shown below.
There are 11 “pull-down” menus across the top of the screen.  You can access most SPSS functions and commands by making selections from the menus on the main menu bar.  Below are the major features accessed from each of the menus on the Student Version of the SPSS software.
File  =  create new SPSS files; open existing files; save a file; print; and exit.
Edit  =  cut and/or copy text or graphics; find specific data; change default options such
as size or type of font, fill patterns for charts, types of tables, display format for
numerical variables, and so forth.
View  =  modify what and how information is displayed in the window.
Data  =  make changes to SPSS data files; add variables and/or cases; change the
order of the respondents; split your data file for analysis; and select specific
respondents for analysis by themselves.
Transform  =  compute changes or combinations of data variables; create new
variables from combinations of other variables;  create random seed
numbers; count occurrences of values within cases;  recode existing
variables; create categories for existing variables; replace missing
variables; and so on.
Analyze  =  prepare reports; execute selected statistical techniques such as
frequencies, correlation and regression, factor, cluster, and so on.
Graphs  =  prepare graphs and charts of data, such as bar, line and pie charts; also
boxplots, scatter diagrams and histograms.
Utilities  =  information about variables such as missing values, column width,
measurement level and so on.
Add-ons  =  other functions such as Missing Values Analysis and AMOS.
Window  =  minimize windows or move between windows.
Help  =  a brief tutorial of how to use SPSS; includes a link to the SPSS home page
at www.spss.com.
ENTERING DATA
There are two ways you can enter data into SPSS files.  One is to enter data directly into the Data Editor window.  This can be done by creating an entirely new file or by bringing data in from another software package such as Excel.  The other is to load data from a file that has been created in another SPSS application.
Let’s begin with explaining how to enter data directly into the Data Editor window.
The process is similar to entering data into a spreadsheet.  The first column typically is used to enter a respondent ID.  Use this to enter a respondent number for each response. The remaining columns are used to enter data.  You can also ‘cut and paste’ data from another application.  Simply open the Data Editor window and minimize it.  Then go to your other application and copy the file, return to the Data Editor window and paste the data in it, making sure you correctly align the columns for each of the variables.
DATA VIEW
When you load up your SPSS file it will show the Data View screen.  Exhibit 2 shows the Data View screen for the Santa Fe Grill survey.  This screen is used to run data analysis and to build data files.   The other view of the Data Editor is Variable View.  The Variable View shows you information about the variables.  To move between the two views go to the bottom left-hand corner of the screen and click on the view you want.   We discuss the Variable View screen in the next section.

Exhibit 2   Data View of the Santa Fe Grill Database

The survey database is set up in columns.  The first column on the far left labeled “id” is a unique number for each of the 405 respondents in your database.  The remaining columns are the data from the customer interviews.  In the first 4 columns to the right of the id you have the values for the four screening questions.  Then, you have the first three variables of the survey – the lifestyle variables (X1 – X3).  For example, respondent 1 gave a “7” on the 7-point scale for the first variable (X1).  Similarly, that same respondent rated the restaurant a “4” on the second variable (X2) and a “5” on the third one (X3).  Exhibit 2 only shows the id, the four screening variables and the first three variables of the survey.  But on your SPSS screen if you scroll to the right you will see the data for all of the survey variables.

VARIABLE VIEW
Exhibit 3 shows the Variable View screen for the SPSS software.  In this view the variable names appear in the far left-hand column.  Then each of the columns defines various attributes of the variables a described below:
Name  =  This is an abbreviated name for each variable.
Type  =  The default for this is numeric with 2 decimal places.  This can be changed to express values as whole numbers or it can do other things such as specify the values as dates, dollar, custom currency and so forth.  To view the options click first on the Numeric cell and then on the three shaded dots to the right of the cell.
Label  =  In this column you give a more descriptive title to your variable.  For example, with the restaurant survey variable X1 is labeled as X1 – Try New and Different Things and variable X2 is labeled as X2 – Party Person.   When you have longer labels and want to be able to see all of them you can go to the top of the file and click between the Label and Values cells and make the column wider.
Values  =  In the values column you can assign a label for each of the values of a variable.  For example, with the restaurant survey data variable X1 –Try New and Different Things we have indicated that a 1 = Strongly Disagree and a 7 = Strongly Agree. To view the options click first on the Values cell and then on the three shaded dots to the right of the cell.  You can add new labels or change existing ones.
Missing  =  Missing values are important in SPSS.  If you do not handle them properly in your database it will cause you to get incorrect results.  Use this column to indicate values that are assigned to missing data.  A blank numeric cell is designated as system-missing and a period (.) is placed in the cell.  The default is no missing data but if you have missing data then you should use this column to tell the SPSS software what is missing.  To do so, you can record one or more values that will be considered as missing data and will not be included in the data analysis.  To use this option, click on the Missing cell and then on the three shaded dots to the right.    You will get a dialog box that shows the default of no missing data.  To indicate one or more values as missing click on Discrete missing values and place a value in one of the cells. You can record up to three separate values.  The value most often used for missing data is a ‘9’.  If you want to specify a range of values click on this option and indicate the range to be considered as missing.
Column  =  Click on the column cell to indicate the width of the column.  The default is 8 spaces but it can be increased or decreased.
Align  =  The default for alignment is initially left, but you can change to either center or right alignment.
Let’s look at the Variable View screen for the Santa Fe Grill database.  It is shown in Exhibit 3.
To see the Variable View screen go to the bottom left-hand corner of the screen and click on “Variable View.”  The name of the variable will be in the first column, but if you look at the fifth column it will tell you more about the variable.  For example, variable X1 is “Try New and Different Things” while X2 is “Party Person.”  All of the remaining variables have a similar description.  Also, if you look under the Values column it will tell you how the variable is coded; e.g., 1 = Strongly Disagree and 7 = Strongly Agree.

Exhibit 3   Variable View of the Santa Fe Grill Survey Data

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