Approaches, Methods and Applications in Europe. Guidelines on using SPSS

Transcription

1 Marketing Research Approaches, Methods and Applications in Europe Introduction Guidelines on using SPSS The background to SPSS is explained on p.293 in the text. Your institution will almost certainly have a site license for SPSS. This means that it should be available on your network. You should also be able to download the student version from your library or information services division onto you own laptop. You can also download a demo version from the SPSS website, but it is a strictly 14-day trial version. For more information visit: SPSS has now just brought out SPSS 15.0 for Windows. This extends the Chart Builder that was added in SPSS For example, you can now overlay charts. The illustrations for the text itself were produced on SPSS 13.0, which was the latest version at the time. As far as you are concerned, it does not make a great deal of difference which version you use. The material below is arranged as three SPSS sessions, each of which should be doable in under an hour. The first focuses on entering data and performing some basic operations on the data. The second is based on accessing the table tennis data, which features in chapters The third uses a different dataset and looks at some more advanced operations. There is some overlap in the SPSS features covered. This is deliberate since I find that students usually appreciate a recap. If you are using your institution s network, your tutor will need to explain to you how to access the SPSS program. The data that you will need for the second session can be accessed from the Thomson website,

2 Session 1 Entering data and doing some basic analyses Entering data The first window you will see (Figure 1) is the Data Editor window - this is for entering new data. The window is a grid for creating a data matrix whose rows represent survey respondents (called cases in SPSS) and whose columns will contain the values of the variables for each case. Figure 1 Data Editor Window Before entering any data it is advisable first to define all the variables. In the Data Editor window click on the Variable View tab on the bottom left hand side of the screen. This will give you the Variable View screen (Figure 2) Figure 2 Variable View

3 Table 1 suggests six variables relating to six questions addressed to a sample of respondents. Each variable is given a short name and a more extended description may be given as a variable label. Where variables are sets of categories, each category may be given a value label and a code. The first variable is named Gender, so type in Gender under Name in the first row of the first column. These names must not exceed eight characters (unless you change the default of 8 in the Columns column), they must begin with a letter, they must not end with a full stop and there must be no spaces. Some words like and, not, by and all are reserved by SPSS and cannot be used. Table 1 Variable name Variable label Value labels Code Gender Sex of respondent Male Female Age Age in years Ban Attitude to banning advertising Strongly agree Agree Neutral Disagree Strongly disagree Sport Participation in sport Participates Does not participate Health Attitude to health Avoids fat Does not avoid fat Smoke Smoking behaviour Smokes Does not smoke As soon as you hit Enter or down arrow or right arrow, the remaining boxes will be filled with default settings, except for Label. You can leave these settings as they are, but it is better to enter variable labels in the Label column, so type in Sex of respondent in the Label column in the first row. The restrictions on variable names do not apply to labels, so you can make them longer, use spaces and so on. If the variable is categorical (binary, nominal or ordinal), it is a good idea to enter value labels. Click in the cell under Values against the variable Gender in the first row and click again on the little grey box to the right of the cell. This will produce the Value Labels dialog box. See Figure Figure3 Completed Variable View

4 In the Value box, type in 1 (the lowest code number) and in Value Label type in Male. N.B. To move between boxes click on the mouse in the appropriate box (or use the Tab key to move to the box below). Click on the Add button and 1= Male will appear in the lowest box. Now add 2=Female using the same procedure and then click on OK. Now name the other five variables, adding variable labels and value labels. Note, however, that age is a metric variable and does not require value labels. The default under Decimals is usually 2. If all the variables are integers, then it is worthwhile changing this to zero. Simply click on the cell and use the little down arrow to reduce to zero. Under Measure, you can put in the correct level of measurement nominal, ordinal or scale (i.e. interval/ratio/metric). The completed Variable View is shown in Figure 3. You are now ready to enter the data. Switch to the Data View and enter the data from Figure 4. This will normally be done row by row, i.e. questionnaire by questionnaire for survey analysis. Put the cell highlight on the cell into which you wish to enter a value (begin top left) and simply type the number (always enter the codes, not the value labels!). Move the highlight using the direction keys. Notice that the process of entering is completed simply by moving the highlight to another cell. You could press the Enter key instead. Figure 4 The Frequencies dialog box Notice that just above the grid is a white bar - this is the Data Editor. This shows your entry as you type it and indicates the cell position on the left. The usual Windows editing functions are available, for example you can cut, copy and paste in the usual way. To change a value in a cell once it

5 has been entered, simply highlight the cell, type in the new value and press Enter. Note that SPSS assumes that all data matrices are rectangular. If you press Enter before you get to the end of the second or subsequent rows a full stop is entered in each of the remaining cells in that row. There can be no empty cells. If no value has been entered, the system supplies the system-missing value, which is indicated by a full stop. Hint If all the values to be entered are integers (whole numbers), it is better to change the default, which specifies the number of decimal places shown - this is normally set at two decimal places. However, you must do this before you enter any data and before you put in variable labels. From the menu bar at the top of the Data Editor window select Edit, then Options, then the Data tab. Change Decimal Places to zero. Click on OK. Saving your work Remember that SPSS does not have an automatic timed backup facility. You need to save your work regularly as you go along. Use the File\Save sequence as usual for Windows applications. The first time you go to save you will be given the Save As dialog box. Choose the folder in which you wish to save your work. File\Exit will get you out of SPSS and back to the Windows desktop. Data analysis SPSS for Windows has some very powerful techniques at its disposal. However, for present purposes only the very basic procedures will be explained. When analysing any dataset you will need to begin by obtaining one-way descriptive summaries for each of your variables, i.e. one at a time. Which summaries are appropriate depends on the type of data. For non-metric (categorical) variables (sometimes called nominal or ordinal scales) you need the Frequencies procedure. This is in the Analyze/Descriptive Statistics drop-down menu from the menu bar at the top. So, click on Analyze, then Descriptive Statistics, then Frequencies. The Frequencies dialog box will appear (see Figure 4). All variables are listed in the left box. To obtain a frequency count for any variable simply transfer it to the Variables box by highlighting it, then clicking on the direction button in the middle. Since all the variables except age are non-metric, highlight all except age in the left box. N.B. Hold down the left mouse button while dragging the mouse and you can do them all at once! Click on OK and, hey presto, you obtain a frequency count for each variable along with Percent, Valid Percent and Cumulative Percent, as illustrated in the Table 3 below for the variable Ban. Valid percent excludes missing cases if there are any. Valid Table 3 Strongly disagree Disagree Neutral Agree Strongly agree Total Attitude to banning of advertising Cumulative Frequency Percent Valid Percent Percent

6 Where there are no missing cases the Percent and Valid Percent are the same. You can edit the table to remove these columns if you wish. Just double-click on the table. Click on Valid Percent and hit Delete. Then highlight the figures in the column and hit Delete and the column will disappear and the table will close up. You can do the same with the Cumulative Percent column. To get out of Edit mode just left-click outside the table area. With the table highlighted (there will be a frame around it and a red arrow to the left) you can select Edit and Copy and then Paste it into any other application like Word or Powerpoint. Hint. For copying into Word, it is better to use the Edit/Paste Special/Picture procedure If you click on Charts in the Frequencies dialog box you obtain the Frequencies: Charts dialog box. Simply click on Bar Chart and indicate whether you want the axis label to display frequencies or percentage, click on Continue and then OK. This will give you a basic default bar chart in addition to the frequencies table. To obtain other kinds of bar chart like stacked or clustered bar charts you will need to select the Graphs/Bar drop-down menu to give you the Bar Charts dialog box. In this box you can also choose between summaries for groups of cases, summaries of separate variables, or values of individual cases. Once you have obtained your chart you can edit it by double-clicking in the chart area. The will give you the SPSS Chart Editor. Try clicking on the Bar Style button and change the bars to a 3-D effect. You can change the colours and a number of other chart features from the editor. Close the Editor when you have finished. If you single-click on the chart area you highlight it. If you now select Edit/Copy you can copy the chart into other applications. For metric variables you can also use the Frequencies procedure, but in addition you can Click on the Statistics button in the Frequencies dialog box to obtain a range of statistics. Just click the boxes as required. Analyze/Descriptive Statistics/ Descriptives provides a quick way of obtaining a range of common descriptive statistics for metric data, both of central tendency and dispersion - try it on age. Put age into the Variables box and click on OK. The next stage in any data analysis is to look at the relationships between variables. For example, are men more or less likely than women to agree with a ban on advertising cigarettes? For this you need the Crosstabs procedure. This generates contingency tables for non-metric variables. Select Analyze/Descriptive Statistics/ Crosstabs to obtain the Crosstabs dialog box (see Figure 5). Enter your dependent variable (Ban) in the Rows box so it will appear at the side and the independent variable Sex in the columns. Have a look at what statistics are available for Crosstabs by clicking on the Statistics button. For nominal variables there is Chisquare, Phi-square, Cramer s V, the Contingency Coefficient, Lambda and an Uncertainty Coefficient. For ordinal data there is Gamma, Somers d, and Tau b and Tau c. For nominal by interval combination there is the statistic Eta. To obtain any of these just click on the appropriate box. Click on OK when you have decided what you want. In this example it s really a nominal by ordinal combination, so only the nominal data statistics can be used. For a review of these statistics see Chapter 12.

7 Figure 5 The Crosstabs dialog box For correlating two metric variables the Analyze/Correlate/Bivariate procedure should be used. N.B. Strictly-speaking there is only one metric variable in this dataset - age. However, treat Ban as a metric variable (i.e. the scores 5-1 are regarded as measures of distance) and correlate Age against Ban. Ensure that the Pearson check box is ticked. Graphically you can obtain a scatterplot with the Graphs/Scatter sequence. Where the two variables are rank ordered, e.g. two variables ranked 1-30, then Spearman s rank correlation is a very good statistic. Select Analyze/Correlate/Bivariate and click the Spearman s check box. N Data transformation After data have been entered into SPSS it may be necessary to modify them in certain ways. For non-metric data it may be necessary to combine or alter the categories of a variable. This is achieved with the Recode procedure. Recode Suppose, for the variable Ban, you wish to add together the responses Strongly Agree and Agree to make a new category, and to put Neutral, Disagree and Strongly Disagree into another category (i.e. to create a binary variable). You may need to perform this kind of operation on several variables if the sample size is small and the process of crosstabulation gives too many cells with too few entries, even empty cells. Select Transform then Recode and click on Into Different Variables. This gives another dialog box (see Figure 6). Click on Ban and on > to paste the name into the Numeric Variable->Output Variable box. Type the name of the new output variable, e.g. Ban2 into the Name box and click on Change to insert the name into the Numeric Variable->Output Variable box.

8 Click on the Old and New Values box to open the next dialog box. Since the value 1 is to remain unchanged, click on the Value radio button in the Old Value box, and enter the value 1. Click on the Copy old value(s) radio button in the New Value box, then on Add in the Old->New box. The values 2 and 3 are to be recoded as 1, so click on the Range radio button in the Old Value box. Enter 2 in the left box and 3 in the right box. Click on the Value radio button in the New Value box and enter 1 and click on Add. Codes 4 and 5 are to be recoded as 2, so return to the Old Value box, enter, under Range 4 in the left box and 5 in the right. In the New Value box enter 2 and click on Add. The Old->New box will show the transformations that are to take place. Click on Continue and then on OK. A new variable Ban2 will be added to the worksheet in the Data Editor window that contains only the values 1 and 2. Check that these transformations have been carried out. Figure 6 The Recode into Different Variable Box The Recode procedure may also be used to recode ranked and metric variables into coded categories. This will be useful if you wish to crosstabulate these with non-metric variables. Try putting Age into groups of 10 years, e.g , 30-39, and Multiple response variables Many questions in questionnaires allow respondents to pick more than one category. For example, the Sport variable could have asked respondents to tick a box against each sport they played. For the sake of the next exercise, add three more variables, foot, tennis, other, and for all 15 cases add a zero or 1 at random for each (just to create some fictional data). SPSS allows you to treat groups of such variables as multiple responses. This facility is under the Analyze drop-down menu. Select Analyze /Multiple Response and Define Sets. The Define Multiple Response Sets dialog box (see Figure 7) allows you to specify the variables to be included in this set. Simply transfer them from the Set definition box to the Variables in Set box. Transfer foot, tennis and other to the Variables in Set

9 box. Click in the Dichotomies radio button in the Variables Are Coded As box and enter the value to be counted as part of the set, e.g. if the value 1 has been entered on the questionnaire to indicate that this item has been selected, then enter 1 in this box. Don t forget to give a name to the set. Entering a label is optional. Click on Add, then Close. Several multiple response sets may be defined at the same time. These are then listed in the Multi- Response Sets box. The sets so defined may now be used either to show frequencies or they may be used in crosstabs. Reselect Analyze/ Multiple Response, then either Frequencies or Crosstabs. Figure 7 Define Multiple Response sets

10 Session 2 Analysing the table tennis data Access the tabten.sav file from the Thomsonlearning website and save it either on your laptop or on your networked account. The questionnaire on which the data are based is shown in Appendix 1 in the text. The background to the survey is explained in Box 11.2 on p. 301 in the text. You will need to open the tabten.sav file when you have SPSS open. In SPSS, select File/Open/Data and browse in the usual way to locate where you have saved you tabten.sav file and click on Open. Obtaining univariate frequency tables To obtain one-way descriptive summaries for categorical variables, you will need the SPSS Frequencies procedure. This is in the Analyze/Descriptive Statistics drop-down menu from the menu bar at the top. Click on Analyze, then Descriptive Statistics, then Frequencies. The Frequencies dialog box will appear. All variables are listed in the left box. To obtain a frequency count for any variable simply transfer it to the Variables box by highlighting it, then clicking on the direction button in the middle. If you hold down the left mouse button while dragging the mouse you can highlight adjacent variables in one move. Click on OK and, hey presto, you obtain a frequency count for each variable. This is ideal for univariate analysis, which is explained in Chapter 11. Table 1 illustrates the default table, which gives you Frequency, Percent, Valid percent and Cumulative Percent. Table 1 SPSS Frequencies output Who encouraged you? Valid Missing Total Friend Parent Other relative Teacher Club leader Other Total System Cumulative Frequency Percent Valid Percent Percent Notice that there are 35 Missing and 85 Valid cases in this table. That means that 35 people did not answer this question. So the 33 valid cases who said they were encouraged by a friend represent 27.5 per cent of the total sample of 120, but 38.8 per cent of the 85 valid cases this is the Valid percent. The Cumulative Percent accumulates the Valid Percents so that, for example, a total of 74.1 per cent were encouraged either by a friend or by a parent. Since the scale here is nominal, the order is not important and reflects the order in which the value labels were entered. Where there are no missing cases the Percent and Valid Percent are the same. You can edit the table to remove these columns if you wish using the edit menu. With the table highlighted (there will be a frame around it and a red arrow to the left) you can select Edit and Copy and then Paste it into any other application like Word or Powerpoint.

11 Obtaining graphs and charts If you click on Charts in the Frequencies dialog box you obtain the Frequencies:Charts dialog box. Simply click on Bar Chart or Pie Chart as appropriate and indicate whether you want the axis label to display frequencies or percentages, click on Continue and then OK. This will give you a basic bar chart or pie chart in addition to the frequencies table. To obtain stacked or clustered bar charts (these are explained in Chapter 12) you will need to select the Graphs/Bar drop-down menu to give you the Bar Charts dialog box. In this box you can also choose between summaries for groups of cases, summaries of separate variables, or values of individual cases. You would normally use summaries for groups of cases, but what happens if you select summaries of separate variable is illustrated in Figure 12.3 (p.342) in the text. Once you have obtained your chart you can edit it. Select the chart you want to edit by double-clicking in the chart area and you will obtain the Chart Editor. Try changing the chart in various ways to see what the Chart Editor can do for you. Close the Editor when you have finished. If you now select Edit/Copy you can copy the chart into other applications. N.B. The procedures for editing charts vary a little between versions 13.0, 14.0 and Crosstabulating variables The next stage in any data analysis is to look at the relationships between variables. For example, is there any relationship between whether or not anybody else in the household plays table tennis (else) and how often they play per week (play)? For this you need the Crosstabs procedure. This generates contingency tables for non-metric variables. Select Analyze/Descriptive Statistics/Crosstabs to obtain the Crosstabs dialog box. Enter your dependent variable (play) in the Row(s) box so it will appear at the side, and the independent variable else in the Column(s) box. Click on OK. To obtain column percentages, click on the Cells button in the Crosstabs dialog box to obtain Crosstabs: Cell Display. Click on Column in the Percentages check box, then on Continue, then on OK. Notice that the frequencies (called Count ) and the percentages to one decimal place are shown in each cell. If you want to display just the percentages just deselect Observed in the Counts box in Crosstabs: Cell Display. To obtain 3-way and n-way tables put the control variable, e.g. gender, in the Layer box and click on OK. You will, in effect, obtain two crosstabulations, one underneath the other, one for males and one for females. You can layer by more than one variable, but the frequencies in the cells get very low and the whole thing becomes more difficult to interpret. Try it and see what happens! Recoding variables If you need to transform a variable by regrouping categories, then it is the SPSS Recode procedure that you need. Select Transform/ Recode/Into Different Variables. For the five variables in Question 8 it might be helpful to add together unimportant and fairly unimportant into a new category, and to add together fairly important and very important into another category. To do this, select Social benefits and put into the Input Variable -> Output Variable box. Now click on Old and New Values. We need codes 1

12 and 2 to become code 1 so in the Old Value dialog area on the left click on the first Range radio button and enter 1 through 2. In the New Value dialog area on the right enter 1 in the Value box and click on Add. This instruction will now be entered into the Old -->New box. Code 3 we want to change to 2 so click on the Value radio button under Old Value and enter 3. Now enter 2 under New Value and click on Add. We now want codes 4 and 5 to be code 3. Click on the Range radio button and enter 4 through 5. Under New Value enter 3 and click on Add. Click on Continue. Give the Output Variable a name in the Name box, for example, socben3 and click on Change then OK. The new variable will appear as the last column. To add value labels for the new variable, change to the Variable View. Click on the Values cell in the appropriate row and obtain the Value Labels dialog box. Enter 1 in Value and Unimportant under Value Label and click on Add. Now enter 2 in Value and Neither under Value Label and click on Add. Finally, enter 3 in Value and Important under Value Label and click on Add. Now click on Continue and OK. You can now check this out using Analyze/Descriptive Statistics / Frequencies procedure. Computing new variables From Q9 generate an average satisfaction score for each of the 5 items. Using Transform/ Compute, create a total average satisfaction score. [Put the items in the Numeric Expression box with a + between them. Bracket the complete expression and divide by 5. Don t forget to give the Target Variable a name like totalsat.] Use Analyze/Descriptive Statistics / Descriptives to produce summary measures. You can now use Recode to group the scores into a limited number of categories - try it. Totalsat can now be crosstabulated against categorical variables. Reliability analysis Conduct a reliability analysis on Q9 to see if it could be reliably scaled into an overall index. Select Analyze/Scale/Reliability Analysis. Check that Model is set at Alpha. Select the 5 items in Q9 and put them into the Items box. Click on OK. Note the value for Alpha. Check what happens to Alpha if items are excluded from the analysis. Click on the Statistics button and select Scale if item deleted. Cronbach s coefficient alpha is explained in Box 5.4 (p.143) in the text. Analysing multiple response questions Analyse Q19 using Analyze/Multiple Response/Define Sets. Transfer foot, tennis, squash, badmin from the Set definition box to the Variables in Set box. Click in the Dichotomies Counted value radio button in the Variables Are Coded As box and enter the value to be counted as part of the set, e.g. if the value 1 has been entered on the questionnaire to indicate that this item has been selected, then enter 1 in this box. Don t forget to give a name to the set. Entering a label is optional. Click on Add, then Close. Several multiple response sets may be defined at the same time. These are then listed in the Mult Response Sets box. Note that these do not appear as new variables. To access them you need to reselect Analyze/Multiple Response and either Frequencies or Crosstabs. The sets so defined may now be used either to show frequencies or they may be used in Crosstabs.

13 You will for the latter, however, need to define the value range of the other variable from the Define Range button. Scattergrams To create a scattergram of two metric variables you need to select Scatter from the Graphs menu. In the Scatterplot dialog box select Simple followed by Define. Put agebegan and spend on the X and Y axes and click on OK. Note the pattern of dots. Obtain Pearson s r for these two variables from Analyze/Correlate/Bivariate. Transfer agebegan and spend into the Variables box, check that Pearson is ticked under Correlation Coefficients and click on OK. Note that r = 0.808, which is quite high and you are told that it is statistically significant. Try using Scatter for benefits and health and see what happens. These variables are discrete metric, not continuous metric. Treating the former as if they were continuous can produce some peculiar results!!!

14 Session 3 More advanced procedures on SPSS In this session you will use SPSS to undertake more sophisticated operations. Access the M&SUK.sav file from the Thomsonleaning website and save onto your laptop or networked account. The background to the data is explained on p.437 in the text. The SPSS functions we will look at include: Custom tables Analysis of variance Factor Analysis Regression analysis Custom tables For all the books and manuals on SPSS there is seldom anything on the Custom Tables function yet it is extremely useful for market researchers. The latter part of the M&SUK dataset contains a number of demographic variables including: Gender Age Nationality Household Size Occupation Shopping frequency in M&S Household income It would be helpful to get a frequency plus percent for each of these in order to be able to describe the characteristics of the sample. If you use the normal Frequencies function, you will end up with seven different tables. To obtain all of these in a single table like Figure 1, select Analyze/ Gender Group Total Age Group Total Nationality Group Total Household Size Group Total Occupation Group Total Shopping frequency in M&S Group Total Household income Group Total Figure 1 Basic Tables Output Male Female 25 years or less years years More than 45 years British Other European countries Overseas 1 member 2 members 3-4 members 5 members or more Not in formal employment Employed (by a third person) Self-employed More than once a week Once a week Once every two weeks Once a month Once every two months 2-3 times a year Less than per year per year More than per year Count Col % % % % % % % % % % 5 3.3% 3 2.0% % % % % % % % % 1.7% % % % % % 7 4.7% 3 2.0% % % % % % % Tables/Basic Tables. Put these seven variables into the Subgroups/Down: box and change the radio button to Each separately (stacked). Click on the Statistics button and bring Count and Col % across to the Cell Statistics box. Click on Continue. If you want totals, click on Totals and check the box Totals over each group variable. Click on Continue then OK. Try some of the other functions on the Basic Tables box, e.g. Layout, Format and Titles. Basic Tables is fine for separate variables where the categories are different for each variable. Suppose, however, the response categories are all the same and you want a table that sets the responses out as a matrix, as in Figure 2. For this you need the Tables of Frequencies

15 function. Select Analyze/ Tables/ Tables of Frequencies. Put the Likert items into the Frequencies for: box. Click on Layout and change the radio button for Variable Labels to Down the side. Click on Continue, then OK. Figure 2 The Tables of Frequencies Output Store is clean and tidy Store decor is attractive Store layout makes shopping easy Store atmosphere is excellent Wide selection of different products Products are of a good quality Merchandise is fashionable St Michael is a reliable brand Prices charged are fair Prices low compared to similar stores You get good value for your money Relationship price/quality is good Store personnel are kind and helpful Salespeople have good knowledge of the products Store operates an easy return policy Store offers a high level of customer service M&S transmits a reliable image M&S projects a conservative image M&S has a clear British appeal M&S serves the middle class You have total confidence in M&S You find M&S totally trustworthy M&S will never let you down M&S is a world class retailer Strongly Disagree Count Generally Disagree Moderately Disagree Neutral Count Moderately Agree Generally Agree Strongly Agree Count Count Count Count Count You may need to adjust some of the column widths. Double-click on the table to get the Edit mode then, as in Excel, drag the column bars to increase the width. Again, try out some of the function buttons in the Tables of Frequencies box. The other functions under Tables are General Tables and Multiple Response Tables. General Tables produces publication-quality tables displaying crosstabulations and subgroup statistics. You can produce tables showing different statistics for different variables, multiple-response variables, mixed nesting and stacking, or complex totals. Multiple Response Tables produces basic frequency and crosstabulation tables in which one or more of the variables is a multiple response set. You are not required to have a multiple response set defined to use this procedure, but you may obtain better results with Basic Tables if you do not need to use a multiple response set. Analysis of variance (ANOVA) This is under Analyze/Compare Means, which, in turn, gives you: Means One-Sample T-Test Independent-Samples T-Test Paired-Samples T-Test One-Way ANOVA Suppose we suspected that males and females would have different views about store layout, décor, tidiness etc. We could compare the mean scores of the males and females, as in Figure 3. To obtain this, select Analyze/Compare Means/ Means. Put the first four variables into the

16 Dependent List: box and Gender into the Independent List: box. From the options button you can choose the statistics you want. The biggest difference, for example, seems to be on store décor. Figure 3 Comparing Means Gender Male Female Total Mean N Mean N Mean N Store layout makes Store Store is clean Store decor shopping atmosphere and tidy is attractive easy is excellent If the 150 cases are a random sample, we can test whether or not these differences are statistically significant. For this we need the Independent- Samples T-Test, so select Analyze/Compare Means/ Independent-Samples T-Test. Put the same four variables into the Test Variable(s) box and Gender into the Grouping Variable box. You will need to Define Groups. Enter 1 for Group 1 and 2 for Group 2. Click on Continue and OK. All you need to look at is the Sig. (2-tailed) column to see that none of the differences are statistically significant (See Independent Samples Test below). Independent Samples Test Store is clean and tidy Store decor is attractive Store layout makes shopping easy Store atmosphere is excellent Equal variances assumed Equal variances not assumed Equal variances assumed Equal variances not assumed Equal variances assumed Equal variances not assumed Equal variances assumed Equal variances not assumed t-test for Equality of Means 95% Confidence Interval of the Mean Std. Error Difference t df Sig. (2-tailed) Difference Difference Lower Upper E E E E E E E Gender is binary, so we were able to use the t-test, but if instead we wanted to use Nationality, there are three groups (or three samples as it would be called by statisticians), so we would need to use the One-Way ANOVA. Select Analyze/Compare Means/One-Way ANOVA. Put the same four variables into the Dependent List: and Nationality into the Factor list: Click on OK. Notice that there is a statistically significant difference for store attractiveness and store atmosphere. (See ANOVA below) What that difference actually is, you would need to go back to the Compare Means procedure.

17 Store is clean and tidy Store decor is attractive Store layout makes shopping easy Store atmosphere is excellent Between Groups Within Groups Total Between Groups Within Groups Total Between Groups Within Groups Total Between Groups Within Groups Total ANOVA Sum of Squares df Mean Square F Sig Factor analysis There are 24 Likert items in the M&SUK dataset. Are any of these grouped together and unrelated to other groupings? Factor analysis is available under Analyze/Data Reduction/Factor. Put the 24 Likert items into the Variables: box. Before running the analysis, it is necessary to select some options that regulate the manner in which the analysis takes place and produce some extra items of output. To be totally minimalist, click on Descriptives and remove any ticks in any of the boxes. Using the Extraction button tick on Scree plot and Continue. Under Rotation, click on Varimax and Display Rotated solution. Click on Continue, then OK. The scree plot gives you the amount of the total test variance that is accounted for by a particular factor. Thus factor 1 has an eigenvalue of over 8.0, factor 2 has an eigenvalue of over 2.0 and so on. Eigenvalues of less than 1.0 are excluded from the analysis, so only the first 5 factors have been extracted. The rotated component matrix shows you how each variable is related to each factor. These are the loadings. Factor 1 is most highly loaded on Products are of good quality, Merchandise is fashionable, St. Michael is a reliable brand, and the last four items. By contrast, factor 2 is loaded against items that have to do with pricing and value for money. Factor 3 relates to personnel and returns policy and so on. Together, the 5 factors explain over 60% of the variance. Regression Regression and multiple regression is available in SPSS under Analyze/ Regression. Most regression will be Linear. This gives the Linear Regression box. There will always be just one dependent variable that has to be a metric variable that the researcher is trying to explain, so there is no point in putting a variable like age into this box!! In this dataset, there is not really a suitable metric dependent variable. In the table tennis study, spend on table tennis might be something that we would wish to explain. However, imagine that the researcher thinks that the key variable or attitude that M&S wants to promote is that it is seen as a world class retailer. We could treat this as a dependent variable. The independent variables also have to be metric, so put the first few Likert items into the Independent box. (Or put all the remaining Likert items in). The Adjusted R Square gives you the total variance that is explained by the items you have put in. In the figure below it is only 0.3.

18 Model 1 Model Summary Adjusted Std. Error of R R Square R Square the Estimate.582 a a. Predictors: (Constant), Merchandise is fashionable, Store layout makes shopping easy, Store is clean and tidy, Wide selection of different products, Products are of a good quality, Store decor is attractive, Store atmosphere is excellent The ANOVA table gives you the statistical significance and the coefficients table gives you the constants that you would need to complete the regression equations in order actually to make a prediction of perceptions of M&S as a world class retailer from the other Likert items.