Workshop in Applied Analysis Software MY591. Introduction to SPSS

Transcription

1 Workshop in Applied Analysis Software MY591 Introduction to SPSS Course Convenor (MY591) Dr. Aude Bicquelet (LSE, Department of Methodology) Contact:

2 Contents I. Introduction Purpose and outline of this course Introducing SPSS Access to SPSS... 5 II. Using SPSS Starting SPSS and creating a new data file... 6 Starting SPSS... 6 Software structure... 6 Naming and defining variables... 8 Entering data Saving files in SPSS Working with an existing data file Open existing data file Managing your dataset Split file Select cases Working with Weights Data operations Computing Recoding Count Descriptive statistics and graphs Summary statistics for categorical variables Simple bar chart Summary statistics for continuous variables Histograms Scatterplot Statistical analysis Crosstabulation Correlations Mean comparison: T test and One-way ANOVA Simple and multiple linear regression Predicting values Working with the Syntax Structure of the Syntax List of commands Saving the syntax Working with the output Saving the output Saving the output from SPSS into Word and Excel

3 III. Exercises Beginners Exercise 1.1: Create a new data file Exercise 1.2: Work with an existing dataset Intermediate Exercise 2.1: Data management Exercise 2.2: Statistical analysis Advanced Exercise 3.1: Linear Regression and fitted values Exercise 3.2: Working with syntax IV. Other References Online Resources Library Resources

4 I. Introduction 1. Purpose and outline of this course The purpose of this course is to provide a guide to use SPSS. It will focus on the different features provided by SPSS to conduct statistical analysis and present findings. It does not aim to provide teaching in statistics or research methods. Rather, it should be thought of as a complement to other statistical courses, such as the ones provided by the Methodology Institute (MY451, MY452, MY455). The course is structured in three levels: beginners, intermediate and advanced. The class for beginners is designed for those who have no or almost no previous experience using the software. In this course we will introduce SPSS and get you started with creating new and working with existing datasets. We will learn how to do basic data operations, such as recoding or computing new variables. The course will finish with some basic descriptive analyses and the presentation of results. The intermediate class assumes some basic knowledge in SPSS and statistics, and can be taken as a continuation of the beginner s class. In this class we will use SPSS to perform slightly more advanced tasks, such as working with weights, as well as using more complex operations to compute new variables. We will move from basic statistical analysis to more advanced bivariate analysis (group comparisons and correlations). In the advanced class we will consider more complex features of SPSS. We will look at how to run regression analysis, to predict values and create graphs of fitted values. An introduction to the use of syntax in SPSS will also be provided. This handout is structured as a general guide to the use of SPSS. It covers the material discussed in all three levels. However, the classes will be structured around the exercises at the end of the document. Exercises are separated by level and a reference is made to the section of the handout where you can find the instructions. For the class and the exercises we will work with data from the European Social Survey. Specifically, we will use Round 4 (2008) for the United Kingdom. Datasets for other countries and rounds can be downloaded from the European Social Survey website ( 4

5 2. Introducing SPSS SPSS is a versatile and powerful data management and analysis software package that will perform a wide variety of statistical procedures. The original acronym stands for Statistical Package for the Social Sciences ; however, the branding has slightly changed over the years and with various versions, e.g., Version 18 PASW (Predictive Analytics Software) Version 19 IBM SPSS statistics. SPSS provides a comprehensive set of flexible tools that can be used to accomplish various data analysis tasks. It is well-suited to accommodate different exploration strategies such as surveys and experiments in diverse fields of enquiry. SPSS is among the most widely used programs for statistical analysis in business, government, health, education, research and academic organisations. 3. Access to SPSS SPSS is available to be purchased by LSE students from the IT Help Desk. The software is available for Windows and Mac OS. For more information, visit: 5

6 II. Using SPSS 1. Starting SPSS and creating a new data file Starting SPSS To start SPSS select Start > All programs > Specialist and Teaching Software > Statistics > SPSS > SPSS19. A window will appear asking whether you want to open an existing data source (Figure 1). To create a new dataset select Cancel and you will see the data editor (Figure 2). Figure 1. Start window Software structure The Data Editor is used for entering and editing data. This worksheet has three main features: a. Menu. This is where you select actions you wish to perform e.g. open new files, conduct data operations and run analyses b. Data view. This is where data is entered. Each row represents one case or unit of analysis (e.g. one participant, one country) and each column represents one variable c. Variable view. This is where you name and define your variables 6

7 Figure 2. Data Editor There are two main file types that you will be working with and a third one for more advanced users (Figure 3): a. Data. This is where you save your data. SPSS uses a.sav extension for datasets. b. Output. When a procedure is run, a new window is opened with the results. SPSS uses a.sps extension for the output. While you can save output files, you might prefer to copy the tables to a word processor (see Section II.8). c. Syntax. Instead of using the menus, it is also possible to write scripts to do all different types of operations and analyses (see Section II.7). Figure 3. File types 7

8 Naming and defining variables To name and define your variables, click on the Variable View tab in the bottom left hand corner of the Data Editor. The Variable View is different from the Data view. Here each row represents a variable and each column represents an attribute of this variable (Figure 4). Figure 4. Variable view In the following, these different attributes will be described: a. Variable Name. Variable names need to be unique and have a length that does not exceed 64 bytes. They need to begin with a letter and cannot have spaces or special characters. b. Variable Type. The default is Numeric, which means that only numbers can be entered into the cells (Figure 5). This does not necessarily mean that the variable is a numeric one. Rather, numeric means that numbers are used to define different values of the variable. In some cases like gender, we might want to use the number 1 for females and 2 for males. In this case, our variable is numeric even though the number is only used to label a category and has no meaning in itself. If you choose String you will be able to enter text. Figure 5. Variable type c. Variable width and decimal places. Width determines the maximum number of characters that will be displayed for the variable in all outputs. This is 8

9 especially relevant when you are using String and enter text. Decimals indicate the number of decimal places that will be displayed and is relevant for numeric data. d. Variable labels. Variable labels allow adding more information for each variable. In contrast to the variable name, variable labels have no restrictions on using symbols and spaces, and the maximum length is considerably larger (255 characters). Figure 6. Value Labels e. Value labels. To assign labels to a variable s values open the Value Labels window (Figure 6). Value labels are useful when working with nominal or ordinal variables where numbers are used to represent categories of a variable. For example, for gender one might use 1 for females and 2 for males. Assigning labels to these numbers helps to clarify what 1 and 2 mean in this context. To add a new value label, define the value and related label and click on Add. Do this for each label. f. Missing values. This is where you define the values that should be excluded from the analysis. For example, you might not want to consider Don t know responses or it might be that some answers are not applicable (for example, to ask a non-smoker the number of cigarettes he smokes per day). While SPSS considers an empty cell as a system-missing value, user-defined missing values can provide more information for your research (for example, the distinction between those who do not know and those who refuse to answer). To define missing values, type in the numbers that have been defined as missing values (for example, 99) and these will be excluded from further analyses. You can also determine a range of values (Figure 7). 9

10 Figure 7. Missing Values g. Columns and align. Columns and align refer to the display of the data in the Data View. The default is 8 characters for each column and to right-align numeric variables and left-align string variables. h. Measurement level of variables. This option is concerned with the measurement level of the variables. SPSS works with three measurement levels: Nominal. Different values represent different categories of the variable. Values are not ordered. For example: gender (male and female) or religious affiliation (Christian, Muslim and Jewish). Ordinal. Values are ordered in terms of degree, but they do not establish numeric differences between data points. For example: level of agreement or disagreement with abortion (disagree strongly, disagree, agree and agree strongly). Scale. SPSS does not differentiate between interval and ratio levels of measurement, but rather lumps together both of these quantitative variable types as "scale". Here values represent ordered categories with a meaningful metric, so that distance comparisons between values are appropriate. For example: age (35) and weight (145 pounds). Figure 8 shows an example of a variable view with three variables: age, gender and education. Age and years of education are numerical variables and value labels do not need to be assigned (as there is no other meaning than the number itself). The variable gender is nominal and value labels have been defined for females (1) and males (2). 10

11 Figure 8. Variable View example Entering data Once you are ready naming and defining all the variables in your study, you can start entering data. To do this, click on the Data View tab of the Data Editor Window. The variable names defined in Variable View now appear as columns (Figure 9). Click on the first cell and start entering data. For gender, you should use the numbers that have been assigned to each category (for example, 1 for female). Tip: In Section II.2 you will learn how to import datasets from another software. This allows you to choose a different software (for example, Excel) to enter your data and then export it into SPSS. Figure 9. Entering data If you click the Value Labels button, SPSS will display the value labels rather than the cell numbers (Figure 10). This also means that you can select the category from a list instead of typing in the corresponding number. 11

12 Figure 10. Value Labels Saving files in SPSS To save your file select File > Save as. SPSS allows you to save the file as an SPSS file (*.sav) or as a file used in other software (e.g. *.xls for Excel) (Figure 11). Tip: You can choose to save only some of your variables. To do this click on Variables and select only those you want to save. The default is to save all variables. Figure 11. Saving a file. 12

13 2. Working with an existing data file Open existing data file To open an existing file select File > Open > Data (Figure 12). SPSS allows working with a wide range of data sources, such as Stata and Excel files (Figure 13). Figure 12. Open new dataset Figure 13. Open new data set If you choose to read an excel file, SPSS will ask you to specify whether the first row in the Excel file contains the variable names. You also need to specify the worksheet where the data is contained (Figure 14). 13

14 Figure 14. Opening Excel Data Source Tip: It can be easier to enter data in Excel and then import the data to SPSS. If you do so, you can define only the variable names in Excel and define the variable and value labels after you import the data into SPSS. Figure 15 is an example of a data file (European Social Survey Round 4). Here you can see that all variable names and labels, as well as value labels and missing values have been defined. The first time you open an existing dataset created by someone else, it is important to pay attention to the way in which variables have been organised and coded. Tip: Most institutions that allow users access to their surveys will also provide a codebook that specifies the variables in the dataset and the associated coding. This can be very useful to make sure you understand how the dataset has been constructed. Figure 15. Data file example 14

15 3. Managing your dataset SPSS provides a wide range of options to manage your dataset under the option Data (Figure 16). In this course we will only focus on some of them: split file, select and weight cases. Figure 16. Options for data management Split file The split file option allows dividing the file in groups to run analyses. For example, it can be used to calculate descriptive statistics separately for men and women. Once this option is activated, all analyses will be run separately for the specified groups. The default is to analyse all cases. In Figure 17 we have specified that groups should be compared. Then we chose the variable Gender (gndr) to split the file. To split the file it is necessary to sort the file by your grouping variable. You can request SPSS to sort the file for you if it is not already sorted. 15

16 Figure 17. Split file Tip: It is sometimes difficult to find the variables of interest in large data files. By right clicking on the variable list you can request having the variable names displayed instead of the variable labels. You can also sort the variables alphabetically instead of the file order (Figure 18). Figure 18. Displaying variable names 16

17 To check whether you have the split file option on, look at the bottom right corner of SPSS (Figure 19). It specifies whether the file is being split. Figure 19. Split file in Data Editor Tip: To run analyses using the whole dataset, go back to the Split file option and select Analyze all cases, do not create groups. Select cases This function allows to select part of the dataset and to exclude the remaining cases from the analyses. For example, you might only be interested in the female respondents. To select some cases, select Data > Select Cases option (Figure 20). The default is to include all cases. If you would like to select only some cases, select If condition is satisfied. You can also choose what to do with the unselected cases. You can filter them out, which means that they will still be in the dataset, but not considered for any analysis. This is the safest option, as it means that you are not deleting the unselected cases. You can also copy the selected cases to a new dataset or delete the unselected cases. Figure 20. Select cases 17

18 The next window allows you to define certain conditions (Figure 21). Here, we specify that cases should be selected if gender is equal to 2 (female). Tip: You can also use this function to select a random sample of your cases. Figure 21. Select cases if condition is satisfied If you selected to filter out the unselected cases, cases that are not being used will now have a slash through the case number (Figure 22) and the bottom right corner of SPSS will specify that the filter is on (Figure 23). Tip: To go back to using the whole dataset, you need to go back to the Select cases option and select All cases. 18

19 Figure 22. Filtered cases Figure 23. Filter On Working with Weights The weight option is used to give some cases in your dataset more weight in the analyses. This is usually done for sampling reasons. For example, it often happens that women are over-sampled (i.e. there is a larger proportion of women in your sample than in the population). Using weights allows giving over-sampled cases less weight and under-sampled cases more weight to try to resemble the population proportions. To weight your dataset, select Data > Weight cases. The default is to not weight cases (Figure 24). To weight the dataset, select Weight cases by and choose the variable containing the weight for each respondent. 19

20 Figure 24. Weights As before, you can check whether the weight is on by looking at the bottom right corner of SPSS (Figure 25). Figure 25. Weights in Data Editor Tip: To run analyses without using weights, you need to go back to the Weight Cases option and select Do not weight cases. Tip: It is not always appropriate to use weights. It is widely agreed that weights should be used for descriptive analyses. However, statisticians differ in their approach to whether weights should be used in other types of analyses, such as regression analyses. 20

21 4. Data operations SPSS provides many different functions which allow creating new variables from existing variables. For example, you can use SPSS to recode variables into categories, create an average of variables or count the number of times that people agree with a set of statements. These functions can be found under the Transform option (Figure 26). In this course, we will look at three functions: compute variables, recode variables and count values within cases. Figure 26. Transform options Computing To compute a new variable select Transform > Compute Variable. Here you need to specify a name for your target variable and a numeric expression that specifies how this new variable will be constructed (Figure 27). For example, you might want to construct a scale by averaging different variables. In Figure 27, the mean of people s trust in institutions is constructed. To do this, you can either write in the upper textbox on the right hand side mean(variable1, variable2, variable3) or (variable1+variable2+variable3)/3. Tip: You can use the If function to compute the new variable only if some condition is satisfied. For example, compute trust in institutions only if someone voted in the last elections. 21

22 Figure 27. Compute Variables Recoding The recoding function allows modifying the values of existing variables. This function can be used to change the codes used for a variable, categorise a numerical variable into groups or correct errors in the data. For example, you might have asked people to rate their political ideology in a scale from 1 (left) to 10 (right). It might, however, be more useful to group people in 3 distinct categories (left, centre and right). To recode a variable, select Transform > Recode into Different variables (Figure 28). Here you need to specify the variable to be recoded and the name and label of the new variable you are creating. Do not forget to click on Change after you write down the name and label of the output variable. To define how the variable will be recoded, select Old and New Values. A new window will open (Figure 29). Here you can specify the value from the original variable (old value) and the new value you want to be assigned to it. You can also specify a range of values. In this case, we have specified that those respondents who answered a number between 1 and 3 will be assigned to the category 1 (left). Do not forget to click on Add after each new recoding command. After you have created the new variable, you should define the appropriate variable and value labels. 22

23 Figure 28. Recoding into Different Variables Figure 29. Old and New Values Tip: While it is also possible to recode into the same variable, this is strongly discouraged. If you choose to do this, you will delete the original variable and will not be able to do any further recoding. Tip: As before, you can use the if function to recode values only if some condition is satisfied. 23

24 Count The count option allows creating a new variable that counts the number of occurrences of a particular value across a list of variables. For example, we might be interested in creating a new variable which captures the number of times respondents said that they completely trusted an institution. To use this function select Transform > Count Occurrences of Values within Cases. Here you need to specify a target variable name and label and the variables within which it will search for the specified condition (Figure 30). Define the values to be considered by selecting Define Values. Figure 30. Count In the next window specify all the values that will be counted (Figure 31). Here, we are interested in those times when respondents said they completely trusted an institution (10). Do not forget to add each value. Figure 31. Count: Define Values 24

25 5. Descriptive statistics and graphs To run analyses with SPSS select the option Analyze, where you will find a list of possible analyses (Figure 32). In this guide we will focus on Descriptive Statistics, Compare means, Correlate and Regression. Figure 32. Analyze options Summary statistics for categorical variables To look at the distribution of your variables select Analyze > Descriptives > Frequencies. Move the variables you are interested in to the box at the right and then click OK (Figure 33). In this case, we are requesting the frequencies for the variables gender and total time of TV watching (overall and politics). After clicking OK, a new window will be opened containing the results of your query (Figure 34). Here you can see, for example, that 45.6% of the respondens are male. It also shows the percentage of missing cases (here, 0.4%). 25

26 Figure 33. Frequencies Figure 34. Output file 26

27 You can also obtain a bar chart by clicking on Charts in the Frequencies window and selecting Bar charts. You also need to define whether bars will represent frequencies (number of cases) or percentages (% of cases) (Figure 35). Figure 35. Analyze options Figure 36 shows an example of a bar chart for the variable gender created by SPSS. Figure 36. Bar chart A second option to obtain a bar chart is to use the Graphs option. SPSS offers a wide range of graph types. To create a graph, select Graphs > Legitimacy Dialogs (Figure 37). 27

28 Figure 37. Graphs options Simple bar chart To obtain a simple bar chart, select Graphs > Legacy Dialogs > Bar. Select Simple and Summaries for groups of cases (Figure 38). Figure 38. Bar Charts options In the next window move the variable of interest to Category Axis. (Figure 39) In this case, we are requesting a bar chart of the total time watching TV (which has been grouped in categories). Bars will represent the % of cases. Click OK to see the output. 28

29 Figure 39. Define Simple Bar The simple bar chart (Figure 40) shows that most respondents watch more than 3 hours of TV a day. 29

30 Figure 40. Bar Chart Tip: To edit the graph, double click on it and the Chart Editor will be displayed. Summary statistics for continuous variables Figure 41. Descriptives To obtain summary statistics for continuous variables, select Analyze > Descriptive Statistics > Descriptives. Move those variables you want to describe to the right box. In 30

31 this case, we would like to describe people s trust in different institutions (Figure 41). Since this is a numeric variable ranging from 0 (not at all) to 10 (completely), we would like to obtain the mean and some dispersion statistics. Click on Options to specify the required statistics (Figure 42). In this example, we will request the mean, standard deviation, minimum and maximum. You can also specify the order of display of the variables. Figure 42. Descriptives: Options Figure 43 shows the output of descriptive statistics for trust in different institutions. You can see that for all institutions the minimum value is 0 and the maximum value is 10. Around respondents answered each question. The means seem to suggest that people trust the police and the legal system more than they trust other institutions. Figure 43. Descriptive Statistics 31

32 Histograms To obtain a histogram, go back to Analyze > Descriptives statistics > Frequencies and click on charts. Select Histograms (Figure 44). You can also request it to show a curve on the histogram. Figure 44. Frequencies: Charts Figure 45 shows the output histogram for trust in the country s parliament. Figure 45. Histogram 32

33 Scatterplot Scatterplots chart one variable against another. They are useful to look at associations in the data between variables whose level of measurement is scale. In this example, we will look at the association between political ideology (0=left, 10=right) and respondent s agreement with the statement Government should reduce differences in income levels (1=agree strongly, 5=disagree strongly). Given the large size of the dataset, we first select a random sample of 2% of the dataset (`Data > Select cases`). To obtain the scatterplot, select Graphs > Legacy dialogs > Scatter/Dot and choose Simple Scatter in the next window (Figure 46). Figure 46. Scatter/Dot In the next window, we determine that we want to plot political ideology along the horizontal axis (X) and agreement with the government reducing income differences along the vertical axis (Y) (Figure 47). Click OK to obtain the graph. Tip: Scatter plots are a useful tool to look at the association between variables when the number of cases in the data set is reduced. For example, you might want to plot associations between variables for different countries. Tip: To label the cases in your scatter plot (for example, attach a label of the country), move the labelling variable to Label Cases by. Click on Options and select the option Display chart with case labels. 33

34 Figure 47. Simple Scatter plot The scatter plot output (Figure 48) suggests a positive association between the variables: those who place themselves more closely to the right also tend to disagree more with governments reducing differences in income level. 34

35 Figure 48. Scatter plot 35

36 6. Statistical analysis SPSS includes a wide range of features for conducting statistical analysis. In this course we will focus on crosstabs, correlations, mean comparisons and linear regression analysis. Crosstabulation To obtain a crosstabulation between two variables select Analyze > Descriptive statistics > Crosstabs (Figure 49). Move the variables to the right/hand boxes. In this case, we would like to know whether the levels of worry about home burglary are different for men than for women. Figure 49. Crosstabs Figure 50 shows the output of crosstabulation with the respective frequencies. You can see that 348 men and 344 women stated they never worry. However, since there are more women in the dataset, it might be more useful to obtain percentages instead of frequencies. To obtain percentages, go back to Analyze > Descriptive statistics > Crosstabs and click on Cells. 36

37 Figure 50. Crosstabulation frequencies Unselect the option Observed and select the option Column (Figure 51). By doing this, we will find out the percentage of men and women that worry a most of tiem time, some of the time, occasionally or never. Figure 51. Crosstabs: Cell Display Tip: It is customary to locate the response variable (here, worry) in the rows and the explanatory variable (here, gender) in the columns. If you do this, percentages should be requested for the columns, as this will make it possible to compare the percentages for two groups of the independent variable. 37

38 In order to test whether the difference between men and women is significant, we will request a Chi-square statistic. Go back to the main crosstabs window and click on Statistics (Figure 52). Select Chi-square and then continue. Figure 52. Crosstabs: Statistics Figure 53 and 54 show the output. Women seem to worry more about being burgled than men. 9.3% of women worry all or most of the time, compared to only 6.9% of men. In addition, only 27.1% of women worry never, while this number is higher in men (32.5%). A significant chi-square statistic suggests that this difference is significant at the 95% level. Figure 53. Crosstabulation column percentage 38

39 Figure 54. Chi-Square test We will now use a clustered bar chart to graph the differences in worry between men and women. Clustered bar charts allow to graph the same variable separated by a grouping variable, in this case gender. An easy way of doing this is by requesting such a graph when running a crosstab. Go back to Analyze > Descriptive statistics > Crosstabs and select the option Display clustered bar charts. A second option is to use the graphs function. To do this, select Graphs > Legacy dialogs > Bar Charts, and select Clustered (Figure 55). Click define. Move worry about burglary to the Category Axis space and define gender as the clustering variable. (Figure 56). Click OK and SPSS will produce your graph. Figure 55. Bar Charts options 39

40 Figure 56. Define clustered bar The output of the clustered bar chart graphically shows that a higher percentage of women worry all of the time and a lower percentage never worry (Figure 57). 40

41 Figure 57. Clustered bar chart Correlations To obtain correlations between variables select Analyze > Correlate > Bivariate. Move to the box on the right the variables of interest. In this example, we will look at the correlation between trusting the country s parliament, legal system, police and politicians (Figure 58). Since these variables are all of the scale measure, we request the Pearson correlation coefficient. Figure 59 shows the output of pair wise correlations between the variables. You can see that all variables are positively correlated. The strongest correlation is between trusting the country s parliament and politicians. All p-values are smaller than 0.05, indicating that all correlations are significant at the 95% confidence level. 41

42 Figure 58. Bivariate Correlations Figure 59. Correlations Mean comparison: T test and One way ANOVA Mean comparisons are used to evaluate differences between two or more groups. T tests are used when two groups are being compared. Independent-Samples T Tests are used when two independent samples are compared (e.g. males and females). Meanwhile, paired-samples T Test requires observations to be paired (for example, variables represent two measurements for the same person). In this course we will focus on independent samples T Tests. 42

43 Figure 60. Independent-Samples T Test To run a T-Test select Analyze > Compare means > Independent-samples T Test. Figure 60 shows the window that will be presented. Move the variable that determines the groups to Grouping Variable. You will also have to define the groups to be compared. In this case, the grouping variable is gender, with two groups being compared (1=males, 2=females). Now move the variables to be tested to the window Test Variables. In this case, we want to test whether males and females differ in their beliefs about whether social benefits make people lazy (1=agree strongly, 5=disagree strongly). The output of groups statistics indicate that the mean for men and women is actually very close (Figure 61). Consistently, the T Test finds no significant difference between both groups (Figure 62). Figure 61. Group Statistics for T-Test Figure 62. T Test results 43

44 Figure 63. One-Way ANOVA T Test can be used to evaluate the difference in means between two groups. One-way ANOVA extends T Test for cases where more than two groups are being compared. To conduct a One-way ANOVA select Analyze > Compare means > One-way ANOVA. As before, you need to specify the factor (grouping variable) and the list of variables to be tested (Dependent list). In this case, we will test whether people with different political ideologies (left, centre and right) differ in their beliefs about people on social benefits being lazy (Figure 63). This test does not provide descriptive results by default. To request them, click on Options and select Descriptive (Figure 64). Figure 64. One-Way ANOVA Options The output of descriptive results (Figure 65) indicate that left-wing people disagree more strongly with the statement Social benefits/services make people lazy (av=2.8) 44

45 than those who are in the centre (2.3) and those who are right-wing (2.1). What s more, results from the ANOVA test indicate that this difference is significant at the 99% confidence level (Figure 66). Figure 65. ANOVA - Descriptives Figure 66. ANOVA results Simple and multiple linear regression To run a linear regression select Analyze > Regression > Linear. Move your dependent and independent variables to the appropriate boxes and then click OK. As an example, in this case we want to predict people s level of trust in institutions (Figure 67). We start by testing whether educational level predicts people s trust. The results of the regression analysis (Figures 68 and 69) indicate that years of education has a positive and significant effect on trust in institutions. However, education only explains 1.3% of the variance in trust. 45

46 Figure 67. Linear Regression Figure 68. Linear regression results R-squared Figure 69. Linear regression results - Coefficients 46

47 We now want to evaluate whether the effect of education is an artifact due to younger people not yet finishing their education. We now add the respondent s age as a control variable in the regression analysis (Figure 70). Figure 70. Linear regression Results indicate that age has no significant effect on trust in institutions. Furthermore, the effect of education remains unchanged after controlling for age (Figure 71). Figure 71. Linear regression results - Coefficients Now, we want to evaluate whether people s interest in politics predicts their trust in institutions. We would like to compare those who are interested with those who are not 47

48 interested. We thus need to transform the variable interest in politics into a dummy variable with the categories 0=not interested and 1=interested. To do this, we recode the variable into a different variable (Figure 72 and Figure 73). We assign a 1 to responses 1 and 2 (very and quite interested) and a 0 to responses 3 and 4 (hardly and not at all interested). Figure 72. Recoding into different variables Figure 73. Recoding into different variables: Old and New values 48

49 Figure 74. Linear regression We can now add the dummy variable interest in politics to the regression (Figure 74). Results show a significant effect of interest in politics: those who are interested in politics trust institutions more (Figure 75). Figure 75. Linear regression results - Coefficients Predicting values It is often difficult to interpret the real importance of the explanatory variables in predicting the outcome variable. Calculating and plotting fitted values can be useful to 49

50 show the strength of association. The coefficients in the equation are here used to predict values in the outcome variable, according to values assigned to each explanatory variable. SPSS does not provide an easy way of predicting values. One option is to do it by hand and use Excel or Word to draw a graph. A second option is to open a new dataset in SPSS and use the command Compute to calculate fitted values. Tip: Stata provides better tools for post-estimation. In this example, we will use the equation above to predict levels of trust in institutions. Particularly, we will plot the effect of education. We will then distinguish between those who are interested in politics and those who are not to predict two fitted lines and plot them together. Before opening a new dataset, it is useful to create a table of descriptive statistics. We will use this to fit the remaining variables (here, age) to the mean. It is also important to know the minimum and maximum of each variable to plot the fitted lines using the whole scale. To obtain descriptive statistics select Analyze > Descriptive Statistics > Descriptives. Follow the steps described in Section II.5 to obtain the mean, minimum and maximum values for the variables of interest (Figure 76). Figure 76. Descriptive statistics Now, open a new dataset by selecting File > New > Data. Create a new variable labelled education and enter values from 0 to 25 in a range of 5 (Figure 77). We will now use the regression coefficients to predict the level of trust for respondents with these different levels of education. 50

51 Figure 77. New dataset Now we need to compute the predicted level of trust. Select Transform > Compute Variable. We will name the predicted variable trust_fitted_interested, as we will estimate the level of trust for someone who is interested in politics. Use the coefficients in Figure 65 above to write down the equation. We fit age to the mean (49) and interest in politics to 1 (interested). We do not assign any value for education, but let it vary according to the values we have entered (Figure 78). Click OK and move back to the data view to look at the new column that has been created (Figure 79). These are the predicted values for trust in institutions for different levels of education. Figure 78. Compute variable 51

52 Figure 79. Fitted values Now we will plot this line using a Line graph. Select Graphs > Legacy Dialogs > Line and Simple chart for Values of individual cases in the next window (Figure 80). Figure 80. Line charts In the next window move the fitted variable to Line represents and education to Category labels > Variable (Figure 81). Click OK. The output graph of fitted values shows that when years of education increase, people s trust in institutions increases as well (Figure 82). 52

53 Figure 81. Define simple line Figure 82. Graph of fitted values 53

54 However, this chart can be misleading in that the values on the Y-axis have been adjusted to the minimum and maximum levels computed. To look at the real extent of the relationship, edit the chart to show the whole range of values in the Y-axis. To do this, double clik on the chart and the chart editor will be displayed (Figure 83). Click on the Y-axis values and select the tab Scale. Set the minimum and maximum values to 0 and 10, respectively (the descriptives in Figure 66 indicate that this is the full range of the variable trust in institutions). Click on Apply and your chart will be changed (Figure 84). While the new chart shows the same relationship as before, it does not exagerate the steepness of the line. Figure 83. Editing charts 54

55 Figure 84. Graph of fitted values Now, we would like to plot two separate lines showing the effect of education: one for those who are interested in politics and one for those who are not. We already computed fitted values for those who are interested in politics. We now need to replicate this step, changing the valu of interest in politics to 0 and creating a new variable, this time called trust_fitted_not_interested (Figure 85). Click OK and make sure that the new column has been created in the dataset. 55

56 Figure 85. Compute variable To create a graph with multiple lines, select Graphs > Legacy Dialogs > Lines and this time select Multiple instead of Simple (Figure 86). Click define. In the next window select both fitted values as Lines represent and education as Category Labels > Variable (Figure 77). Click OK. Figure 86. Line charts 56

57 Figure 87. Define multiple lines We have now created a graph showing the effect of education separate for those who are and are not interested in politics. The graph indicates that education has a positive effect and that those interested in politics are also higher in trust than those who are not interested. Tip: A related question is whether there is an interaction effect between education and interest in politics. Interaction effects allow the effects of one variable to depend on the level of a second variable. In this case, it would be possible to test whether the effect of education is stronger or weaker for those who are interested in politics than those who are not. Graphically, this means allowing the lines of education to have different slopes for those who are interested and those who are not (Figure 88). To include interaction effects, compute a new variable which is the product of the two original ones and include it in the regression next to the main effects for both variables. 57

58 Figure 88. Graph of fitted values 58

59 7. Working with the Syntax Until now, we have used the menus in SPSS to transform the data and perform statistical analyses. A second option is to work with SPSS in syntax mode. When using the syntax, you need to type codes in a syntax editor and submit it to SPSS to get results. Working with syntax is more advanced, as you need to learn how to write the code to produce outputs. However, it is also more flexible and can be faster, especially when you need to conduct similar analyses multiple times. Furthermore, it allows saving your data transformations and analyses and can be used to review them, making sure that there are no problems with your transformations or analyses. Structure of the Syntax To open the syntax editor, select File > Open > Syntax. A new window is displayed (Figure 89). Figure 89. Syntax editor 59

60 A good way to start using the syntax editor is to use the menus to define a transformation, but selecting Paste instead of running the transformation. This will paste the code that corresponds to the transformation or analysis into the syntax editor. This is a great way of learning how to write SPSS code. For example, in Figure 90 you can see the code to recode political ideology into categories. The code requests to recode the variable lrscale and creates a new variable lrscale_cat by recoding the values 1, 2 and 3 ( 1 thru 3 ) to 1, the values 4, 5, 6 and 7 ( 4 thru 7 ) to 2 and the values 8, 9, 10 ( 8 thru 10 ) to 3. It also defines the variable label of the new variable lrscale_cat as Political ideology cat. Commands need to start on a new line and to end with a period to indicate that the command is being terminated. At the end, you need to add the line Execute. Figure 90. Syntax example To run the syntax, select the text and click on the green arrow (you can also click Control+R) (Figure 91). This will produce the same changes in the data set as if you had used the option Recode into a different variable through the menu. Figure 91. Running syntax 60

61 List of commands Defining variable labels VARIABLE LABEL age 'Age' gender 'Gender' education 'Years of education completed'. EXECUTE. Defining values labels VALUE LABELS gender 1 Female 2 Male. EXECUTE. Saving files in SPSS SAVE OUTFILE='C:\SPSS files\ess4gb_new.sav' /Compressed. Split file SORT CASES BY gndr. SPLIT FILE LAYERED BY gndr. EXECUTE. Select cases USE ALL. COMPUTE filter_$=(gndr=2). VARIABLE LABEL filter_$ 'gndr=2 (FILTER)'. VALUE LABELS filter_$ 0 'Not Selected' 1 'Selected'. FORMAT filter_$ (f1.0). FILTER BY filter_$. EXECUTE. Weight WEIGHT BY dweight. 61

62 Computing COMPUTE trust_mean=mean(trstprl,trstlgl,trstplc,trstplt,trstprt,trstep,trstun). EXECUTE. Recoding RECODE lrscale (1 thru 3=1) (4 thru 7=2) (8 thru 10=3) INTO lrscale_cat. VARIABLE LABELS lrscale_cat 'Political ideology cat' VALUE LABELS lrscale_cat 1 Left 2 Centre 3 Right. EXECUTE. Count COUNT trust_count=trstprl trstlgl trstplc trstplt trstprt trstep trstun(10). VARIABLE LABELS trust_count 'Times complete trust'. EXECUTE. Frequencies FREQUENCIES VARIABLES=gndr tvtot tvpol /BARCHART FREQ /ORDER=ANALYSIS. Simple bar chart GRAPH /BAR(SIMPLE)=PCT BY tvtot. Clustered bar chart GRAPH /BAR(GROUPED)=PCT BY tvtot BY gndr. Descriptives DESCRIPTIVES VARIABLES=trstprl trstlgl trstplc trstplt trstprt trstep trstun /STATISTICS=MEAN STDDEV MIN MAX. 62

63 Histogram FREQUENCIES VARIABLES=trstprl /FORMAT=NOTABLE /HISTOGRAM NORMAL /ORDER=ANALYSIS. Scatterplot GRAPH /SCATTERPLOT(BIVAR)=lrscale WITH gincdif /MISSING=LISTWISE. Crosstab CROSSTABS /TABLES=brghmwr BY gndr /FORMAT=AVALUE TABLES /STATISTICS=CHISQ /CELLS=COLUMN /COUNT ROUND CELL. Correlations CORRELATIONS /VARIABLES=trstprl trstlgl trstplc trstplt /PRINT=TWOTAIL NOSIG /MISSING=PAIRWISE. T-Test T TEST GROUPS=gndr(1 2) /MISSING=ANALYSIS /VARIABLES=sblazy /CRITERIA=CI(.95). One-Way ANOVA ONEWAY sblazy BY lrscale_cat /STATISTICS DESCRIPTIVES /MISSING ANALYSIS. 63

64 Regression Analysis REGRESSION /MISSING LISTWISE /STATISTICS COEFF OUTS R ANOVA /CRITERIA=PIN(.05) POUT(.10) /NOORIGIN /DEPENDENT trust_mean /METHOD=ENTER eduyrs agea. Graph of fitted values with single line GRAPH /LINE(SIMPLE)=VALUE(trust_fitted_interested) BY education. Graph of fitted values with multiple lines GRAPH /LINE(MULTIPLE)=VALUE(trust_fitted_interested trust_fitted_not_interested) BY education. 64

65 Saving the syntax To save the syntax, select File > Save as and select a folder to save your file (Figure 92). Syntax are saved as *.sps and can only be read with SPSS. Figure 92. Saving syntax 65

66 8. Working with the output There are many different ways of working with your output. Most of the times, it will require exporting tables and graphs to a different software (such as Word or Excel) for editing purposes. Saving the output To save the output select File > Save as. SPSS will save the file to a *.spv extension (Figure 93). The problem with this output file is that it can only be read by SPSS. Figure 93. Saving the output Saving the output from SPSS into Word and Excel A second option is to export the output into another software, such as Word or Excel. To export the output, select File > Export. You can define whether you would like to export all objects or only visible ones (Figure 94). You can also choose the type of the file where your output will be saved in (for example, Word, Excel and HTML) and the output folder. 66

67 Figure 94. Exporting output A third option is to copy and paste individual tables or graphs from SPSS to another software. To do this, right click on one table or graph and select Copy special (Figure 95). Figure 95. Copying output 67

68 You can copy the table or graph as text, image or a worksheet (Figure 96). If you choose to copy as image you can paste the table or graph directly into a text processor (for example, see Figure 97 and all tables in this coursepack). Figure 96. Formats to copy Figure 97. SPSS table saved as image However, more often than not you will want to edit the table, either to change the style or to remove or add information. For example, in Figure 98 we removed the missing values, the percentage column and the information of whether the category was valid or missing. The same is true for graphs. Sometimes other software is more flexible to draw graphs. For example, Figure 99 was created using Excel. Tip: It is usually not appropriate to copy and paste SPSS tables and graphs into papers or research reports. Some formatting is usually required. 68

69 Politics too complicated to understand Frequency Valid Percent Cumulative Percent Never Seldom Occasionally Regularly Frequently Total Figure 98. Excel formatted table Figure 99. Excel formatted graph 69

70 III. Exercises 1. Beginners Exercise 1.1: Create a new data file In this exercise you will create a new data file, define and name variables, code your response categories and enter data (refer to Section II.1). Consider the following questionnaire: 1. What is your gender? Female (1) Male (2) 2. What is your age? Questionnaire 3. Which of these statements comes closest to your beliefs? Don t believe there is any sort of spirit, God or life force (1) Believe there is some sort of spirit or life force (2) Believe there is a God (3) 4. How important is God in your life? Please use this scale to indicate 10 means very important and 1 means not at all important. Use the scale provided Not at all Very important important 5. Apart from weddings or funerals, about how often do you attend religious services? Once a week or more (1) About once a month (2) About each two or three months (3) Only on special holy days (4) About once a year (5) Less often (6) Never or practically never (7) 70

71 This questionnaire has two socio-demographic variables (gender and age) and three questions about religious practice. Questions 1, 3 and 5 are categorical questions and codes are assigned to each category (in brackets). Questions 2 and 4 are numerical. Using the questionnaire and dataset provided: a. Open a new data file b. Save this file in your hard drive or personal space c. Click on Variable view and create six new variables (one for each variable plus an ID number for each participant). Give the variables short names (e.g. attendance ). You can use the label to describe your variable more in detail (e.g. Frequency of attendance of religious services ). d. Use the Values tag to assign codes to the categorical variables e. Click on Data View and enter the data Dataset ID Gender Age Belief in God Importance of God Religious attendance

72 Exercise 1.2: Work with an existing dataset In this exercise you will work with the dataset from the European Social Survey Round 4 for the United Kingdom. You will start by recoding some variables and then obtain descriptive statistics and graphs (refer to Sections II.2, II.4 and II.5). You will finish by saving your outputs to a word processor and editing tables and graphs (refer to Section II.8). a. Open the file ESS4GB.sav b. Spend some time exploring the dataset, variables, value labels and missing values. Familiarise yourself with the data set. c. Recode the variable Age (agea) into a new variable called age_group with the following categories: (15 to 29 years old) (30 to 45 years old) (46 to 59 years old) 60-4 (60 years old ) Tip: Do not forget to define the label of the variable and categories d. Recode the variable Religious attendance (rlgatnd) into a new variable called religious_attendance_2cat with the following categories: (1) Once a week or more (2) Less than once a week Tip: Look at the value labels to determine which codes should be assigned to categories 1 and 2. e. Use Compute to calculate the mean of people s attitudes towards immigration. Call this scale attitudes_immigration. Use the following three items: Immigration bad or good for country's economy ( imbgeco ) Country's cultural life undermined or enriched by immigrants ( imueclt ) Immigrants make country worse or better place to live ( imwbcnt ) f. Obtain descriptive statistics and graphs for Age ( age_group ), Religious attendance ( religious_attendance_2cat ) and Attitudes towards immigration ( attitudes_immigration ). Copy and paste the tables and graphs into a word processor (either directly or using excel) and write a few comments. Tip: Use frequencies and bar charts to describe age and religious attendance (here recoded to be categorical variables) and descriptive and histogram to describe attitudes towards immigration (numerical variable). 72