Scottish Crime and Justice Survey workshop: Analysing the Datasets. 28 th April 2016 University of Edinburgh. Paul Norris Rebecca Pillinger

Transcription

1 UK Data Archive Study Number Scottish Crime and Justice Survey, Scottish Crime and Justice Survey workshop: Analysing the Datasets 28 th April 2016 University of Edinburgh Presented by: Susan McVie Paul Norris Rebecca Pillinger

2 Copyright University of Edinburgh This document has been produced for AQMeN by the presenters listed on the front page. Any material in this document must not be reproduced, published or used for teaching without the permission of the authors. For further information or to contact the authors, please contact

3 Contents 1. Introduction Tips on analysis using SCJS Themes Covered in SCJS SCJS Datasets used in this workbook SCJS Reports Accessing the SCJS Data Practical tips on SPSS set-up Examples of frequencies and cross-tabs via menu... 8 Frequencies... 9 Crosstabs Weighting variables Recoding a variable via menus Computing new variables using menus Practical Examples Using the Respondent Dataset 2010/ What is the estimated number of household and personal crimes that occurred in Scotland during 2010/11? How does experience of victimisation vary by age and sex? To what extent does the Scottish public think that crime is a problem? And are there other issues that are considered more problematic? What attitudes do the Scottish public have towards community sentences? How common are credit card fraud and identity theft? Questions for you to explore Practical Examples Using the Self-completion Dataset 2010/ What percentage of the Scottish population has taken drugs, and does this vary by age and sex? i

4 4.2 Does the likelihood of sexual victimisation vary by age? Do gender and age predict the likelihood of experiencing partner abuse? Questions for you to explore Practical Examples Using the Victim Form Dataset 2010/ What types of crime are reported to the police? How does reporting crime impact on service use? Relationship to offender and emotions felt Questions for you to explore... 96

5 1. Introduction This workbook is intended to guide you through a series of practical exercises using data from the Scottish Crime and Justice Survey (SCJS). There are four main sections to this workbook. The first section provides some guidance on data management using SPSS, illustrated with examples from the SCJS. The next three will introduce you to the datasets produced from the SCJS: the main respondent file; the self-completion file; and the victim form file. A second workbook is also available which provides detailed guidance on how to merge the different datasets together, including examples of aggregating variables. Each of the sections provides step by step guidance on using the SCJS, including detailed instructions on using SPSS windows and syntax; and provides helpful hints and tips on how to conduct the types of analysis that are contained in the published survey reports. We will focus mainly on the most recent survey, conducted in 2010/11; however, we recommend that you explore different datasets in order to understand how crime has changed over time. Although this workbook covers a range of different exercises using SPSS, it is not intended to be a training manual for any one specific technique. If you require further information on statistical training, please refer to the AQMeN website at Here you will find information about our forthcoming training courses, and you can also request copies of training manuals for the courses we have run in the past ( If you require further information, you should contact us at info@aqmen.ac.uk. 1.1 Tips on analysis using SCJS When conducting any analysis using SCJS (or, indeed, any dataset) it is necessary to first get to grips with the data that is available. The most useful sources for this are the SCJS User Guide , the SCJS Questionnaire 1 and the various Technical Reports for the Scottish Crime and Justice Survey 2. The User Guide 3 is the recommended starting point for your analysis, providing accessible information about the background and methodology of SCJS. If you require further information, or wish to check that information from the User Guide is still current, the SCJS Technical Report for each year provides detailed information about the background, structure, and questionnaire of the survey, among other topics. Especially useful is the detailed information provided about the weights used in SCJS. Finally, when undertaking your own analysis, it is important to carefully examine the questions asked in the SCJS survey to determine precisely what the variables listed in SCJS are measuring. 1 The version of which can be accessed at Justice/Publications/publications/1011Quest 2 The version of can be accessed at Justice/Publications/publications/SCJStechreport Available at 3

6 1.2 Themes Covered in SCJS The SCJS 2010/11 contains data on the following 4 : Main questionnaire (13,010 respondents): Section 1: General views on crime and social issues; Section 2: Victim form screener. Victim form (Section 3) (completed by 2,568 respondents): Repeated up to five times per victim, based on information collected in the victim form screener section: Incident dates; Incident details; Experience of criminal justice system and related issues (emotions, support and advice, perceptions of the incident, police contact, offender(s) prosecution, information and assistance, Procurator Fiscal, attitudes towards offender prosecution and sentencing); Incident summary. Full sample modules (Section 4) (13,010 respondents): Community sentencing; Local community; Scottish criminal justice system. Quarter-sample modules (That is, the following sections are only asked of one quarter of the sample) (Section 5) (approx. 3,250 respondents per module): Module A: Fear of crime; Module B: Police (visibility, attitudes towards and stopped by police); Road safety cameras; Module C: Fraud (card fraud and identity theft); Civil law; Module D: Civil law; Procurator Fiscal. Main questionnaire continued (13,010 respondents): Section 6: Demographics (newspaper readership, tenure and accommodation type, marital status, work status and employment details, health status, ethnicity and religion and income). 4 Adapted from 2010/11 SCJS Technical Report, page 20 4

7 Self-completion questionnaire (completed by 10,999 respondents): Section 7: Illicit drug use; Section 8: Stalking, harassment and partner abuse; Section 9: Sexual victimisation. 1.3 SCJS Datasets used in this workbook SCJS datasets can be accessed from the UK Data Archive (as described below). Due to licensing restrictions, it is not possible for us to provide copies of the data to take away from this workshop; however, you will be provided with a number of datasets to work on during the course of the practical session, and you can retain your output and any syntax files that you create. The datasets provided here are: scjs_s4_ _rf_ukda_ sav SCJS 2010/11 main respondent data file scjs_s4_ _sc_ukda_ sav SCJS 2010/11 self-completion data file scjs_s4_ _vf_ukda_ sav SCJS 2010/11 victim form data file Please note that a condition of participating in the workshop is that you may use these datafiles for the practical exercises, but you must not copy the data onto a memory stick or in any way take it with you from the computer laboratory. You must also sign a Teaching Agreement form that will be submitted to the UK Data Archive. 1.4 SCJS Reports A number of reports about the findings of SCJS are compiled for each sweep of the survey. Reports are released for main findings, partner abuse, sexual victimization and stalking and drug use. These reports can be accessed from As an example, the 2010/11 main report covers the extent of crime in Scotland, risk and characteristics of crime, impact and perceptions of crime, reporting crime and support for victims, public perceptions of crime, the public and the police and Scottish justice systems and organizations. When conducting your own analysis, it may be worth investigating these existing reports to see how the SCJS has been used to examine any topics which may be of interest to you. 1.5 Accessing the SCJS Data SCJS is hosted by the UK Data Service, which contains datasets from 1993 (when SCJS was the Scottish Crime Survey) up to the 2010/11 sweep of SCJS. To access the datsets, go to and search for SCJS in the Search our data catalogue and related resources field. From the results produced, select the version of the dataset you wish to download, and click the Download/Order button. (Note that to download the data, you need to be registered with the UK Data Archive. Details on how to register can be found at Data Management in SPSS This section of the workshop provides some practical hints and tips on setting up SPSS to analyse the SCJS data, and how to conduct simple analysis such as frequencies and cross tabulations. It also provides an introduction to the weights required for analysis of 5

8 the Survey data, and how to recode existing variables and compute new variables. You should find these practical tips useful regardless of the dataset you are using. 2.1 Practical tips on SPSS set-up In the menu at the top of the SPSS window click on Edit / Options : The Options dialog box opens at the General tab, as shown in the screenshot below. There a number of useful things you can do here to help you with your analysis. If you would rather see variables displayed in lists according to their variable name rather than their label, select the option Display names in the Variable Lists. The default option is to list variables in the order they were entered into the dataset. If you wish, you can change this to list the variables alphabetically by clicking the Alphabetical button. [It is recommended that for this workshop we leave the data ordered by File.] If you want a new syntax window to open by default each time you open SPSS, tick the box at the bottom left (it is unticked by default). 6

9 You can also change whether SPSS displays variable names or labels by right-clicking in any box which displays variables, as shown below: In the Viewer tab make sure that Display commands in the log at the bottom left is ticked. If you run any syntax commands, it will be displayed in the output screen which opens up automatically when a dataset is opened. This can help spot any errors in the syntax. 7

10 In the Output Labels tab select the names and labels options in the drop down lists, as this will ensure that all your output includes a clear name and label for tables and graphs: 2.2 Examples of frequencies and cross-tabs via menu SPSS allows users to analyse data via a menus or via syntax (text commands). When working with SPSS it is strongly advised to use syntax while conducting your analysis, as this allows you to keep an accurate record of all the analysis you do (essential if you 8

11 want to come back to the analysis at a later date). This is essential for replication and alteration of your work (if necessary), and is incredibly useful in spotting any errors that might have been made. The menus in SPSS can be used in the first instance to set up your analysis, and then the commands pasted into the syntax window. The following section shows how to perform some basic analysis using menus and how to paste the commands into the syntax window. These examples use the 2010/11 Self-completion data file (scjs_s4_ _sc_ukda_ sav). Frequencies Select Analyze in the menu, then select Descriptive statistics Frequencies in the Analyze drop-down menu. The dialog box below will appear. Browse the list of variables on the left and highlight the one(s) you want to analyse and click on the arrow in the middle. For this example we ll use QDGEN: Respondent s Gender [qdgen] variable in the self-completion dataset. This variable provides information about the gender of each person who completed the Self-Completion Form of the SCJS. Either scroll down the list of variables to select this variable, or change the variable list to display variable names (see above) and type the variable name (qdgen) to search through the list. 9

12 Click Paste. The command for a frequency analysis of qdgen will be pasted int a syntax file, as shown below. Highlight the command and either click the run button (green arrow in the tool bar) or press CTRL-R. This the following output will be produced. Note that we would have produced the same output if we had pressed OK in the Frequencies window. Now, however, if we want to run a frequency table for a different variable we can just amend the variable name in the syntax which we pasted in the syntax menu, rather than reproducing every step with the menus. Whilst this may not be especially useful for simple operations, for more complex commands it is very helpful and saves a lot of time! 10

13 Crosstabs To produce cross-tabs, select Crosstabs from the Descriptive statistics menu in the Analyse toolbar: This will produce the following window. As before, browse and highlight the variables you want to cross-reference and move them to the row and column boxes as applicable. Typically the dependent variable(s) goes in the rows, and the independent variable(s) goes in the column. In this example we ll use the variables QEVE_ANY: Whether 11

14 respondent has ever taken ANY of listed drugs and the qdgen variable from before. We ll assume that QEVE_ANY is the dependent variable, and that gender may have an effect on drug usage rather than drug usage impacting a person s gender. Remember that these exercises are just intended to demonstrate how to perform these tasks via SPSS menus, and so the selection of variables here has been made arbitrarily. Crosstabs allow us to present the data in multiple ways. We may want, for example, to examine the percentages of responses within particular categories. To do this, press the Cells button. The window below will be presented. Select the Columns option in the Percentages box and then press Continue. This will add to the crosstab the percentages of men and women who fall into each of the drug use categories. After pressing Continue, we are taken back to the Crosstabs window. We can also use crosstabs to examine whether a relationship is statistically significant. The appropriate measure to use depends on the nature of the variables you are analyzing. For this example we will just show how to get SPSS to show us the Chisquared statistic using menus. 12

15 First, select the Statistics button; then select the Chi-squared option and then press Continue, as shown below. This will return you to the Crosstabs window. Click Paste to get the following syntax: CROSSTABS /TABLES=qeve_any BY qdgen /FORMAT=AVALUE TABLES /STATISTICS=CHISQ /CELLS=COUNT COLUMN /COUNT ROUND CELL. 13

16 Note that if we had not selected the column percentages and the Chi-squared statistic, the syntax would be: CROSSTABS /TABLES=qeve_any BY qdgen /FORMAT=AVALUE TABLES /CELLS=COUNT /COUNT ROUND CELL. Run the command pasted into the syntax window either using the Run Selection button or using Ctrl+R as described above. The following tables will appear in the Output window. QEVE_ANY: Whether respondent has ever taken ANY of listed drugs * QDGEN: Respondent's Gender Case Processing Summary Cases Valid Missing Total N Percent N Percent N Percent % 22.2% % QEVE_ANY: Whether respondent has ever taken ANY of listed drugs * QDGEN: Respondent's Gender Crosstabulation QDGEN: Respondent's Gender Male Female Total QEVE_ANY: Whether Yes Count respondent has ever taken % within QDGEN: 25.8% 18.1% 21.4% ANY of listed drugs Respondent's Gender No/DK/RF Count % within QDGEN: 74.2% 81.9% 78.6% Respondent's Gender Total Count % within QDGEN: Respondent's Gender 100.0% 100.0% 100.0% Chi-Square Tests Asymp. Sig. (2- Value df sided) Pearson Chi-Square a Continuity Correction b Likelihood Ratio Exact Sig. (2- sided) Exact Sig. (1- sided) Fisher's Exact Test Linear-by-Linear Association N of Valid Cases a. 0 cells (.0%) have expected count less than 5. The minimum expected count is b. Computed only for a 2x2 table As this section is intended only to show how to produce common functions in SPSS via menus, we will not discuss the interpretation of the Chi-Squared Tests here. This is discussed in the exercises since Chi-squared, and various other statistical tests, are used to analyse the SCJS data. 14

17 2.3 Weighting variables The SCJS contains a number of weighting variables which must be applied when undertaking analysis. The further information about weighting and the weights necessary can be found in the SCJS User Guide in Sections (pages 18-19) and 2.6 (pages 34-37), and the 2010/11 Technical Report pages Before undertaking your own analysis, you should consult this material in order to understand fully the impact of different weights. For the purpose of this workshop the appropriate weight to be used will be listed with each example. These are selected based on the following rules: Variable name Data file Description of weight WGTGHHD Respondent file (RF)& Grossed household weight Victim Form file (VFF) WGTGINDIV RF & VFF Grossed individual weight WGTGINC_SCJS VFF Grossed incident weight for SCJS crimes WGTGHHD_SC Self-Completion file (SCF) Grossed self-completion household weight WGTGINDIV_SC SCF Grossed self-completion individual weight WGTGHHD_SCALE RF & VFF Scaled household weight WGTGINDIV_SCALE RF & VFF Scaled individual weight WGTGHHD_SC_SCALE SCF Scaled self-completion household weight WGTGINDIV_SC_SCALE SCF Scaled self-completion incident weight Source: Reproduced from SCJS User Guide page 34. To apply a weight via the menus in SPSS, select the Weight Cases option from the Data menu: 15

18 This will bring up the window shown below. Select the appropriate weighting variable from the list on the left hand side. For this example, we will use WGTGHHD_SC, the Grossed self-completion household weight. Click the button next to Weight cases by, click on the weighting variable and then use the arrow button to select the weight. Once you have selected the weighting variable, press Paste. 16

19 This will produce the following syntax: WEIGHT BY WGTGHHD_SC_SCALE. Run this command in the syntax window either using the Run Selection button or Ctrl+R. Once the weight has been applied, you will see a notification in the bottom right of the SPSS DataSet window. To turn off a weight that you have previously applied, open the Weight Cases window in the Data menu as described above, and select the Do not weight cases option: and then click Paste. This will give the following syntax: WEIGHT off. 17

20 In addition, you will see that there is no indicator in the SPSS dataset window saying that a weighting variable is applied: 2.4 Recoding a variable via menus This example uses the 2010/11 Respondent form dataset (scjs_s4_ _rf_ukda_ sav). During this session there are a number of occasions where data requires recoding before it is analysed. Recoding variables is a particularly good example of why analysis using syntax is preferred to relying on SPSS menus, as using syntax provides a record of which variables have been included in the transformation. As such, throughout the session most recoding will be done via syntax. However, if you do wish to recode a variable using the menus the process is as follows. This example uses the SCJS Respondent Data file, but the process is the same for any dataset. In this example we want to recode five variables which relate to questions that respondents were asked about their attitudes towards community sentences (this recoding forms part of exercise 3,4 later). The questions with suffix 02, 03, 04 and 07 all express positive views about the effects of community sentences but the question with suffix 05 expresses a negative view.. The statements that respondents were asked to agree or disagree with are: QDISATT_02 Drug users need treatment not prison QDISATT_03 Community sentencing is an effective way of dealing with less serious crime QDISATT_04 Learning new skills during community sentences stops criminals from committing more crimes QDISATT_05 Community sentences do not punish criminals enough QDISATT_07 Criminals who complete their community have paid back to their community for the harm they have caused Each variable is coded from 1, meaning agree strongly, to 5, meaning disagree strongly. To combine these attitudes into scale we want to recode the questions so that large values for each question represent positive attitudes towards community sentences and low values represent a negative attitude. To do this, we need to reverse the direction of the answers for the positive questions, responses to which at present are coded 1-5, to be coded 4-0. This will allow them to be combined into a in which higher values mean more positive views towards community sentences. We will then recode the negative question, so that the values 1-5 run 0-4. The five items can then be added together as they all run in the same direction. Setting the lowest value at zero makes the investigation of the mean values of the new scale variable more meaningful. Go to the Transform menu and click on Recode into Different Variable (note that this is not the Recode into Same Variables option; recoding into the same variables overwrites the original data. This means that if you make a mistake, or in the future want to use the original variable you will not be able to. It is almost never a good idea to use the Recode into Same Variables option!). 18

21 This will produce the recode window, shown below. Select the first variable we want to recode, in this case QDISATT_02 from the menu on the left hand side and then press the arrow button: The variable will appear in the Numeric Variable -> Output Variable box. Click on the Old and New Values button. As stated above, we want to change the direction of this variable, so that 5 becomes 0, 4 becomes 1 and so on. To do this, first enter 5 in the Value: field in the Old Value box and 0 in the Value: field in the New Value box. 19

22 Click the Add button, and this change will appear in the Old --> New field. Follow the same process for the rest of the values for this variable, changing 4 to 1, 3 to 2, 2 to 3 and 0 to 4. Once you have done this, click the All other values option at the bottom of the Old Value box, and select the System-missing option from the New Value box and then press Add as before. The Old and New Values window should then look as follows: Click Continue to return to the Recode into Different Variables window. Click on the qdisatt_02 variable in the Numeric Variable -> Output Variable field and it will highlight in yellow. Click in the Name: field of the Output Variable box and enter the name for the new variable. We will call this CSATT2. In the Label: field, type Recoded Community sentencing is an effective way of dealing with less serious crime, and then click the Change button: 20

23 This will tell SPSS the name and label of the new variable we want to create. Click Paste and the following commands will appear in the Syntax window: RECODE qdisatt_02 (5=0) (4=1) (3=2) (2=3) (1=4) (ELSE=SYSMIS) INTO csatt2. VARIABLE LABELS csatt2 'Recoded Drug users need treatment not prison'. EXECUTE. We can see that a new variable has been created by looking in Variable View in the main SPSS window and scrolling down to the bottom of the list of variables. The newly created csatt2 variable is the last variable on the list. We can now repeat the process for the other four variables we want to recode, remembering that for csatt3, 4 and 7 we want to use the same variable coding as for csatt2, and for csatt5 we want to reverse the coding. The syntax for this operation as a whole is: 21

24 recode QDISATT_02 (5=0) (4=1) (3=2) (2=3) (1=4) (else=sysmis) into csatt2. recode QDISATT_03 (5=0) (4=1) (3=2) (2=3) (1=4) (else=sysmis) into csatt3. recode QDISATT_04 (5=0) (4=1) (3=2) (2=3) (1=4) (else=sysmis) into csatt4. recode QDISATT_05 (5=4) (4=3) (3=2) (2=1) (1=0) (else=sysmis) into csatt5. recode QDISATT_07 (5=0) (4=1) (3=2) (2=3) (1=4) (else=sysmis) into csatt7. VARIABLE LABELS csatt2 'Recoded Drug users need treatment not prison'. VARIABLE LABELS csatt3 'Recoded Community sentencing is an effective way of dealing with less serious crime. VARIABLE LABELS csatt4 'Recoded Learning new skills during community sentences stops criminals from committing more crimes. VARIABLE LABELS csatt5 'Recoded Community sentences do not punish criminals enough. VARIABLE LABELS csatt7 'Recoded Criminals who complete their community have paid back to their community for the harm they have caused. EXECUTE. 2.5 Computing new variables using menus The second step of this process uses the Compute menu to create a new variable from the multiple variables we have just created. When doing this for your own analysis, you would need to check the consistency of the variables using Cronbach s Alpha, and also your theoretical knowledge of the concepts being measured, to see whether it makes sense to combine them together (see exercise 3.4). As this example is just to demonstrate how to perform these tasks using menus, we will carry on and compute the new variable without performing these tests. To do this, select Compute from the Transform menu. This brings up the Compute window. First, we want to enter the name of our new variable in the Target Variable: field. In this example, we will call the variable csatt_sc (see screenshot below). Next, we want to add together each of the recoded variables to form a single scale. (NB This is just one of the many functions which can be used to compute new variables. As the point of this exercise is just to show the process required, we will not provide an overview of all these different options). To do this, click on the first of these variables (which will be the last four variables in the list on the left hand side) to highlight it, and then press the arrow button. This will place the variable in the Numeric Expression: field. Because we want to add the four variables together, click on the + button from the keyboard in the middle of the window. This will add a + to the numeric expression (it is also possible to do this by typing + into the Numeric Expression field manually). Add the three other recoded variables in the same way (without a + after the last variable) until the Numeric Expression window reads as follows: 22

25 Click Paste and the following command will appear in the Syntax window: COMPUTE csatt_sc=csatt2 + csatt3 + csatt4 + csatt5 + csatt7. VARIABLE LABELS csatt_sc Scale of attitudes towards community sentencing (negative to positive). Run this command from the syntax window and the csatt_sc variable will appear at the bottom of the dataset. 23

26 A further advantage of using syntax to compute and recode variables is that complicated processes can be undertaken much quicker than they can using menus. For that reason, in addition to the benefits syntax affords in terms of error checking, as we go through the exercises we will not provide full details of how to create every new variable that is computed via the menus. The following sections of this workbook contain example exercises from each of the three SCJS datasets, and a final set of exercises that demonstrates how to merge data from the different datasets together. It is recommended that if you are an inexperienced analyst, you follow each of the exercises step by step; whereas, if you are an experienced analyst you can dip in and out of the exercises as you wish. 24

27 2. Practical Examples Using the Respondent Dataset 2010/11 In this section we will explore five different types of questions using data from the Scottish Crime and Justice Survey 2010/11 main respondent data file. To conduct this analysis you should use the dataset titled SCJS_S4_ _RF_UKDA_ What is the estimated number of household and personal crimes that occurred in Scotland during 2010/11? One of the main purposes of the SCJS is to calculate an estimate of the number of crimes that occur in Scotland during a one year period. This can be done very simply by asking for a frequency command that includes a weighted sum. The variables used to calculate national estimates are victimisation variables that are derived from the 2010/11 victim form file but which are aggregated and saved into the respondent data file. Those that measure the incidence of each type of crime (and have the suffix INC in the dataset) are included in this analysis. It is important to remember that household and personal crimes need to be calculated separately, as there are different weights that apply. For household crimes, the grossing weight WGTGHHD should be used as this provides an estimate of the number of crimes against all private households in Scotland; while for personal crimes, the grossing weight WGTGINDIV should be used as this provides an estimate of the number of crimes committed against all individuals age 16 or over in Scotland. (1) First, the weights need to be applied. Start by going to the data menu, then click on weight cases to get the dialogue box below. Click on weight cases by and search for the appropriate weighting variable in the list of variables on the left. The weight shown here WGTGHHD is the grossing weight for household crimes. The syntax produced by clicking on paste should look like this: weight by WGTGHHD. 25

28 Then run a normal frequency analysis of the household crime incidence variables. Go to the analyze menu and click on frequencies then select from the variable list those household crimes that you wish to analyse, making sure to select those starting with the prefix inc. Then click on statistics and choose the sum option under central tendency. This will measure the estimated number of households that experienced each type of household crime. Note also that we have de-selected the option Display frequency tables as we do not need individual tables for each variable we just need the table that provides the grossed estimates. After clicking on the paste option, you should get the following syntax: FREQUENCIES VARIABLES=incvand incacquis inchousebreak inctheftofmv incbicycletheft /STATISTICS=SUM /FORMAT=NOTABLE /ORDER=ANALYSIS. Looking at the output below, the sum value shows the national estimate for the household crimes. As you can see, there were an estimated 275,387 incidents of vandalism against private households in Scotland during 2010/11. The estimated number of acquisitive crimes (which includes housebreaking, motor vehicle thefts and bicycle thefts) was only 60,751 in the same year. 26

29 (2) To do the same for the personal crimes violence, assault and robbery, you use very similar syntax, remembering to apply the individual grossing weight: weight by WGTGINDIV. FREQUENCIES VARIABLES= incviolent incassault incrob /STATISTICS=SUM /FORMAT=NOTABLE /ORDER=ANALYSIS. As before, the sum value in the output shows that there were an estimated 220,136 incidents of violent crime committed against Scottish adults aged 16 or over in 2010/11. This is the total number of incidents of assault and robbery added together. 3.2 How does experience of victimisation vary by age and sex? It is possible to explore the extent to which victimisation varies by age and sex using both the incidence and prevalence variables for victimisation types in the respondent data file, and the variables QDGEN (for gender) and QDAGE (for age). There is also a derived age variable QDAGE2 which is banded into 10 broad age groups. The analysis below uses the variables PREVPERSON and INCPERSON, which relate to the prevalence and incidence of all personal crimes (i.e. violence and personal theft). There are a number of possible ways to conduct this analysis, so a range of syntax is provided. (1) Start by weighting the data using the scaled individual weight WGTGINDIV_SCALE and ask for a simple frequency of the prevalence variable PREVPERSON and the incidence variable INCPERSON simply to observe the variables. You do not need to ask 27

30 for the sum option this time as you are not analysing national estimates. The syntax needed to run this analysis is: weight by WGTGINDIV_SCALE. fre PREVPERSON INCPERSON. The frequency table for PREVPERSON shows that 5.1% of respondents to the SCJS 2010/11 were victims of a personal crime. The frequency table for INCPERSON shows that 94.9% of people had no incidents of personal crime (as expected having observed the frequency table for PREVPERSON). Of the remaining 5.1% of respondents, most of them had experienced only one incident of personal crime in the reference year; but a very small proportion of respondents had experienced more than one. As we are really only interested in those who experienced one or more incidents, we can set the zeros to missing for this variable (see step 2). (2) Set the incidence value for those who had not experienced any personal crimes to missing, so as to restrict analysis of incidence to victims only (this is not essential, but it is more useful to look at incidence amongst victims only). This can be done using the syntax: Missing values INCPERSON (0). fre INCPERSON. 28

31 The output shows that the majority (71.8%) of victims of personal crime experienced only one incident, although a small minority (4.4%) of individuals were victims of five or more incidents of personal crime. Since this is straightforward count data, you could also ask for a mean of incidence of personal crime using the descriptives commands; however, it is bound to be very close to 1. (3) To explore differences in prevalence by gender, run a cross-tabulation with the variables PREVPERSON and QDGEN. This can be done by going to the Analyze menu and selecting Crosstabs then entering the two variables into the dialogue boxes, as shown below. In this case you would request column percentages in the cells box, as you are interested in the percentage of males and females that were victims of personal crime. 29

32 After clicking on paste, you should get the following syntax: CROSSTABS /TABLES=prevperson BY qdgen /FORMAT=AVALUE TABLES /CELLS=COUNT COLUMN /COUNT ROUND CELL. The resulting table shows that 6.0% of males were victims of a personal crime during 2010/11 compared to 4.3% of females. A chi-square test (not shown) revealed that the gender difference in prevalence was statistically significant (p<.001). (4) To explore differences in prevalence by age, you can use various methods. The SCJS contains two age variables: QDAGE which is a continuous measure of age in years; and QDAGE2 which is age banded into categories. Using QDAGE2, you could run a simple cross-tabulation (similar to the above) to find the difference in age bands between victims and non-victims. This can be done using the following syntax (simply replace QDGEN from the previous analysis with QDAGE2): CROSSTABS /TABLES=prevperson BY qdage2 /FORMAT=AVALUE TABLES /CELLS=COUNT COLUMN /COUNT ROUND CELL. The output for this analysis shows that younger people (especially those between 16 and 24) were most likely to have been victims of personal crime; whereas those in the older age bands, especially over 60, were least likely to have experienced this type of crime. This is useful information if you are simply interested in broad age bands, but you may want to find the mean or median age of victims (see step 5). 30

33 (5) Using the QDAGE variable, you can examine the mean and median age of those who were victims of personal crime compared to those who were not. This can be done by going to the Analyze menu, and clicking on compare means, then selecting means. The screenshot below shows the dialogue boxes, and highlights the various options that can be selected including requesting both mean and median, and asking for an Anova table which will tell you whether the difference in mean age between victims and nonvictims is statistically significant. This analysis will produce the following syntax: MEANS TABLES=qdage BY prevperson /CELLS MEAN COUNT STDDEV MEDIAN /STATISTICS ANOVA. 31

34 The output below shows that there is a difference in the mean age of victims and nonvictims. On average, victims of personal crime were 34 years old, which compares to a mean age of 48 for those who were not victims. The median ages are, similarly, very different. The Anova table, shown below, reveals that the mean age for the two groups is significantly different. (6) The difference in the median age of victims and non-victims can be demonstrated graphically using a boxplot. This can be done by going to the Graphs menu, selecting Legacy dialogs, then boxplot and selecting a simple chart. The dialogue box below shows how to enter the variable names. 32

35 Clicking on the paste box produces the following syntax: EXAMINE VARIABLES=qdage BY prevperson /PLOT=BOXPLOT /STATISTICS=NONE /NOTOTAL. The boxplot below shows the median age and interquartile ranges for those who were and were not victims of personal crime. As we might have expected, the range for the bottom quartile of victims is very narrow (i.e. most victims in the bottom quartile are around the same age); whereas, there is a much greater spread in the top quartile (i.e. the ages of the older victims range over a wider number of years). Thus, the age distribution for victims is very unsymmetrical. There are also a large number of outliers for the victim boxplot. The non-victim boxplot is more evenly distributed around the median. Thus, victims are disproportionately represented amongst the younger people in the sample. (7) Finally, it is interesting to look at an error bar which compares the mean age for victims and non-victims. You can also break this down further to include a comparison of males and females. This is done by going to the Graphs menu and selecting Legacy dialogs, then error bar and selecting the clustered option. Enter the variables as shown below. 33

36 The syntax produced by clicking paste is as follows: GRAPH /ERRORBAR(CI 95)=qdage BY prevperson BY qdgen. The error bar graph below shows that the mean age for male victims of personal crime is lower than the mean age for females. However, the 95% confidence intervals overlap, which means the difference in average age between male and female victims is not statistically significant. As expected, the average age of male and female non-victims is much higher than for victims. This time there is no overlap in the 95% confidence intervals for male and female non-victims, which means that male non-victims are significantly younger on average compared to female non-victims. 34

37 3.3 To what extent does the Scottish public think that crime is a problem? And are there other issues that are considered more problematic? The SCJS questionnaire begins with some warm up questions about the extent to which respondents consider certain types of social issue a problem. Crime is listed amongst a number of other issues, including unemployment, standards of health care, alcohol abuse, racial discrimination and drug abuse. These questions are each designed to be analysed individually, using simple frequency analysis. However, they can also be analysed together, for example using the multiple response command. This example starts by looking individually at the crime variable (QSPR_4); and then examines this option in the context of the other social problems listed in the survey. (1) Start by checking your variable(s) of interest. A quick inspection of QSPR_4 in the variable view window of SPSS reveals that the refused (-2) and don t know (-1) options are not set to missing. You may wish to consider whether it is worthwhile excluding these cases by first running a simple frequency analysis, remembering to switch the weighting off. The following syntax can be used: weight off. FREQUENCIES VARIABLES=qspr_04 /ORDER=ANALYSIS. 35

38 Looking at the output below, there were no respondents who refused to answer the question and only a small number of the total 13,010 respondents who said they didn t know. Therefore, they could reasonably be excluded from the remaining analysis. The simplest way of exclude them from the analysis is to use the missing values command, which can be done using the following syntax: missing values QSPR_4 (-2,-1). You should then check the distribution of this variable by running a frequency analysis, this time remembering to include the weighting. As this is an attitudinal question, the scaled individual weight WGTGINDIV_SCALE should be applied prior to running the analysis: weight by WGTGINDIV_SCALE. FREQUENCIES VARIABLES=qspr_04 /ORDER=ANALYSIS. The resulting output shows that almost half of all respondents thought that crime in Scotland was a big problem, while a further 46% thought it was a bit of a problem. Less than 5% of respondents thought that crime was not a problem in Scotland today. (2) To identify whether there are other social issues that the public think are more problematic than crime, we could run frequencies for each of the 10 questions that ask about social problems (starting qspr ) and compare the individual tables. (Remember to start by ensuring that the don t know and refused responses to all of the qspr variables are set to missing first). 36

39 However, a simpler way to conduct this analysis would be to run a multiple response command which allows us to analyse the responses to all 10 items together. This can be done by going to the Analyze menu and selecting Multiple Response (see dialogue box below). You start with define variables sets and add each of the variables you want to include in the analysis into the variables in set box (this is all 10 variables that start qspr ). Select dichotomies and indicate that you wish to analyse the counted values for those who gave the response a big problem (which has the value 1) to any question. Give your new variable a name and label, then click on add to include it in the multiple response sets. You can create as many multiple response sets as you like, but they are not saved to the dataset; therefore, it is important to save your syntax for doing later analysis. Once you have created your response sets, click the close button, then go back to the Analyze menu and select Multiple Response again. You will see that this time you are permitted to select either frequencies or crosstabs, as you have valid response sets waiting to be analysed. Click on frequencies and you will see the following dialogue box. Add your response set $qspr into the table(s) for box, then click on paste. 37

40 Once you have clicked on paste, the following syntax is produced: MULT RESPONSE GROUPS=$qspr 'A big problem in Scotland today' (qspr_01 qspr_02 qspr_03 qspr_04 qspr_05 qspr_06 qspr_07 qspr_08 qspr_09 qspr_10 (1)) /FREQUENCIES=$qspr. The output, presented below, shows that the multiple response command presents the results in two ways: firstly, in terms of the percentage of responses; and secondly, in terms of the percentage of cases. The percentage of responses tells us that of all the answers received across the 10 questions in which the response a big problem was given, what percentage can be attributed to each individual item. Thus, looking at all the big problems in Scotland identified by respondents, the social problem considered the biggest is drug abuse (19.6% of all responses), followed by alcohol abuse (18.8%) and then unemployment (17.9%). Crime is the fourth most commonly reported social issue as a big problem in Scotland, at 12.5% of all responses given. The percentage of cases tells us about the percentage of all those who answered each individual question who noted that this issue was a big problem rather than just a bit of a problem or no problem at all. This confirms that drug abuse was considered the most problematic issue in Scotland today, with 80.9% of respondents reporting this to be a big problem. This was closely followed by alcohol abuse (77.7% stating this to be a big problem) and unemployment (73.9% a big problem). In fact, only just over half (51.8%) of all respondents reported that crime was a big problem in Scotland today. The result is a little different from the original frequency analysis because some cases with missing data in one or more of the 10 items have been excluded. To calculate significant differences between responses to different questions, this would have to be done individually using cross-tabulations. 38

41 3.4 What attitudes do the Scottish public have towards community sentences? There is a bank of questions on community sentences in the full sample module, which is asked just after the screener questions for victimisation. Within this bank of questions, the respondents are read out five statements that other people have made about community sentencing in general and asked how much they agree or disagree with these statements (a 5 point scale ranging from agree strongly to disagree strongly). The variable names and statements are as follows: QDISATT_02 Drug users need treatment not prison QDISATT_03 Community sentencing is an effective way of dealing with less serious crime 39

42 QDISATT_04 Learning new skills during community sentences stops criminals from committing more crimes QDISATT_05 Community sentences do not punish criminals enough QDISATT_07 Criminals who complete their community have paid back to their community for the harm they have caused (1) Start by analysing the variables individually using a simple frequency analysis. weight off. FREQUENCIES VARIABLES=QDISATT_02 QDISATT_03 QDISATT_04 QDISATT_05 QDISATT_07 /ORDER=ANALYSIS. Looking at the question wording, you can see that four of the items are worded in a positive way (QDISATT_2, _3, _4 and _7), while the remaining one (QDISSAT_5) is worded in a negative way. Before conducting further analysis with these variables (such as grouping them together into a scale), all of the items have to be coded in the same way (i.e. running from negative to positive or vice versa). Recode the variables so that they are all ordered in the same way (from negative to positive in this example) and to get rid of the irrelevant responses (refused and don t know). The following syntax will achieve this purpose (including variable names and labels): recode QDISATT_02 (5=0) (4=1) (3=2) (2=3) (1=4) (else=sysmis) into csatt2. recode QDISATT_03 (5=0) (4=1) (3=2) (2=3) (1=4) (else=sysmis) into csatt3. recode QDISATT_04 (5=0) (4=1) (3=2) (2=3) (1=4) (else=sysmis) into csatt4. recode QDISATT_05 (5=4) (4=3) (3=2) (2=1) (1=0) (else=sysmis) into csatt5. recode QDISATT_07 (5=0) (4=1) (3=2) (2=3) (1=4) (else=sysmis) into csatt7. VARIABLE LABELS csatt2 'Recoded Drug users need treatment not prison'. VARIABLE LABELS csatt3 'Recoded Community sentencing is an effective way of dealing with less serious crime. VARIABLE LABELS csatt4 'Recoded Learning new skills during community sentences stops criminals from committing more crimes. VARIABLE LABELS csatt5 'Recoded Community sentences do not punish criminals enough. VARIABLE LABELS csatt7 'Recoded Criminals who complete their community have paid back to their community for the harm they have caused. EXECUTE. You will see here that the values have been changed slightly. Instead of having values 1 to 5, the new variables will have values of 0 to 4. This does not make a great deal of difference, but it means your scale runs from 0 which may make it more meaningful to run a mean for example. Note also that the recode has created five new variables (CSATT2 to CSATT7), so we have not altered the original variables at all. (2) Once a set of scale measures running in the same direction have been created, a simple method of testing the reliability of grouping the five measures into one overarching scale exists. This is found by going to the Analyze menu and selecting Scale, then Reliability analysis. This option performs analysis to determine whether the five items are closely correlated enough that they may be single items that contribute towards an overarching latent variable. The dialogue box below shows how the variables are entered. 40

43 Note that you can also include a scale label, which in this case we have called CSATT. By clicking on the statistics button you can also choose a range of options that help you determine how well the five variables correlate together. Click on the paste button to get the following syntax. Note that as this is attitudinal data, the scaled individual weight should be applied. weight BY WGTGINDIV_SCALE. RELIABILITY /VARIABLES=CSATT2 CSATT3 CSATT4 CSATT5 CSATT7 /SCALE('CSATT') ALL /MODEL=ALPHA /STATISTICS=DESCRIPTIVE SCALE CORR ANOVA /SUMMARY=MEANS. (2) The reliability analysis produces a standardised Cronbach s Alpha value for the new scale. Cronbach s alpha is a coefficient of reliability (ranging from 0 to 1) that is commonly used to measure internal consistency or reliability of a set of items, for example during psychometric testing. A high value might indicate that the items collectively represent an underlying latent trait. In this case, we have a Cronbach s alpha value of 0.674, which indicates that the variables have reasonably high internal consistency (although many social scientists would prefer this value to be above 0.700). This could be considered just good enough to consider constructing a singular scale (although you should also consider more complex forms of analysis such as factor analysis, principal components analysis or structural equation modelling). 41

44 The inter-item correlation matrix shows that none of the item pairings have a particularly high correlation, although this is not really surprising given that the scales have only five points. CSATT3 and CSATT7 appear to be the most highly correlated (with an r value of.402). Those are the questions that asked whether the respondents agreed that community sentencing is an effective way of dealing with less serious crime and criminals who complete their community sentences have paid back to their community for the harm they have caused. The mean score across the items is shown as The way in which the variables were recoded meant that the range ran from 0 to 20, which suggests that this may be a normally distributed variable (this could be tested using a Kolmogorov-Smirnoff Test or looking at a histogram). (3) To create a scale from these items, you simply compute a new scale variable which adds the scores for each variable together. A higher value on the new scale variable indicates a positive attitude to community sentences, whereas a lower value indicates a negative attitude. The following syntax can be used to create a new scale variable and name it: COMPUTE csatt_sc=csatt2 + csatt3 + csatt4 + csatt5 + csatt7. VARIABLE LABELS csatt_sc Scale of attitudes towards community sentencing (negative to positive). Examine this variable to see how it is distributed. This can be done in a range of different ways, but we will use the explore command (this can be found under the Analyze menu within Descriptive Statistics). In addition to the other options available, we will ask for a histogram, as shown below. 42

45 The descriptive statistics for the new variable csatt_sc show that it has a range of values from 0 to 20, and a mean of 10.9 (so the mean is approximately in the centre of the range). The trimmed mean is very slightly higher at 11.04, and the median is 11, so we would not be expecting the variable to be highly skewed. The skewness statistic confirms that the level of skew is fairly minor and is in a negative direction (-.384). The standard deviation is 4.2, which suggests that there is some variance around the mean but it is not excessive. 43

46 Looking at the histogram, below, we can see that our assessment of the descriptive statistics is fairly accurate. There is some degree of negative skew (to the right), but on the whole the variable is approximately normally distributed. In other words, people s attitudes towards the value of community sentencing forms an approximate normal distribution curve. 3.5 How common are credit card fraud and identity theft? There is a perception that credit card and identity theft in Scotland are common. Therefore, one quarter sample module (Module C) of the 2010/11 survey asked a set of questions about people s experiences of credit card fraud and identity theft. Here we simply look to see what percentage of survey respondents knew that they had been a victim in terms of: having their credit card and/or bank card used without their permission (CARDVIC_01); having their bank details used without their permission (CARDVIC_02); and having had someone pretend to be the respondent or use their personal details without permission (IDTHEF). In this case, we take the three separate questions above and compute a new variable CC_ID_CRIME which identifies the prevalence of having experienced one of these three types of fraud. The new variable has a value of 1 if the respondent experienced any one of the three crime types, and 0 if they did not. compute CC_ID_CRIME=0. if cardvic_01 = 1 CC_ID_CRIME=1. if cardvic_02 = 1 CC_ID_CRIME=1. if idthef =1 CC_ID_CRIME=1. var lab CC_ID_CRIME 'Known victim of credit card or identity theft crime'. value labels CC_ID_CRIME 1 'Yes' 0 'No'. 44

47 Again, as the analysis is about the experience of individuals, the scaled individual weight is applied before a simple frequency of the new variable is run. weight BY WGTGINDIV_SCALE. fre CC_ID_CRIME. The output from the frequency command shown above produces the following table which shows that, despite the perception that this is a common problem, only 1.3% of the sample as a whole reported that they had been a victim of either credit/bank card fraud or identity theft. As with the earlier analysis on incidence of victimisation, we could run a weighted frequency analysis to estimate how many people within Scotland are victims of this type of crime using the sum command (just over 55,000 in 2010/11). Questions for you to explore There are many other variables contained in the main respondent dataset of the SCJS that could be analysed. Here are some further questions you may wish to explore: 1. To what extent people s experience of crime, their age and their sex related to their attitudes towards community sentences? (Tip: you can use the newly created csatt_sc variable in a series of bivariate analyses, or you could use it as the dependent variable in a linear regression). 2. Which forms of community sentence do the public believe are most useful in driving down crime? (Tip: use a multiple response analysis to examine variables qdisred_01 to qdisred_40). 3. How many people in Scotland believe that capital punishment is a form of community sentence offered in Scottish courts? (Tip: use variable QDISKNW12_09: Known community sentences: Spontaneous - Total Mentions: Capital Punishment). 4. What is the relationship between victimisation and offending? (Tip: use variables qdbeenp_01 to qdbeenp_04). 5. Is crime victimisation explained by social deprivation, even when controlling for a range of other factors such as age, sex and socio-economic status? (Tip: use variable SIMD_QUINT: Scottish Index of Multiple Deprivation Quintiles or SIMD_TOP: Scottish Index of Multiple Deprivation - Top 15% Most Deprived). 45

48 3. Practical Examples Using the Self-completion Dataset 2010/11 In this section we will explore three questions using a variety of analyses on the Selfcompletion dataset from 2010/11. First, open this dataset scjs_s4_ _sc_ukda_ sav. 4.1 What percentage of the Scottish population has taken drugs, and does this vary by age and sex? The SCJS provides the only national prevalence data on drug use. The self-completion questionnaire contains a series of questions in which respondents were asked to identify which items from a list of substances they had ever taken, which they had taken within the last 12 months, and which they had taken within the last month. For the purposes of this example, the ever question will be used. Thus, the results will not show current drug use, but rather lifetime prevalence of drug use. In order to undertake this analysis, we have to create a new derived variable EVERDRUG to measure lifetime drug use. The 2010/11 dataset that is used in this data workshop actually contains a variable QEVE_ANY, which is essentially the same as the derived variable created. However, not all of the available datasets have this variable attached, so it is worthwhile creating syntax to be able to do this. A frequency analysis of the derived variable will determine overall prevalence of drug use, and a crosstabulation by age and sex will determine the extent to which drug use varies for different demographic groups. This difference will be illustrated using an error bar graph. The individual weight WGTGINDIV_SC_SCALE will be used as we are looking at individual differences. This analysis is split into four parts. (1) To create the derived variable EVERDRUG, run the following syntax: compute EVERDRUG=99. if (qeve_01=1 or qeve_02=1 or qeve_03=1 or qeve_04=1 or qeve_05=1 or qeve_06=1 or qeve_07=1 or qeve_08=1 or qeve_09=1 or qeve_11=1 or qeve_12=1 or qeve_13=1 or qeve_14=1 or qeve_15=1 or qeve_16=1 or qeve_17=1 or qeve_18=1 or qeve_19=1 or qeve_20=1 or qeve_21=1 or qeve_22=1) EVERDRUG=1. If (qeve_01=2 and qeve_02=2 and qeve_03=2 and qeve_04=2 and qeve_05=2 and qeve_06=2 and qeve_07=2 and qeve_08=2 and qeve_09=2 and qeve_11=2 and qeve_12=2 and qeve_13=2 and qeve_14=2 and qeve_15=2 and qeve_16=2 and qeve_17=2 and qeve_18=2 and qeve_19=2 and qeve_20=2 and qeve_21=2 and qeve_22=2) EVERDRUG=0.var lab EVERDRUG 'Whether respondent has ever taken drugs'. val lab EVERDRUG 1 'yes' 0 'no' 99 'missing'. missing values EVERDRUG (99). To create this variable via the menus requires three separate operations; setting the default value as 99, turning everyone who answered yes to any of the drug use questions to having a value of 1 for new variable, then turning everyone who answered no to all of the drug use questions to 0. This is quite a time consuming operation, also with greater chance of making an error, and so it is not recommended. If you do wish to recode this variable, however, the steps are as follows: 46

49 First, open the Compute window as outlined in the example in section 2. Type EVERDRUG in the target variable field and the 99 in the Numeric Expression field, and then Press Paste. This gives the command COMPUTE EVERDRUG=99. EXECUTE. With this command saved in the syntax window, delete 99 from the Numeric expression window and click on the If button at the bottom left of the screen. This will bring up the If Cases window (see screenshot below). Click on the Include if case satisfies condition: button, which will activate the variable list on the left hand side. We effectively need to recreate the syntax for the operation if (qeve_01=1 or qeve_02=1 or qeve_03=1 or qeve_04=1 or qeve_05=1 or qeve_06=1 or qeve_07=1 or qeve_08=1 or qeve_09=1 or qeve_11=1 or qeve_12=1 or qeve_13=1 or qeve_14=1 or qeve_15=1 or qeve_16=1 or qeve_17=1 or qeve_18=1 or qeve_19=1 or qeve_20=1 or qeve_21=1 or qeve_22=1) EVERDRUG=1. In the Include if case satisfied condition: field. To do this, select the QEVE_01 variable from the list on the left hand side, and then press the arrow button. Now type =1 or in the Include if case satisfies condition: field (The or command lets us create a variable for anyone who answered yes to any of the drug use questions). Now add the variable qeve_02 as before, selecting it from the variable list and then clicking the arrow button. Now type =1 or after qeve_02 as before. Continue this process for all of the qeve 47

50 variables up to qeve_22 (Note that there is no qeve_10 variable). After qeve_22 just type =1, not =1 or. The window should look as below: Press continue to return to the Compute Variable window. Now, type 1 in the Numeric Expression: field. Press paste and the following will appear in the Syntax window: 48

51 IF (qeve_01=1 or qeve_02=1 or qeve_03=1 or qeve_04=1 or qeve_05=1 or qeve_06=1 or qeve_07=1 or qeve_08=1 or qeve_09=1 or qeve_11=1 or qeve_12=1 or qeve_13=1 or qeve_14=1 or qeve_15=1 or qeve_16=1 or qeve_17=1 or qeve_18=1 or qeve_19=1 or qeve_20=1 or qeve_21=1 or qeve_22=1) EVERDRUG=1. EXECUTE. With the syntax saved, delete the 1 from the Numeric Expression window, click on the If button again. As we want to set a condition for all of the same variables, you can either delete all the variables and add them again, or just change =1 if between the variable names to =2 and. This tells SPSS that we want to select everyone who answered no to every drug use question. Once you have made these changes the If Cases window should look as follows: Click Continue, and then in the Compute Variable window, enter 0 in the Numeric Expression: field, then press Paste. 49

52 This gives us the following syntax: IF (qeve_01=2 and qeve_02=2 and qeve_03=2 and qeve_04=2 and qeve_05=2 and qeve_06=2 and qeve_07=2 and qeve_08=2 and qeve_09=2 and qeve_11=2 and qeve_12=2 and qeve_13=2 and qeve_14=2 and qeve_15=2 and qeve_16=2 and qeve_17=2 and qeve_18=2 and qeve_19=2 and qeve_20=2 and qeve_21=2 and qeve_22=2) EVERDRUG=0. EXECUTE. To create the EVERDRUG variable, run all of the three commands we have created (beginning with COMPUTE EVERDRUG=99). This will place the EVERDRUG variable at the bottom of the dataset. If you have created the EVERDRUG variable via the menus, run the following command to label the variable appropriately; var lab EVERDRUG 'Whether respondent has ever taken drugs'. val lab EVERDRUG 1 'yes' 0 'no' 99 'missing'. missing values EVERDRUG (99). Now that the EVERDRUG variable has been created, to apply the individual scaled weight for the self-completion file and run a frequency analysis of EVERDRUG run the following: weight by WGTGINDIV_SC_SCALE. frequencies variables=everdrug /order=analysis. 50

53 The frequency table below shows that around a quarter (24%) of those who responded to the self-completion questionnaire indicated that they had taken an illegal drug at least once during their lifetime. Whether respondent has ever taken drugs Frequency Percent Valid Percent Cumulative Percent Valid no yes Total Missing missing 52.5 Total (2) To analyse lifetime drug use by sex: crosstabs /tables=qdgen by EVERDRUG /format=avalue tables /statistics=chisq /cells=count row /count round cell. Looking at the row percentages in the table below, you can see that prevalence of drug use amongst men (29%) was significantly higher than for women (19%). The chisquare test result indicated that this difference was statistically significant (p<.001). QDGEN: Respondent's Gender * Whether respondent has ever taken drugs Crosstabulation Whether respondent has ever taken drugs no yes Total QDGEN: Respondent's Gender Male Count % within QDGEN: Respondent's 70.7% 29.3% 100.0% Gender Female Count % within QDGEN: Respondent's 81.2% 18.8% 100.0% Gender Total Count % within QDGEN: Respondent's Gender 76.2% 23.8% 100.0% Chi-Square Tests Asymp. Sig. (2- Value df sided) Pearson Chi-Square a Continuity Correction b Likelihood Ratio Exact Sig. (2-sided) Exact Sig. (1- sided) Fisher's Exact Test Linear-by-Linear Association N of Valid Cases a. 0 cells (.0%) have expected count less than 5. The minimum expected count is b. Computed only for a 2x2 table 51

54 (3) To analyse lifetime drug use by age group (recoding the QDAGE2 variable to group the age year olds into one band and those aged 65 or over into one band): recode QDAGE2 (1 thru 3=1) (4=2) (5=3) (6=4) (7=5) (8 thru highest=6) (else=sysmis) into QDAGE3. var lab QDAGE3 'Recode of QDAGE2 age bands'. val lab QDAGE3 1 '16-24 years' 2 '25-34 years' 3 '35-44 years' 4 '45-54 years' 5 '55-64 years' 6 '65+ years'. Using the recoded age variable, the cross-tabulation below shows that lifetime prevalence of drug use was greatest amongst those in the youngest age bands especially those aged 25 to 34 amongst whom just under half (43%) had used an illegal drug at some time. Prevalence reduced with age, with less than one in 20 of those aged 65 or over stating that they had ever taken an illegal drug. Recode of QDAGE2 age bands * Whether respondent has ever taken drugs Crosstabulation Whether respondent has ever taken drugs no yes Total Recode of QDAGE2 age bands years Count % within Recode of QDAGE2 age 62.7% 37.3% 100.0% bands years Count % within Recode of QDAGE2 age 57.2% 42.8% 100.0% bands years Count % within Recode of QDAGE2 age 65.8% 34.2% 100.0% bands years Count % within Recode of QDAGE2 age 78.0% 22.0% 100.0% bands years Count % within Recode of QDAGE2 age 85.6% 14.4% 100.0% bands 65+ years Count % within Recode of QDAGE2 age 96.2% 3.8% 100.0% bands Total Count % within Recode of QDAGE2 age bands 76.2% 23.8% 100.0% Chi-Square Tests Value df Asymp. Sig. (2-sided) Pearson Chi-Square a Likelihood Ratio Linear-by-Linear Association N of Valid Cases a. 0 cells (.0%) have expected count less than 5. The minimum expected count is (4) To create an error bar graph showing age and sex differences in lifetime drug use via syntax, run the following command (this is included in the syntax file): graph /errorbar(ci 95)= QDAGE by EVERDRUG by QDGEN. To create an error bar graph via the menus, complete the following steps: 52

55 Select Error Bar from the Legacy Dialogs option in the Graphs menu: You will be presented with a window offering a choice of either a simple or a clustered error bar graph. As we wish to investigate age and sex differences in lifetime drug use we need to select a clustered error bar, in order to look at age differences for men and women separately. Click on the box next to the Clustered option (the box will then be outlined in black as below) and then select Define. Leave Summaries for groups of cases selected. You will be presented with the window shown in the screenshot below. From the list of variables on the left hand side, select qdage as the Variable, EVERDRUG as the Category 53

56 Axis and qdgen as Define Clusters by (It may be easiest to do this after selecting the variables to show by variable names see section 2). Once you have selected the variables, press Paste to see the syntax for the operation (not shown here). The error bar graph (shown below) confirms that there is a highly significant difference in lifetime prevalence of drug use by age (with the mean age of drug users being around 35 and the mean age of non-drug users being around 50). Comparing the error bars for males and females indicates that the mean age for male drug users is slightly higher than for female drug users. However, the confidence intervals overlap, so the difference is not statistically significant. In other words, the average age for female drug users is around the same as for male drug users. 54

57 4.2 Does the likelihood of sexual victimisation vary by age? The SCJS asks respondents to identify whether they have ever, since they were 16 years old, experienced a range of behaviours that can be grouped together under the broad heading of sexual victimisation. These include: indecent exposure; sexual threats; unwanted sexual touching; forced sexual intercourse; attempted forced sexual intercourse; forced other sexual activities; and attempted forced other sexual activities. These are grouped together into two categories (serious sexual assault and less serious sexual assault) for government analysis and reporting. The SCJS asks respondents if they have ever experienced the behaviours and, if a respondent reports that they have, follows up with a question on whether the experience took place within the last 12 months. Respondents reporting such recent experiences are then presented with a series of follow-up questions on the effects of the victimisation and whether the police were informed. The number of respondents reporting sexual victimisation overall is small, and this figure is reduced further when limiting analysis to recent experience. For the purposes of this workshop, the wider ever subgroup will be examined. The SCJS 2010/11 self-completion dataset includes e a derived variable SAANY_EV. This details whether a respondent stated that they had experienced at least one of the serious sexual assault behaviours. A further variable SVANY_EV details whether respondents stated they had experienced at least one of the less serious sexual assault variables. These variables are not available on earlier datasets, so in order to examine the experience of sexual victimisation, derived variables SERIOUS and LESSSERIOUS will be created. Frequency tables will be used to examine overall prevalence of sexual victimisation within the survey sample, and cross-tabulations and error bar graphs will 55

58 be used to explore whether this experience varies by age. This analysis requires two stages. (1) To create the derived variables SERIOUS and LESSSERIOUS : COMPUTE SERIOUS=99. If (safs=1 or saafs=1 or saos=1 or saaos=1) SERIOUS=1. If (safs=2 and saafs=2 and saos=2 and saaos=2) SERIOUS=0. variable label SERIOUS 'Respondent reports experience of serious sexual assault - ever'. value labels SERIOUS 1 'Yes' 0 'No'. missing values SERIOUS (99). COMPUTE LESSSERIOUS=99. If (svinex=1 or svst=1 or svts=1 ) LESSSERIOUS=1. If (svinex=2 and svst=2 and svts=2) LESSSERIOUS=0. variable label LESSSERIOUS 'Respondent reports experience of less serious sexual assault - ever'. value labels LESSSERIOUS 1 'Yes' 0 'No'. missing values LESSSERIOUS (99). To apply the individual scaled weight for self-completion respondents and run a frequency of the derived variables SERIOUS and LESSSERIOUS, use the following syntax: weight by WGTGINDIV_SC_SCALE. frequencies variables=serious LESSSERIOUS /order=analysis. The frequency tables show that only a small proportion of the survey sample report having experienced sexual victimisation. Only 2.8% of the sample report having experienced a serious sexual assault, and only 8.3% report having experienced a less serious sexual assault. It could be concluded that less serious sexual assault is more prevalent that serious sexual assault, but that both have a relatively low prevalence when compared against other forms of victimisation. Respondent reports experience of serious sexual assault - ever Frequency Percent Valid Percent Cumulative Percent Valid No Yes Total Respondent reports experience of less serious sexual assault - ever Frequency Percent Valid Percent Cumulative Percent Valid No Yes Total

59 (2) To analyse experience of sexual victimisation across different age groups in the sample, you can run a simple cross tabulation using the following syntax: crosstabs /tables=qdage3 by SERIOUS LESSSERIOUS /format=avalue tables /statistics=chisq /cells=count row /count round cell. The cross tabulation tables, shown below, indicate that the experience of sexual victimisation does vary by age. The first table, which presents the findings for serious sexual assault, indicates that prevalence of victimisation appears to be highest amongst respondents aged between 25 and 44 years, and lowest amongst the oldest group (1% of respondents aged 65 years or over) and the youngest (2% of respondents aged years). The chi square test indicates that this is a significant association. The results indicate that there is a curvi-linear relationship between age of the respondents and the probability of reporting experience of serious sexual assault. Remember that this is a cross-sectional survey and the measure is an ever one, so a curvilinear relationship might indicate some cohort effect (i.e. people who were born 65 or more years ago were less likely to be of a generation that experienced sexual victimisation) or it may be indicative of non-reporting (i.e. people who were born 65 or more years ago are less likely to admit that they experienced sexual victimisation). Nevertheless, the overall number of victims here is very small, so any findings ought to be treated cautiously. 57

60 Recode of QDAGE2 age bands Crosstab Respondent reports experience of serious sexual assault - ever No Yes Total years Count % within Recode of 98.1% 1.9% 100.0% QDAGE2 age bands years Count % within Recode of 95.9% 4.1% 100.0% QDAGE2 age bands years Count % within Recode of 95.9% 4.1% 100.0% QDAGE2 age bands years Count % within Recode of 96.4% 3.6% 100.0% QDAGE2 age bands years Count % within Recode of 96.5% 3.5% 100.0% QDAGE2 age bands 65+ years Count % within Recode of 99.0% 1.0% 100.0% QDAGE2 age bands Total Count % within Recode of QDAGE2 age bands 97.2% 2.8% 100.0% With regard to less serious sexual assault (shown in the table below), it appears that there also some variation by age group. This is less pronounced at the lower end of the age spectrum, but those aged 65 or over are far less likely to report such incidents. Once again, this is a statistically significant association (chi-squared table not shown) and the numbers are greater which indicates that there may well be either a cohort or age effect here. Recode of QDAGE2 age bands Crosstab Respondent reports experience of less serious sexual assault - ever No Yes Total years Count % within Recode of 91.6% 8.4% 100.0% QDAGE2 age bands years Count % within Recode of 90.3% 9.7% 100.0% QDAGE2 age bands years Count % within Recode of 88.7% 11.3% 100.0% QDAGE2 age bands years Count % within Recode of 90.1% 9.9% 100.0% QDAGE2 age bands years Count % within Recode of 90.7% 9.3% 100.0% QDAGE2 age bands 65+ years Count % within Recode of 95.8% 4.2% 100.0% QDAGE2 age bands Total Count

61 (3) Using a simple error bar graph you can visualise the association between age and experience of sexual victimisation by comparing the age profiles of victims and nonvictims.. Note that for these error bar graphs there are only two variables being analysed, and so you would select the Simple option if creating the error bar graph via the menu (as shown below). The window for the simple error bar is essentially the same as that for the clustered error bar, minus the Define Clusters by field. In these examples, qdage would be placed in the Variable field as before, and SERIOUS in the Category Axis field for the first example, and LESSSERIOUS in the Category Axis field in the second. Alternatively, you could simply use the following syntax for an error bar chart: graph /errorbar(ci 95)= QDAGE by SERIOUS. graph /errorbar(ci 95)= QDAGE by LESSSERIOUS. For both variables, the graphs below show that the mean age of victims is significantly different from the mean age of non-victims. Nevertheless, in contrast to the drugs example used above, the degree of difference is relatively small. In both cases, the mean age for victims is around 42 years, and the mean age of non-victims is around 47 years. 59

62 4.3 Do gender and age predict the likelihood of experiencing partner abuse? The self-completion element of the SCJS also provides national prevalence data for partner and domestic abuse. Rather than asking respondents directly if they have experienced partner abuse, the self-completion questionnaire asks respondents whether they have experienced any of a series of acts or behaviours by a partner since they were age 16. The information we need to determine simply whether any respondent has ever been a victim of partner abuse is contained in multiple questions, so it is necessary to create a new derived variable (PABUSE). The following example introduces logistic regression modelling. This is a very brief introduction to a complex method of analysis. If you wish to use this method in your own research it is imperative you seek a full course of training. There are three steps required to undertake this procedure. 60

63 (1) To start, we must create the derived variable PABUSE and give it appropriate variable and value labels. This can be done using the Compute command, as with the previous example on sexual victimisation, but for convenience the syntax has been provided for you below. Essentially, this gives everyone who experienced any form of partner abuse a value of 1, and everyone who did not experience any of the forms of abuse asked about a value of 0. Anyone with missing information is coded as 99 and set to missing. compute PABUSE=99. if (da_1i_01=1 or da_1i_02=1 or da_1i_03=1 or da_1i_04=1 or da_1i_04=1 or da_1i_05=1 or da_1i_06=1 or da_1i_07=1 or da_1i_08=1 or da_1i_09=1 or da_1i_10=1 or da_1i_11=1 or da_1i_12=1 or da_1iii_01=1 or da_1iii_02=1 or da_1iii_03=1 or da_1iii_04=1 or da_1iii_05=1 or da_1iii_06=1 or da_1iii_07=1) PABUSE=1. if (da_1i_01=0 and da_1i_02=0 and da_1i_03=0 and da_1i_04=0 and da_1i_04=0 and da_1i_05=0 and da_1i_06=0 and da_1i_07=0 and da_1i_08=0 and da_1i_09=0 and da_1i_10=0 and da_1i_11=0 and da_1i_12=0 and da_1iii_01=0 and da_1iii_02=0 and da_1iii_03=0 and da_1iii_04=0 and da_1iii_05=0 and da_1iii_06=0 and da_1iii_07=0) PABUSE=0. var lab PABUSE 'ever experienced partner abuse'. val lab PABUSE 1 'yes' 0 'no' 99 'missing'. missing values PABUSE (99). As this is an individual level variable, you must apply the individual scaled weight for self-completion respondents before you run a frequency of the new variable PABUSE: weight by WGTGINDIV_SC_SCALE. frequencies variables=pabuse /order=analysis. The frequency table below shows that most respondents (83.7%) did not report experiencing partner abuse since the age of 16. However, around 16% of respondents in 2010/11 reported that they had experienced some form of partner abuse. ever experienced partner abuse Frequency Percent Valid Percent Cumulative Percent Valid no yes Total Missing missing Total

64 (2) We can examine the bivariate relationship between partner abuse and gender and age using cross tabulation analysis and a chi square test, using the syntax below. crosstabs /tables=pabuse by QDGEN QDAGE3 /format=avalue tables /statistics=chisq /cells=count column /count round cell. The following crosstab tables and the accompanying chi square test results suggest that there is a significant relationship between both gender and age and the likelihood of experiencing partner abuse. Looking first at the results relating to gender, it appears that women are more likely than men to report an experience of partner abuse. However, the proportions observed are more similar than we might have expected based on current perceptions of partner abuse as a crime predominantly against women. Crosstab QDGEN: Respondent's Gender Male Female Total ever experienced partner no Count abuse % within QDGEN: 86.5% 81.1% 83.7% Respondent's Gender yes Count % within QDGEN: 13.5% 18.9% 16.3% Respondent's Gender Total Count % within QDGEN: Respondent's Gender 100.0% 100.0% 100.0% Chi-Square Tests Asymp. Sig. (2- Value df sided) Pearson Chi-Square a Continuity Correction b Likelihood Ratio Exact Sig. (2- sided) Exact Sig. (1- sided) Fisher's Exact Test Linear-by-Linear Association N of Valid Cases a. 0 cells (.0%) have expected count less than 5. The minimum expected count is b. Computed only for a 2x2 table With regards to age, it appears that the likelihood of reporting an experience of partner abuse increases with age, until the age of respondents reaches years. At this point, the likelihood of a respondent reporting an experience of partner abuse begins to decrease again. As with sexual victimisation, this suggests there is a curvilinear relationship between age of the respondent and the experience of partner abuse. As this is cross-sectional data and the measure is ever, this would suggest that those over the age of 45 were of a generation where partner abuse was less likely to happen or that people over this age are more reluctant to report such incidents. 62

65 ever experienced partner abuse Crosstab Recode of QDAGE2 age bands years years years years years 65+ years Total no Count % within Recode 81.5% 77.7% 75.3% 80.6% 85.9% 94.2% 83.7% of QDAGE2 age bands yes Count % within Recode 18.5% 22.3% 24.7% 19.4% 14.1% 5.8% 16.3% of QDAGE2 age bands Total Count % within Recode 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% of QDAGE2 age bands Chi-Square Tests Value df Asymp. Sig. (2-sided) Pearson Chi-Square a Likelihood Ratio Linear-by-Linear Association N of Valid Cases a. 0 cells (.0%) have expected count less than 5. The minimum expected count is (3) We are now going to model the data to see whether age and sex influence the probability of reporting partner abuse, when controlling for the other. Our analysis so far has shown that there is a significant relationship between partner abuse and both sex and age, although the relationship with age is not linear. Therefore, we should take account of this in the modelling. The original scale version of the age variable, qdage, is most appropriate for modelling in this case. To start, we will recode qdage so that it starts at 0 rather than 16 (which is the minimum age of respondents in the survey). This can be done using the following syntax, which simply subtracts 16 from all values in the dataset so that the scale begins at zero: COMPUTE qdage16=qdage Because we suspect that the relationship between partner abuse and age increases to a certain point and then declines, we should include some form of quadratic in our model. You can do this by creating a squared age variable, using the syntax below: COMPUTE age16squared=qdage16 * qdage16. Finally, we can convert the gender variable into two dummy variables, as shown below. Only one of these dummies will be entered into the model predicting experience of partner abuse. We will not explore how dummies work in this session, but using dummies makes the results of the model easier to interpret. COMPUTE male=qdgen=1. COMPUTE female=qdgen=2. 63

66 To specify a logistic regression model to predict the probability of experiencing partner abuse via the menus, first select Binary Logistic from the Regression option in the Analyse menu, as shown below. This produces the Logistic Regression window, shown below. First, select the appropriate variables from the list on the left hand side. In this example PABUSE is the Dependent variable, and qdage16, age16squared and female are the independent variables which are placed in the Covariates box. There are various other things that you can do in logistic regression (such as save predicted probabilities); but for the time being, you should just click on the Options box to select some specific output that we will examine further. 64

67 Under options, tick the Classification plots, Hosmer-Lemeshow goodness-of-fit, Casewise listing of residuals, Correlations of estimates, Iteration history and CI for exp(b). The choices in the Options window will provide additional output, including odds ratios as well as coefficient values and some goodness of fit tests. There is not scope in this session to examine all of these different options fully, but some key points are discussed below. Once you have selected these options, click Continue. 65