Social Sciences as Hokum

1. Social Sciences as Hokum

4. HOW I USE IT solutions unexpected patterns plan predictive analytics anticipate change high-impact decisions develop models improve outcomes. associations information to predict…and the power to act.

5. The SPSS cure! Forecasting Sales Growth in the NHS and social research in the public sector. By Tassadaque Masood

6. Objectives • My experiences of using SPSS • Forecasting-NHS Supply Chain • Nottingham University Hospitals NHS Trust A&EForecasting Project. • BSA 2007 Data Set • Conclusions & Recommendations • Questions

7. Forecasting

8. Data Challenges • Time series data- each case (row) represents a set of observations at a different time, and the length of time between cases is uniform. • Create 3 new time series variables as functions of existing time series variables. • Generate date variables to establish periodicity and to distinguish between historical and forecasting periods.

17. NHS Supply Chain-Advantages • Uncover data patterns • Predict trends and forecast future events • Effective planning tool against a competitive backdrop which is constantly changing- e.g. flu pandemic. • Effective staff planning at peak periods. • Access, manipulate and model data types, from all sources and show different methods according to business need.

18. Nottingham University Hospitals NHS Trust

19. Data Challenges • Time series data- each case (row) represents a set of observations at a different time, and the length of time between cases is uniform-Not the case. • Create new time series variables as functions of existing time series variables. • Generate date variables to establish periodicity and to distinguish between historical, validation, and forecasting periods. • Missing Values

20. Data Integration with SPSS Data Cleansing using SPSS Data Summary SPSS Syntax- Create Periodicity Forecasting Model Data Validity Checks Data Interpretation Process Overview

21. Generating descriptive statistics independently of frequencies • Select Analyze, and then Descriptive Statistics... from the main menu. • When the sub- menu appears select Descriptive… to call up the Descriptive dialogue box. • When you are ready to run your descriptive statistics from the Descriptive dialogue box click on OK.

22. Example

23. SPSS Syntax- Create Periodicity-Define Dates • The Define Dates dialog box allows you to generate date variables that can be used to establish the periodicity of a time series and to label output from time series analysis.

24. Define Dates SPSS Syntax- Create Periodicity-Define Dates • From the menus choose: • Data • Define Dates • Select a time interval from the Cases Are list. • Enter the value(s) that define the starting date for First Case Is, which determines the date assigned to the first case.

25. British Social Attitudes 2007

26. Compare means • Another useful function that SPSS possesses is the ability to compare the means for 2 variables. • Go to the Analyze menu and select Compare Means and then Means… from the submenu.

27. Compare means • Select the variable that deals with attitudes to censorship and the variable for sex of respondent and paste them into the correct parts of the window so that they look like the example here. • Click on OK and SPSS will produce the following output for you.

28. Compare means • Given that the scoring system in relation to this (and the other variables concerning moral issues) variable, it runs from 1 for those who say that they ‘strongly agree’ through to 5 for those who say that they ‘strongly disagree’, • The lower the mean score, the more agreement that there is with the statement.

29. Manipulating and transforming data: the Recode function • Modify an existing variable by looking at how we can create a new variable that will put the respondents’ ages in age bands. • Go to the Transform menu and select Recode into Different Variables • This will call up the Recode into Different Variables dialogue box.

30. Select the Age variable from the source list and place it in window in the middle of the dialogue box using the arrow button or by dragging and dropping it. • Click on the Old and New Values button to call up the Old and New Values dialogue box.

31. Click where it says ‘Range, LOWEST through value’ to put a dot in the white circle. • Then on the ‘new value’ side of the dialogue box type 1 into the space where it says ‘value’.

32. Click on the Add button and ‘lowest thru 24 → 1’ appears in the ‘old → new’ area. • This operation has recoded all of the ages from the youngest respondent through to those aged 24 and put them all in an age group.

33. Using the ‘Range’ part of the ‘old value’ side of the dialogue box, recode the age bands in groups of 10 years, i.e., 25 - 34 up to 75 - 84. • As you progress through these age groups you will need to give them new values from 2 onwards up to 7. • Finally, use the ‘Range, value through HIGHEST’ function to recode all of those over the age of 85 into an eighth age category.

34. Click on Continue to return to the Recode into Different Variables dialogue box. • Now, give the new variable that you are creating a new name (age2, for example) and a new variable label (age groups), and then click on the Change button. • Click on the OK button to run the recode.

35. Click in the Numeric cell and change the width and decimals to 1 and 0 respectively. • Click in the values cell and assign the values and value labels that correspond with the recoded categories. • When you have done this your labels dialogue box should like the example shown here. • Finally, assign your new variable an ordinal level of measurement. • Run a frequency to see the new age group variable.

36. Scale • This new recoded age variable will be very useful when it comes to conducting bivariate analysis using cross-tabulation tables. • Recode function like this to make some data more manageable, bear in mind that whilst you will make it easier for the data to be used you are likely to downgrade their level of measurement and thus make more sophisticated analysis less likely. • In this example, a scale variable has been reduced to an ordinal variable. Ordinal

37. To do this you would recode 1 and 2 (‘agree strongly’ and ‘agree’) into a single agree category = 1. • Note that while you would need to code 2 = 1, to leave 1 = 1 you can use the ‘all other values’ and ‘copy old values’ buttons in the recode dialogue box. • The previous middle neutral category of ‘neither agree nor disagree’ would be recoded from 3 to a new middle value of 2. Finally, 4 and 5(‘disagree’ and ‘disagree strongly’) would be recoded into a single disagree category or 3.

38. Recoding to allow for higher statistical analysis • Let us look at the example of the variable that deals with the highest education qualification that is held by respondents. • However, there are two values that serve to interrupt this flow: ‘foreign or other’ and ‘don’t know/refusal/not applicable’.

39. To recode this variable accordingly we would need to: • Recode the values 6 (foreign or other) and 8 (don’t know/refusal/not applicable) as missing. • Recode 7 (no qualification) as 6, thus maintaining a flowing numerical order from 1 (degree) to 6 (no qualification). • Copy all the other values across as they were in the original variable.

40. Having removed 9.6% of respondents who were coded 6 or 8 as missing, our new frequency table shows an ordinal variable.

41. Computing a new variable • The Compute command can be used to transform the values of one or more existing variable(s) into a new variable. • Let’s work with the set of six variables at the end of the data set that looks at people’s attitudes towards a number of statement concerning moral values. • By using the Compute function, however, it is possible to get an overall view of attitudes towards moral issues.

42. Go to the Transform menu and select Compute Variable and the Compute Variable dialogue box will appear. • Click in the Target Variable box at the top left of the dialogue box and type in the name of the new variable that you will compute. Let’s call this variable morals. • Give your new variable a variable label by clicking on the Type & Label button below the Target Variable box, typing in an appropriate variable label, and then • Click on Continue.

43. Go to the source list of variables which contain the six variables that deal with moral issues. • Select the first of these for GB values] and paste it into the Numeric Expression box. • Click on the + button and then repeat the process with the remaining variables.. • Then click on OK.

44. BSA Example

45. From this initial analysis of the new variable, we can see that the respondents do tend more towards the authoritarian than the libertarian end of the scale. • There are a variety of ways that we could report this data. • One option would be to simply add together a certain number of responses at either end of the variable. • For example, while 22.1% of respondents are in the lowest five categories (6-10), only 0.4% are in the highest five categories (25- • 30).

46. If we run a means comparison against age (grouped), we can also see that there appears to be a clear pattern between moral values and age. • The older the age group the more the tendency to hold an authoritarian outlook.

47. Creating graphics in SPSS • SPSS gives you the opportunity to generate a graphic whenever you ask it to run a frequency. • Go to the Frequencies command. • Select the variable the ‘censor’ variable that deals with respondents’ attitudes towards censorship and paste it into the target list.

48. Click on the Charts… button in the dialogue box. • Select Pie charts as a Chart Type, and then Percentages for the Chart Values. • Click on Continue, and then OK in the Frequency dialogue box to run the output.

49. Check level of measurement-Nominal (Sex of Respondent), Ordinal (logical order e.g. Agree to Disagree) or Scale. Carry out analysis according to level of measurement. The ability to choose the appropriate statistical test and to see whether the calculations are Scale variables=Larger Statistical analysis Conscious of a progression from one level of measurement to another. BSA Dataset-Key Conclusions

50. Conclusions & Recommendations