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Data Liberation Training 2001

Data Liberation Training 2001. Complex Files: Pasting and Cutting with SPSS Université de Montréal Wendy Watkins April 24, 2001. Objectives. To be able to recognize types of complex files To understand the process of matching and adding files

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Data Liberation Training 2001

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  1. Data Liberation Training 2001 Complex Files: Pasting and Cutting with SPSS Université de Montréal Wendy Watkins April 24, 2001

  2. Objectives To be able to recognize types of complex files To understand the process of matching and adding files To have enough information to warn users about how to handle complex files

  3. Outline: Concepts Complex Files Longitudinal Files Hierarchical Files Separate Files Combined Files “Split” Files

  4. Outline: Tasks Pasting and Cutting with SPSS Pasting Adding variables Adding cases Cutting Selecting Flag Variables Weighting

  5. Complex Files Concepts

  6. Longitudinal Files eg. Kids, NPHS and SLID surveys Same respondents Different variables or variable names Data collected on a regular schedule Provide a look at what happens over time

  7. Longitudinal Files Have a common linking variable Usually an ID number Are combined through a matching process

  8. Separate Hierarchical Files eg. GSS10 - Family Same respondents Different units of analysis Allow matching of individuals with attributes Based on data structure

  9. Separate Hierarchical Files: Structure GSS 10 - Family Main file Respondent 1(R1) Respondent 2 (R2) …. Respondent n (Rn) Child file Kid 1 (R1) Kid 2 (R1) Kid 3 (R3)…. Kid N (Rn)

  10. Separate Hierarchical Files Must be certain to put the right child/children with the right respondent Each respondent has a unique identifier (id number) Each child has a matching identifier

  11. Combined Hierarchical Files eg. GSS 3 - Vicimization Same respondents Different units of analysis Everything in one file Based on data structure

  12. Combined Hierarchical Files: Structure GSS 3 - Victimization Respondent 1(R1) Incident 1 (I1-R1) Incident 2 (I2-R1) Respondent 2 (R2) …. Incident 1 (I1-R2) Respondent 3 (R3) Respondent n (Rn) Incident 1 (I1-Rn) Incident 2 (I2-Rn) Incident 3 (I3-Rn)

  13. Combined Hierarchical Files Must be certain to put the right incident with the right respondent Also need to be able to separate the units of analyses (individuals and incidents)

  14. Combined Hierarchical Files Each unit of analysis has a flag and weight Individuals Person flag/Person weight Incidents Incident flag/Incident weight

  15. “Split” Files Different respondents Same variables Same unit of analysis Files literally in pieces Monthly files - Travel Survey Regional files - HIFE Based on data-management

  16. “Split” Files eg. Travel Survey January file + February file + …. + December file = Annual file Combine by simply adding No matching necessary

  17. Complex Files Tasks: Pasting and Cutting with SPSS

  18. Complex Files NOT like word-processing Either paste Add cases Add variables Or cut Select flags and weights

  19. Pasting with SPSS Longitudinal files Adding variables Same respondents Different variables Same units of analysis

  20. Pasting with SPSS Longitudinal files Must ensure the files are in the same order Each individual has a unique ID number Files must be sorted by this ID, before they arematched

  21. Pasting with SPSS Longitudinal files Step 1: Sort all files by matching variable and save results

  22. Pasting with SPSS Longitudinal files Step 2: Merge sorted files by adding variables.

  23. Pasting with SPSS Longitudinal files Step 3: Match files by matching variable and save

  24. Pasting with SPSS Separate Hierarchical Files Similar to longitudinal files Must ensure the files are in the same order Each record has a unique identifier used for matching

  25. Pasting with SPSS Separate Hierarchical Files Must match all attributes to individual One respondent may have none, one or many eg. parent / child(ren)

  26. Pasting with SPSS Separate Hierarchical Files Sort files by matching variable and save results Match files by adding variables main respondent is in TABLE attributes are in FILE

  27. Pasting with SPSS Separate Hierarchical Files Main respondent=keyed table

  28. Pasting with SPSS “Split” Files Add cases Different respondents Same variables Same units of analysis No need to match or sort

  29. Pasting with SPSS “Split” Files One-step process; no sorting required

  30. Cutting with SPSS Combined Hierarchical Files Same cases Different units of analysis Files are already matched Want to analyze one unit of analysis Must use: Flag Variables Appropriate Weights

  31. Cutting with SPSS Combined Hierarchical Files Step 1: Select unit of analysis (eg. person) Step 2: Select appropriate flag Step 3: Apply appropriate weight

  32. Cutting with SPSS Combined Hierarchical Files Steps 1 and 2

  33. Cutting with SPSS Combined Hierarchical Files Step 3

  34. In a Nutshell Pasting Longitudinal files Sort and match with FILE Separate hierarchical files Sort and match with TABLE Split files Add cases Cutting Combined hierarchical files SELECT and WEIGHT

  35. A Quick Review from 2000:Levels of Measurement and SPSS Procedures Nominal variables Ordinal variables Frequencies Crosstabs Interval variables Descriptives Compare means

  36. Levels of Measurement Categorical Variables Numbers Denote Categories Have No Intrinsic Meaning Nominal Are unordered Ordinal Have an order

  37. Categorical Variables Nominal Variables Numbers stand for names Can’t order them eg. Marital Status 1=Single 2=Married or Common Law 3=Separated/Divorced/Widowed Can’t use arithmetic to add, etc.

  38. Categorical Variables Ordinal Variables Numbers can be ordered Spaces between numbers can’t be measured eg. How well do you like Harris? 1=Not at all 2=Less still 3=Even less than that Can’t use arithmetic to add, etc.

  39. Continuous Variables Interval Variables Numbers stand for what they are Spaces between numbers are equal eg. How many children do you have? Can use arithmetic eg. What is the average number of children in a family?

  40. Levels of Information Interval Variables = most information Ordinal Variables = less information Nominal Variables = least information

  41. Using Crosstabs How does ‘x’ relate to ‘y’? Use with nominal and ordinal measures eg. Are men or women more likely to use computers at work?

  42. Using Means Compares the average (mean) between groups Use when one variable is interval and the other is ordinal or nominal eg. Who has worked longer at their job, men or women?

  43. Time for a Break!

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