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

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

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

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

Complex Files Concepts

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

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

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

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

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

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

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

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

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

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

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

Complex Files Tasks: Pasting and Cutting with SPSS

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

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

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

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

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

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

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

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

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

Pasting with SPSS u Separate Hierarchical Files u Main respondent=keyed table

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

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

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

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

Cutting with SPSS u Combined Hierarchical Files u Steps 1 and 2

Cutting with SPSS u Combined Hierarchical Files u Step 3

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

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

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

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

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

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

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

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

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

Time for a Break!