Data Liberation Training Data Liberation Training 20012001

Complex Files:Pasting and Cutting

with SPSS

Université de Montréal

Wendy WatkinsApril 24, 2001

ObjectivesObjectives

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

Outline: ConceptsOutline: Concepts

Complex Files

Longitudinal Files

Hierarchical Files Separate Files Combined Files

“Split” Files

Outline: TasksOutline: Tasks Pasting and Cutting with

SPSS

Pasting Adding variables Adding cases

Cutting Selecting Flag Variables Weighting

Complex FilesComplex Files

Concepts

Longitudinal FilesLongitudinal 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

Longitudinal FilesLongitudinal Files

Have a common linking variable Usually an ID number

Are combined through a matching process

Separate Hierarchical FilesSeparate Hierarchical Files

eg. GSS10 - Family Same respondents Different units of analysis Allow matching of

individuals with attributes Based on data structure

Separate Hierarchical Separate Hierarchical Files: StructureFiles: 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)

Separate Hierarchical FilesSeparate 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

Combined Hierarchical Combined Hierarchical FilesFiles

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

Combined Hierarchical Combined Hierarchical Files: StructureFiles: 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)

Combined Hierarchical Combined Hierarchical FilesFiles

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)

Combined Hierarchical Combined Hierarchical FilesFiles

Each unit of analysis has a flag and weight

Individuals Person flag/Person weight

Incidents Incident flag/Incident weight

““Split” FilesSplit” Files

Different respondents Same variables Same unit of analysis Files literally in pieces

Monthly files - Travel Survey Regional files - HIFE

Based on data-management

““Split” FilesSplit” Files

eg. Travel Survey January file + February file + …. + December file = Annual file

Combine by simply adding No matching necessary

Complex FilesComplex Files

Tasks:

Pasting and Cutting with SPSS

Complex FilesComplex Files NOT like word-processing

Either paste Add casesAdd variables

Or cutSelect flags and weights

Pasting with SPSSPasting with SPSS

Longitudinal filesAdding variables

Same respondentsDifferent variablesSame units of analysis

Pasting with SPSSPasting with SPSS

Longitudinal filesMust ensure the files are in the same order

Each individual has a unique ID number Files must be sorted by this ID,

before they are matched

Pasting with SPSSPasting with SPSS Longitudinal files

Step 1: Sort all files by matching variable and save results

Pasting with SPSSPasting with SPSS Longitudinal files

Step 2: Merge sorted files by adding variables.

Pasting with SPSSPasting with SPSS Longitudinal files

Step 3: Match files by matching variable and save

Pasting with SPSSPasting 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

Pasting with SPSSPasting with SPSS

Separate Hierarchical Files Must match all attributes to

individual One respondent may have none,

one or many eg. parent / child(ren)

Pasting with SPSSPasting 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

Pasting with SPSSPasting with SPSS Separate Hierarchical Files

Main respondent=keyed table

Pasting with SPSSPasting with SPSS

“Split” Files Add cases

Different respondents Same variables Same units of analysis No need to match or sort

Pasting with SPSSPasting with SPSS “Split” Files

One-step process; no sorting required

Cutting with SPSSCutting 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

Cutting with SPSSCutting with SPSS Combined Hierarchical Files

Step 1: Select unit of analysis (eg. person)

Step 2: Select appropriate flag

Step 3: Apply appropriate weight

Cutting with SPSSCutting with SPSS Combined Hierarchical

FilesSteps 1 and 2

Cutting with SPSSCutting with SPSS Combined Hierarchical Files Step 3

In a NutshellIn a NutshellPasting 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

A Quick Review from A Quick Review from 2000:2000:

Levels of Measurement and Levels of Measurement and SPSS ProceduresSPSS Procedures

Nominal variables Ordinal variables

Frequencies Crosstabs

Interval variables Descriptives Compare means

Levels of MeasurementLevels of Measurement

Categorical Variables Numbers Denote

Categories Have No Intrinsic Meaning Nominal

Are unordered Ordinal

Have an order

Categorical VariablesCategorical 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.

Categorical VariablesCategorical 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.

Continuous VariablesContinuous 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?

Levels of InformationLevels of Information

Interval Variables = most information

Ordinal Variables = less information

Nominal Variables = least information

Using CrosstabsUsing 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?

Using MeansUsing 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?

Time for a Break!Time for a Break!