A Functional Example of Analyzing Co-occurrence and Sequence of Variables

Step-by-step techniques in SPSSWhitney I. Mattson 09/15/2010

What is in this Document

How to look at proportions of a behavior

How to look at proportion of co-occurrence

How to look at simple patterns of transition

Using a rate per minute measure SPSS syntax for the functions

described

The Example file

Contains Repeated rows for each

subject Each row corresponds

to the same unit of time Multiple variables from

a 1 to 5 scale▪ Missing values represent

no occurrence These methods are

Most applicable to files in a similar format

Tools here can be adapted to other cases

How to look at proportions of a behavior

The more traditional way: Split your file by a

break variable, here idSORT CASES BY id.SPLIT FILE LAYERED BY id.

Run FrequenciesFREQUENCIES VARIABLES=AU1 /ORDER=ANALYSIS.

This works well▪ But is limited in what it

can tell us

How to look at proportions of a behavior An aggregation

approach: In Data > Aggregate …

▪ Set your break variable (the same as the split file)

▪ Create two summaries of each variable ▪ Weighted N▪ Weighted Missing Values

▪ Create a new dataset with only the aggregated variables

How to look at proportions of a behavior The new file contains

A row for each subject The numerator and

denominator for our proportion

The proportion can be calculated with a compute statement

More time consuming Needed for more complex

proportion scores Proportions can be

analyzed

DATASET DECLARE Agg.AGGREGATE /OUTFILE='Agg' /BREAK=id /AU1_n=N(AU1) /AU1_nmiss=NMISS(AU1).

COMPUTE AU1_prop=AU1_n / (AU1_n + AU1_nmiss).EXECUTE.

How to look at proportion of co-occurrence

Back to the base file Compute a value when

variables co-occur▪ Here when there is one valid

case of variable AU1 and variable AU4

Aggregate again ▪ Add in summaries of the new

variable▪ Weighted N▪ Weighted Missing Values

Compute the proportion of time these two variables co-occur

IF (NVALID(AU1)>0 & NVALID(AU4)>0) AU1_AU4=1.EXECUTE.

DATASET DECLARE Agg.AGGREGATE /OUTFILE='Agg' /BREAK=id /AU1_n=N(AU1) /AU1_nmiss=NMISS(AU1) /AU4_n=N(AU4) /AU4_nmiss=NMISS(AU4) /AU1_AU4_n=N(AU1_AU4) /AU1_AU4_nmiss=NMISS(AU1_AU4).

COMPUTE AU1_AU4_prop=AU1_AU4_n / (AU1_AU4_n + AU1_AU4_nmiss).EXECUTE.

How to look at proportion of co-occurrence We now have a

proportion of the session that AU1 and AU4 co-occur

Using these same functions with different denominators yields other proportions

For example If you instead computed

AU1 and AU4 co-occurrence over AU4 cases

Proportion of time during AU4 when AU1 co-occurred

COMPUTE AU1_AU4_during_AU4_prop=AU1_AU4_n / (AU4_n).EXECUTE.

How to look at simple patterns of transition

Proportions are helpful in looking at characteristics of behavior broadly

However, we miss the evolution of sequence and co-occurrence throughout time

Time-series or lag analysis can tell us how often certain behaviors transition to certain other behaviors.

How to look at simple patterns of transition

Using the lag function to get values in previous rows

lag ( variable name ) Returns the last row’s value for the

specified variable Can be used in compute statements

to compare changes in variables

How to look at simple patterns of transition Here we use a lag

function to assess a transition When AU11 moves

to AU11 & AU14▪ This gives us the

frequency that AU14 occurs when AU11 is already there

IF (NVALID(AU11)>0 & NVALID(lag(AU11))>0 & NVALID(lag(AU14))<1 & NVALID(AU14)>0) AU11_to_AU11_AU14=1.EXECUTE.

How to look at simple patterns of transition

In addition to obtaining a straight frequency you can also use this transition variable to Assess a proportion of a specific

transition out of all transitions Summarize several of these variables

into a composite variable of transitions Plug these variables into more complex

equations

How to look at simple patterns of transition Here are a few other useful time series

variables you can create:(All of these are accessible through the Transform > Create Time Series… menu)

Lead – Returns the value of the variable in the next row

Difference – Returns the change in value from the previous row to the current row▪ Useful for finding changes in levels within a variable

In this menu you can easily change how many steps back or forward (order) your function takes▪ For example the value two rows previous

Using a rate per minute measure

Creating a rate per minute measure can Help tell you how often a behavior

occurs▪ While controlling for variation in session

duration Can be used to summarize changes

during meaningful epochs of time▪ For example, when Stimulus A is presented,

do subjects increase their onset of Behavior X

Using a rate per minute measure

Calculating a rate per minute Create a transition

(lag) variable for behavior onset

Use Aggregation to create:▪ Frequency of onset

variable▪ A duration of session

variable

IF (NVALID(AU1)>0 & NVALID(lag(AU1))<1) AU1_onset=1.EXECUTE.

DATASET DECLARE Agg.AGGREGATE /OUTFILE='Agg' /BREAK=id /AU11_onset_n=N(AU1_onset) /frame_n=N(frame).

Using a rate per minute measure

The new aggregated dataset allows Calculation of a

rate per minute variable

(30 for the number of frames per second,

60 for the number of seconds in a minute) Comparison across

subjects in rate per minute

COMPUTE AU11_RPM=AU11_onset_n / (frame_n / (30*60)).EXECUTE.

Using a rate per minute measure

You can also use this same method for different epochs of time Just add more break variables

For example, I create variable Stim_1 that signifies when I present a stimuli

I then aggregate by ID and this new variable…

Using a rate per minute measure

Like so…

We now have a rate per minute for both conditions

IF (frame < 500 & frame > 599) Stim_1=1.EXECUTE.

AGGREGATE /OUTFILE='Agg' /BREAK=id Stim_1 /AU1_onset_n=N(AU1_onset) /frame_n=N(frame).

Further analysis and combining techniques

Based on the aggregated datasets presented here you can Analyze group differences in

▪ Proportions of behavior▪ Proportions of behavior co-occurrence▪ Number of transitions▪ Rate per minute across meaningful periods of

time

Further analysis and combining techniques

Based on these variable creation techniques you can Combine methods to produce variables

which assess more complex questions For example:

▪ Is the proportion of Variable A during Variable B higher after Event X?

▪ Is the rate of transition per minute from Variable A to Variable B more frequent when Variable C co-occurs?

Final notes

As with any set of analyses, ensure that the particular variable you are calculating in a meaningful construct

Thank you for your interest!