A Functional Example of Analyzing - PowerPoint Presentation

Slide1

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

S

tep-by-step techniques in SPSS

Whitney I.

Mattson 09/15/2010Slide2

What is in this DocumentHow 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 describedSlide3

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 areMost applicable to files in a similar formatTools here can be adapted to other casesSlide4

How to look at proportions of a behavior

The more traditional way:

Split your file by a break variable, here id

SORT 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 usSlide5

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 NWeighted Missing ValuesCreate a new dataset with only the aggregated variablesSlide6

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 consumingNeeded for more complex proportion scoresProportions 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.Slide7

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 variableWeighted NWeighted 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.

Slide8

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.Slide9

How to look at simple patterns of transitionProportions 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.Slide10

How to look at simple patterns of transition

Using the lag function to get values in previous rows

l

ag (

variable name

)Returns the last row’s value for the specified variableCan be used in compute statements to compare changes in variablesSlide11

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.Slide12

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 equationsSlide13

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 rowUseful 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 previousSlide14

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 XSlide15

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).Slide16

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.Slide17

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…Slide18

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).Slide19

Further analysis and combining techniquesBased 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 timeSlide20

Further analysis and combining techniquesBased 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?Slide21

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

Thank you for your interest!