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

 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

 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

 The more traditional way:  Split your file by a break variable, here id SORT CASES BY id. SPLIT FILE LAYERED BY id.  Run Frequencies FREQUENCIES VARIABLES=AU1 /ORDER=ANALYSIS.  This works well ▪ But is limited in what it can tell us

 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

 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.

 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.

 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.

 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.

 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

 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)) 0) AU11_to_AU11_AU14=1. EXECUTE.

 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

 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

 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

 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).

 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.

 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…

 Like so…  We now have a rate per minute for both conditions IF (frame 599) Stim_1=1. EXECUTE. AGGREGATE /OUTFILE='Agg' /BREAK=id Stim_1 /AU1_onset_n=N(AU1_onset) /frame_n=N(frame).

 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

 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?

 As with any set of analyses, ensure that the particular variable you are calculating in a meaningful construct  Thank you for your interest!