1. Getting the most out of interactive and developmental data
Daniel Messinger
5.2001

2. Basics Know your phenomena Know your data
Watch the tapes
Know your data
Look at descriptives and frequencies
Look for patterns that you can see in individuals or dyads
Not just in the group as a whole

3. Topics Types of codes and coding
Frequency, duration, and combined approaches
Agreement and reliability
Measures of association between two behaviors
Development

4. Coding Types of codes Type of coding Agreement/Reliability
Objective vs. socially recognizable
Type of coding
Frequency, duration, combined approaches
Agreement/Reliability
Percent agreement, Cohen’s Kappa, intra-class correlation

5. Types of codes Objectively describable codes
E.g., Facial Action Coding System
Socially recognizable codes
E.g., Emotionally positive moments
Measurement and analysis more geared to objectively describable but there is no qualitative difference between different types of codes

6. Type of coding Duration Frequency Combined approaches
Agreement/Reliability
Agreeing on what we saw

7. Agreeing on what we saw Agreement Reliability
Whether same thing was observed at the same time
The focus of percent agreement and Kappa
Reliability
Did we see the same number of things in a given interactive period
Summary measure of codes over session
It all depends on the research question

8. Duration How long does behavior last Code onsets and offsets
E.g., Total time smiling
Code onsets and offsets
Smile begins, smile ends
Or exclusive categories
Smile, neutral, smile

9. Duration

10. Agreement on duration
Agreement = Total duration of time both coders indicated same event was occurring
Disagreement = Total duration of time coders indicated different events were occurring
Its ok to collapse codes

11. Duration agreement statistics
Proportion agreement
Agreement / (Agreement + Disagreement)
Observed agreement
Cohen’s Kappa
= (Observed agreement – Expected agreement) / (1 – Expected agreement)
Expected agreement
= Sum of product of marginals
Bakeman & Gottman

12. Frequency How often does behavior occur
Code onsets only
Always express as onsets per unit of time
E.g., number of smiles per minute
You can calculate frequencies from duration codes
Just count the onsets
But onset and offset coding is more difficult
E.g., vocalizations

13. Frequency

14. Frequency agreement Percent agreement
Number of times the same code is recorded by independent coders within a given time window (e.g., 2 seconds)
Divided by one half the total number of codes
No Kappa for frequency
There is no expected measure of agreement

15. Frequency and Duration: Pros and cons
Duration is a relatively stable measure of what’s going on
But not how they occurred
E..g., Total gazing at mother
Frequency tells you about discrete activities
But not how long they lasted
E.g., number of speech acts
Mixed approaches

16. Mixed approaches
Duration of smiling in a given period of interaction initiated by infant
Duration in that it’s a total time measure
But frequency in that its onset of infant action
Calculate both types of agreement

17. Reliability (intra-class correlation)
Summary measure of codes over session
Variance attributable to differences between subjects expressed as a proportion of all variance
Including variance between coders – want to minimize
Type of ANOVA
Better than a simple correlation

18. Measures of association between two behaviors
Group level analysis
Individual level analysis
Duration and frequency approaches

19. Example Infant gaze direction Mother smile Coded continuously in time
At mother’s face or away
Mother smile
Yes or no
Coded continuously in time

20. SPSS (10.0) VALUE LABELS m12 1.00000000000000 "Mother Not Smiling"
"Mother Smiling"

21. General duration variable
create
/leadweeK=lead(week 1) /LEADSECs=LEAD(SECS 1).
EXECUTE.
IF (week=leadweek) Duration=LEADSECS-SECS.
IF SYSMIS(DUR) DUR=0.

22. Analysis of entire group
Weight by duration, then . . .

23. Analysis of entire group
Doesn’t tell you if any given infant/dyad shows the association
Or the strength of association for a given infant/dyad
Use when necessary
E.g. small amounts of data for individual infants/dyads

24. Analysis of individuals
Determine frequency and duration of variables for whole group
Then aggregate
By subject/participant for general analysis
By time period for developmental analyses
Construct variables
Analysis

25. Specific variable duration
How much time do infants spend gazing at mother
And away from mother
How much time do mothers spend smiling
And not smiling

26. Specific variable duration
IF (infgaze=1) infgazeM=Duration.
IF (infgaze=2) infgazeA=Duration.
IF (momsmile=1) M1=Duration.
IF (momsmile=2) M2=Duration.
Execute.

27. Duration of two co-occurring behaviors
How long do infants gaze at mother when she is smiling?

28. Duration of two co-occurring behaviors
IF (infgaze=1 & momsmile=1) GMM1=Duration.
IF (infgaze=1 & momsmile=2) GMM2=Duration.
IF (infgaze=2 & momsmile=1) GAM1=Duration.
IF (infgaze=2 & momsmile=2) GAM2=Duration.
EXECUTE.

29. Combined duration and frequency approach
Duration of two co-occurring behaviors given that one has just occurred
How long do infants gaze at smiling mother having just gazed at her
Not when they were gazing and she smiled
Attention: The following technique assumes a dataset created so that only the variables of interest (in this case, two variables of interest) exist and that cases (rows) exist only when one of these variables changes (or there is a new session).

30. Combined duration and frequency approach
CREATE
/momsml_1=LAG(momsml 1) /infgaz_1=LAG(infgaz 1).
IF (infgaz=2 & infgaz_1~=2 & momsml=2) GAM2F=duration.
IF (infgaz=1 & infgaz_1~=1 & momsml=2) GMM2F=duration.
IF (infgaz=2 & infgaz_1~=2 & momsml=1) GAM1F=duration.
IF (infgaz=1 & infgaz_1~=1 & momsml=1) GMM1F=duration.
EXECUTE.

31. Aggregate: Summarizing data for analysis
Aggregate over subject for overall effects
Aggregate over subject and time period for developmental analyses

32. Aggregate creates new variables

33. Summarizes over time-linked cases
Summary measures
Number of cases for frequency
Sum of values for duration

34. Ouch! AGGREGATE /OUTFILE=* /BREAK=newsub
/ infgazm2= N(infgazem) /infgaza2 = N(infgazea)
/gmm1_3 = SUM(gmm1) /gmm2_3 = SUM(gmm2) /gam1_3 = SUM(gam1) /gam2_3 =
SUM(gam2) /gam2f_2 = N(gam2f) /gmm2f_2 = N(gmm2f) /gam1f_2 = N(gam1f)
/gmm1f_2 = N(gmm1f) /gam2f_3 = SUM(gam2f) /gmm2f_3 = SUM(gmm2f) /gam1f_3 =
SUM(gam1f) /gmm1f_3 = SUM(gmm1f).

35. Voile’

36. Constructing variables
New duration and frequency dependent measures are calculated per subject in new file
Same dependent measures will be calculated per time period within subject (in a different file) for developmental analyses

37. Creating durational proportions
COMPUTE GMM2P=Gmm2_3/M2_3.
Note: M2_3 (total time mother is smiling) can be created during aggregation or computed as = GMM2_3+GAM2_3.
Number of seconds of gazing at mother while mother is smiling divided by total time gazing at mother
Do the same for gazes at mother while mother is not smiling
These variables are calculated for each subject in new aggregated file

38. Analyses, finally

39. Results
Look at results subject by subject by graphing

41. Duration and frequency
Can tell you the same thing about an interaction
Or different things

42. Creating frequency per minute
Example:
COMPUTE GAM2PM=
(GAM2F_2/M2_3)*60.
This is calculated for each subject in new aggregated file
Number of gazes away while mother is smiling divided by total time mother is smiling
per minute
Do the same for gazes away while mother is not smiling

45. Duration and frequency together
Infant gazes at mother, mother smiles, infant then gazes away
Combined (frequency and duration) approach might have shed light on this directly

46. Development – Conceptual
How do individuals change over time?
Unit of analysis is individuals (or individual dyads) - singoli
Can developmental effects be seen in each individual’s data?
Or in a significant proportion of each individual’s data?
Keep it simple
General trends preferred over particular periods
Linear versus curvilinear effects

47. Development - Practical
Choices for developmental analyses
Hierarchical linear modeling
Individual growth (Linear and curvilinear models)
T-tests, binomial tests,
Graph individual and group data

48. Development - How to Go back to the original file
Create a case for every week
Or other age category
E.g.:
AGGREGATE
/OUTFILE=*
/BREAK=newsub WEEK
/ infgazm2= N(infgazem) /infgaza2 = N(infgazea) / etc.

49. Voile’ - Development
Each case now summarizes the variables of interest for a given age period for a given subject
Create summary proportional duration and frequency per minute variables as before

50. Individual growth modeling
Conduct developmental analyses within individuals
Regression analyses (within individuals)
Typically linear effects

51. Analysis by individual

52. Analysis techniques Is mean slope different from 0
T-test
Data can be copied from regression output to make a new developmental data file

53. Graphing and binomial tests
Do a significant proportion of subjects show an increase (or decrease) with age?
Binomial test (Hays)
Consistency within variability

54. Consistency across individuals

55. Hierarchical linear modeling
Age is nested within individual
Calculates “mean” slope for group
T-test
Estimates variability around that mean
Chi-square
Assumes normal distribution of slope parameters
Should have many subjects
Level 2 units

56. Specialized programs – WHLM
Ask for OLE output
for each subject
Need to get your data into the program.
Can be tricky

57. Table: Gaze away

58. Review Types of codes and coding
Frequency, duration, and combined approaches
Agreement and reliability
Measures of association between two behaviors
Complete picture may involve both duration and frequency (or combined approaches)
Development
Occurs and should be studied as it occurs in individuals (or individual dyads)

59. Duchenne Smiles (M = 20.95%) Non-Duchenne Smiles (M = 39.29%)
59.8%
46.3%
53.7%
40.3%
22.7%
30.2%
27.5%
20.5%
Non-Duchenne Smiles
(M = 39.29%)
72.5%
69.8%
Non-Smiles
(M = 39.76%)
77.3%
79.5%

60. Transitional probabilities
Likelihood of one behavior following another
Typically within a modality of actiojn
One code following another within a category