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Getting the Most Out of Interactive and Developmental Data

Learn how to analyze interactive and developmental data effectively, including coding types, agreement and reliability measures, duration and frequency approaches, and measures of association.

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Getting the Most Out of Interactive and Developmental Data

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

  2. Basics • Know your phenomena • 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 • 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 • 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 • 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 • 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" 2.00000000000000 "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. EXECUTE.

  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. 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 Constructing variables

  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 Creating durational proportions

  38. Analyses, finally

  39. Results • Look at results subject by subject by graphing

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

  41. 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 Creating frequency per minute

  42. 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

  43. 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

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

  45. 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.

  46. 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

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

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