Getting the most out of interactive and developmental data

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Getting the most out of interactive and
developmental data

• Daniel Messinger
• 5.2001

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Notes

• set workspace10000.
• If ((episode episode_lag1) and (SN   SN_lag1)
  and (sysmis(parentface))) parentfacelag(parentfac
  e).
• exe.

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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

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Topics

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

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Coding

• Types of codes
• Objective vs. socially recognizable
• Type of coding
• Frequency, duration, combined approaches
• Agreement/Reliability
• Percent agreement, Cohens Kappa, intra-class
  correlation

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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

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Type of coding

• Duration
• Frequency
• Combined approaches
• Agreement/Reliability
• Agreeing on what we saw

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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

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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

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Duration
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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

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Duration agreement statistics

• Proportion agreement
• Agreement / (Agreement Disagreement)
• Observed agreement
• Cohens Kappa
• (Observed agreement Expected agreement) / (1
  Expected agreement)
• Expected agreement
• Sum of product of marginals
• Bakeman Gottman

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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

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Frequency
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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

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Frequency and DurationPros and cons

• Duration is a relatively stable measure of whats
  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

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Mixed approaches

• Duration of smiling in a given period of
  interaction initiated by infant
• Duration in that its a total time measure
• But frequency in that its onset of infant action
• Calculate both types of agreement

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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

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Measures of association between two behaviors

• Group level analysis
• Individual level analysis
• Duration and frequency approaches

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Example

• Infant gaze direction
• At mothers face or away
• Mother smile
• Yes or no
• Coded continuously in time

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SPSS (10.0)

• VALUE LABELS m12
• 1.00000000000000 "Mother Not Smiling"
• 2.00000000000000 "Mother Smiling"

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General duration variable

• create
• /leadweeKlead(week 1) /LEADSECsLEAD(SECS 1).
• EXECUTE.
• IF (weekleadweek) DurationLEADSECS-SECS.
• IF SYSMIS(DUR) DUR0.
• EXECUTE.

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Analysis of entire group

• Weight by duration, then . . .

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Analysis of entire group

• Doesnt 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

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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

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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

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Specific variable duration

• IF (infgaze1) infgazeMDuration.
• IF (infgaze2) infgazeADuration.
• IF (momsmile1) M1Duration.
• IF (momsmile2) M2Duration.
• Execute.

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Duration of two co-occurring behaviors

• How long do infants gaze at mother when she is
  smiling?

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Duration of two co-occurring behaviors

• IF (infgaze1 momsmile1) GMM1Duration.
• IF (infgaze1 momsmile2) GMM2Duration.
• IF (infgaze2 momsmile1) GAM1Duration.
• IF (infgaze2 momsmile2) GAM2Duration.
• EXECUTE.

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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).

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Combined duration and frequency approach

• CREATE
• /momsml_1LAG(momsml 1) /infgaz_1LAG(infgaz 1).
• IF (infgaz2 infgaz_12 momsml2)
  GAM2Fduration.
• IF (infgaz1 infgaz_11 momsml2)
  GMM2Fduration.
• IF (infgaz2 infgaz_12 momsml1)
  GAM1Fduration.
• IF (infgaz1 infgaz_11 momsml1)
  GMM1Fduration.
• EXECUTE.

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Aggregate Summarizing data for analysis

• Aggregate over subject for overall effects
• Aggregate over subject and time period for
  developmental analyses

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Aggregate creates new variables
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Summarizes over time-linked cases

• Summary measures
• Number of cases for frequency
• Sum of values for duration

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Ouch!

• AGGREGATE
• /OUTFILE
• /BREAKnewsub
• / 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).

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Voile
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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

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Creating durational proportions

• Creating durational proportions
• COMPUTE GMM2PGmm2_3/(GMM2_3GAM2_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

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Creating durational proportions

• Creating durational proportions
• COMPUTE GMM2PGmm2_3/M2_3.
• Note M2_3 (total time mother is smiling) can be
  created during aggregation or computed as
  GMM2_3GAM2_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

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Analyses, finally
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Results

• Look at results subject by subject by graphing

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Duration and frequency

• Can tell you the same thing about an interaction
• Or different things

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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

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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

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Development Conceptual

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

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Development - Practical

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

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Development - How to

• Go back to the original file
• Create a case for every week
• Or other age category
• E.g.
• AGGREGATE
• /OUTFILE
• /BREAKnewsub WEEK
• / infgazm2 N(infgazem) /infgaza2 N(infgazea)
  / etc.

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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

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Individual growth modeling

• Conduct developmental analyses within individuals
• Regression analyses (within individuals)
• Typically linear effects

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Analysis by individual
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Analysis techniques

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

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Graphing and binomial tests

• Do a significant proportion of subjects show an
  increase (or decrease) with age?
• Binomial test (Hays)
• Consistency within variability

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Consistencyacross individuals
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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

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Specialized programs WHLM
Ask for OLE output for each subject

• 1 0.54269 -0.02004
• 2 0.41946 -0.01507
• 3 0.50880 -0.02033
• 4 0.45630 -0.01529
• 5 0.99627 -0.04153
• 6 0.67704 -0.02057
• 7 0.34434 -0.00026
• 8 0.73655 -0.02059
• 9 0.38579 -0.00123
• 10 0.51995 -0.01611

• Need to get your data into the program.
• Can be tricky

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Table Gaze away
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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)

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Duchenne Smiles (M 20.95)
40.3
59.8
53.7
46.3
22.7
27.5
30.2
20.5
Non-Duchenne Smiles (M 39.29)
Non-Smiles (M 39.76)
77.3
79.5
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Transitional probabilities

• Likelihood of one behavior following another
• Typically within a modality of actiojn
• One code following another within a category