1. Introduction to sequential analysis Brian Baucom, Ph.D.
DEPARTMENT OF PSYCHOLOGY | THE UNIVERITY OF UTAH

2. Operationalizing temporal processes in close relationships
Overview
Conceptualizing emotional and behavioral processes in close relationships
Operationalizing temporal processes in close relationships
Important considerations in lagged models
Over-time APIM
Repeated-measures APIM
Cross-lagged APIM

3. Research foci and clinical orientation
Conduct basic and intervention research on overlapping relational, mental, and physical health outcomes
Research driven by couple therapy perspective
Small couple therapy clinic
Karney & Bradbury, 1995; Robles et al., 2013; Whisman & Uebelacker, 2003

4. polarization model Differences between partners
Regulation
Experiences and perceptions of differences
Strong emotional reactivity & impaired emotion regulation maintain & exacerbate the mutual trap and vilification
Mutual trap
Vilification
Reactivity
Relationship distress
CLOSE Lab

5. Types of data that could be used to observe emotional, cognitive, and behavioral processes
Audio and video
Couple interactions in lab & at-home
Psychotherapy sessions
Psychophysiology
Couple interactions
Daily life
Self-report
Standard questionnaires
Daily diary
Ecological momentary assessment

6. Operationalizing temporal processes in close relationships
Processes unfold over time
Processes involve intrapersonal and interpersonal components
Many processes are contextually dependent

7. Cross-sectional Actor-Partner Interdependence ModelX1 Y1 E1 p p X2 Y2 E2 a
Outcome = Y
Predictor = X
Within-partner association = a
Actor effect
Between-partner association = p
Partner effect

8. APIM over time
Lagged associations
Link between predictor (measured at earlier time point) and outcome (measured at later time point)
Major goal is to predict change in outcome
Possible with simultaneous measurement of 2 or more variables from both members of a dyad on 2 or more occasions

9. Stability, frequency, & intensity of measurement
Stability of outcome
Measures need to change over from one assessment point to the next
Frequency and intensity of measurement needs to match phenomenon
Determining “correct” time lag is a major on-going area of research
Safest approach is to sample over smallest time interval with the greatest frequency possible

10. Measurement invariance
Measures assumed to assess the same underlying construct in the same way at each time point
See Brown, 2006 for methods of assessing measurement invariance
Want to demonstrate invariance in factor loadings
Ensures equivalent rank ordering over time

11. Ex: husband and wife ambulatory heart rate data
We are interested knowing how spouse’s heart rates are related to one another throughout the day.
Spouses wore Actiheart biosensors that continuously recorded heart rate during all waking hours over 7 consecutive days
128 Hz ECG waveform data was down-sampled to 1-minute epochs for analysis
Case A, 210 minutes of HR data per spouse (N = 420 data points)
Case B, 60 minutes of HR data per spouse (N = 120 data points)

12. HRH, time =t-1 HRH, time = t E1 HRW, time = t-1 HRW, time = t E2
Lagged APIM a
HRH, time =t-1
HRH, time = t E1 p p
HRW, time = t-1
HRW, time = t E2 a

13. Data organization

14. Model results

15. Vector flow field diagram of results

16. Vector flow field diagram of results

17. Comparing vfds

18. Creating A vfd

19. Creating a vfd, cont.

20. Extending the model: does it matter if spouses are in the same place?

21. Extending the model, 2: vocal entrainment and emotional bond
Clientf0
Therapistf0
Therapistf0
Clientf0
Bond
Talk turn = t-1
Talk turn = t

22. Results - interview Bond Clientf0 Therapistf0 Therapistf0 Clientf0
n.s.
n.s.
Clientf0
Therapistf0
.32***
Therapistf0
Clientf0
.3***
.02**
-8.55**
Bond

23. Results - intervention
Bond
-.05***
n.s.
Clientf0
Therapistf0
.34***
Therapistf0
Clientf0
.3***
-.07***
n.s.
Bond

24. VFDs Low emotional bond High emotional bond Client f0 Therapist f0
Stronger
Client f0
Strength of association
We currently only have a sample of 13 individuals for whom we have processed complete data, so these results are very preliminary. But really interesting trends seem to be emerging.
First, descriptives show that both patient and therapist tend to express more emotion in their voice during the intervention itself, compared with during the assessment.
The bottom table shows results from the regression model. We included patient and therapist f0 at both assessment and intervention in the model to predict bond. Note that we excluded age, gender, and type of treatment from this model simply because we don’t have the sample size to include that many predictors. Again these are very preliminary results. However, the first thing to notice is that therapist f0 at assessment still does not seem to predict bond, and patient f0 at assessment still seems to be an important predictor of bond, and a strong one, even when f0 at intervention is included in the model. Again, higher patient vocally encoded emotional arousal at assessment is associated with lower ratings of bond.
During the intervention itself, suddenly therapist f0 matters, and seems to matter a lot. The more the therapist expresses emotion in her voice, the strong bond tends to be.
But the most interesting trend that seems to be emerging from these data is that the relationship between patient f0 and bond is flipped during intervention. Suddenly, higher vocal expression of emotion predicts higher bond, as we originally hypothesized. So for the patient, higher vocally encoded emotional arousal at assessment is bad for bond, but during intervention it is good.
Weaker
Therapist f0

25. VFDs are helpful plots for presenting results of lagged APIMs
summary
Lagged models assess associations from previous time point to current time point.
Lagged APIM results need to be interpreted with respect to time rather than to the mean.
VFDs are helpful plots for presenting results of lagged APIMs
Helps present information in a combined fashion

26. resources Additional reading about APIM over time
Kashy, D.A., & Donnellan, M.B. (2012). Conceptual and methodological issues in the analysis of data from dyads and groups. In K. Deaux and M. Snyder (Eds.), The Oxford Handbook of Personality and Social Psychology (pp. 209 – 238). New York: Oxford.
Assessing measurement invariance over time
Brown, T.A. (2006). Confirmatory factor analysis for applied research. New York: Guilford.
Interpreting lagged APIMs
Perry, N. S., Baucom, K. J., Bourne, S., Butner, J., Crenshaw, A. O., Hogan, J. N., ... & Baucom, B. R. (2017). Graphic methods for interpreting longitudinal dyadic patterns from repeated-measures actor–partner interdependence models. Journal of Family Psychology, 31, 592.
VFD on-line tool

28. Biological mechanisms: Surrogate endpoints Clinical endpoints
Enduring vulnerabilities:
Behavioral mechanisms
Adaptive processes
Marital quality
Marital stability
Cognitive mechanisms
Psychological symptoms
Psychopathology
Stressful events