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Cross-lagged Panel Correlation (CLPC). David A. Kenny. Example. Depression and Marital Satisfaction measured at two points in time. Four measured variables S 1 , S 2 , D 1 , and D 2. Causal Assumptions.

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Presentation Transcript
example
Example
  • Depression and Marital Satisfaction measured at two points in time.
  • Four measured variables S1, S2, D1, and D2.
causal assumptions
Causal Assumptions
  • Most analyses of longitudinal variables explain the correlation between two variables as being due to the variables causing each other: S  D and D  S.
  • CLPC starts by assuming that the correlation between variables is not due to the two variables causing one another.
  • Rather it is assumed that some unknown third variable, e.g., social desirability, brings out about the relationship.
model of spuriousness
Model of Spuriousness
  • Assume that a variable Z explains the correlation between variables at each time. The variable Z is changing over-time.
  • The model is under-identified as a whole, but the squared correlation between Z1 and Z2 is identified as rD1S2rD2S1 /(rD1S1rD2S2).
ruling out spuriousness
Ruling out Spuriousness
  • The strategy developed by Kenny in the 1970s in a series of paper is to assume stationarity.
  • Requires at least three variables measured at each time.
  • Stationarity
    • Define how much variance for a given a given variable, say D, is available to correlate.
    • Define the ratio of variance, time 2 divided by time 1.
stationarity
Stationarity
  • Define how much variance for a given a given variable, say XA, is available to correlate.
  • Define the ratio of variance, time 2 divided by time 1 for XA, to be denoted as kA2.
  • Given stationarity, the covariance between XA and XB at time 2 equals the time 1 covariance times kAkB.
  • Also C(XA1,XB2)kB = C(XA2,XB1)kA where C is a covariance.
basic strategy
Basic Strategy
  • Test for stationarity of cross-sectional relationships.
    • df = n(n – 3)/2
  • If met, test for spuriousness.
      • df = n(n – 1)/2
  • Mplus syntax can be downloaded at www.handbookofsem.com/files/ch09/index.html
example data
Example Data

Dumenci, L., & Windle, M.  (1996). Multivariate Behavioral Research, 31, 313-330.

Depression with four indicators (CESD)               PA: Positive Affect (lack thereof)              DA: Depressive Affect

SO: Somatic Symptoms              IN: Interpersonal Issues Four times separated by 6 months

Use waves 1 and 2 for the example 433 adolescent females Age 16.2 at wave 1  

example1
Example
  • Test for stationarity of cross-sectional relationships:
    • c2(2) = 5.186, p = .075
  • Because stationarity is met, test for spuriousness:
      • c2(6) = 2.534, p = .865
  • Evidence consistent with spuriousness.
  • Mplus syntax can be downloaded at
  • www.handbookofsem.com/files/ch09/index.html
why is this strategy not adopted
Why is this strategy not adopted?
  • Most researchers are interested in estimating a causal effect, not in showing you do not need to estimate any causal effects.
  • Also, CLPC was initially proposed as way of determining causal effects, not as a way of testing of spuriousness.
in principle
In principle…
  • Researchers should show that spuriousness can plausibly explain the covariation in their data.
  • CLPC has a use.
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