Mixed emotions

A post from the ISPR Presence Community FB page (by Matt Lombard) brings up an interesting question: what is an emotion (human)? How many are there? Is Longing one of them?

It does not appear in the list by Smith:

But it does, in another from Clore, which shows how emotions may be ‘organizing themselves’…:

Clore, G. L., & Schnall, S. (2005). The influence of affect on attitude. In D. Albarracin, B. T. Johnson, & M. P. Zanna (Eds.), The handbook of attitudes (pp. 437-489). Mahwah, New Jersey: Lawrence Erlbaum Associates.

Shaver, P. R., Wu, S., & Schwartz, J. C. (1992). Cross-cultural similarities and differences in emotion and its representation. In M. S. Clark (Ed.), Emotion. Review of personality and social psvchology (pp. 175-212). Thousand Oaks, CA: Sage.

Smith, H., & Schneider, A. (2009). Critiquing Models of Emotions. Sociological Methods & Research, 37(4), 560-589.

Latent change score model in Mplus

A similar post was done for R, and here's an Mplus code; requests for such simple code come along periodically.
It is for 2 waves only, with 1 indicator latents (and with measurement error estimated for both 'cause' and 'effect' sets of variables: yes, it runs!), and for observed variables only. It was posted on SEMNET, and there is a lot more on this 'change modeling' (another posting on Semnet sends to lots of materials, full texts too).

Try it on your data (no changes needed: just add your own variable names in DEFINE), and let me know what you see in your data, in one of mine I saw the SIGN of the effect flipping when using latents!!!

1. With LVs

  DEFINE:
  X1 = YourXatTime1;
      X2 = YourXatTime2;
  Y1 = YourYatTime1;
      Y2 = YourYatTime2;

  MODEL:
  !X part
  ! first create LV behind each observed indicator:
  ! time 1
  LX1 by X1; ! define new 1 indicator LV
  [X1@0] ; ! set the intercept of the X1 to 0 to identify/define the meal of its latent
  X1 (measerrX);! name the residual error to be set equal across time and estimated
  ! time 2
  LX2 by X2; ! define new 1 indicator LV
  [X2@0] ; ! set the intercept of the X1 to 0 to identify/define the meal of its latent
  X2 (measerrX);! name the residual error to be set equal across time and estimated

  LCS21X by LX2@1; !defined by the 2nd variable
  LX2 @0; !residual variance of 2nd measure @0: note this is true residual, with measurement
  LCS21X ON LX1;  !PROPORTIONAL-GROWTH estimated: can omit it too, by adding ! or adding @0:
  [LX2 @0] ; !intercepts of 2nd measure @0
  LX2 on LX1@1; !autoregressive path set @1

  !Y part
  ! first create LV behind each observed indicator:
  ! time 1
  LY1 by Y1; ! define new 1 indicator LV
  [Y1@0] ; ! set the intercept of the X1 to 0 to identify/define the meal of its latent
  Y1 (measerrY);! name the residual error to be set equal across time and estimated
  ! time 2
  LY2 by Y2; ! define new 1 indicator LV
  [Y2@0] ; ! set the intercept of the X1 to 0 to identify/define the meal of its latent
  Y2 (measerrY);! name the residual error to be set equal across time and estimated

  LCS21Y by LY2@1; !defined by the 2nd variable
  LY2 @0; !residual variance of 2nd measure @0: note this is true residual, with measurement
  LCS21Y ON LY1;  !PROPORTIONAL-GROWTH estimated: can omit it too, by adding ! or adding @0:
  [LY2 @0] ; !intercepts of 2nd measure @0
  LY2 on LY1@1; !autoregressive path set @1

  ! bivariate section

  LCS21Y ON LCS21X; ! CHANGE IN X LEADING TO CHANGE IN Y
  ! COVARIANCES BETWEEN EXOGENOUS VARIABLES ARE ADDED BY DEFAULT: YOU MAY WANT TO DEFLECT TH
  ! LX1 WITH LY1@0 ;
  ! BE PREPARED TO MAKE THESE @0 IT MAY NOT RUN OTHERWISE
  LCS21X @0;
  LCS21Y @0;
   OUTPUT: TECH1 TECH4 stand ;

2. With observed variables:

DEFINE:
X1 = YourXatTime1;
    X2 = YourXatTime2;
Y1 = YourYatTime1;
    Y2 = YourYatTime2;

MODEL:
!X part

LCS21X by X2@1; !defined by the 2nd variable
X2 @0; !residual variance of 2nd measure @0: note this is true residual, with measurement error of X1 partialled out
LCS21X ON X1;  !PROPORTIONAL-GROWTH estimated: can omit it too, by adding ! or adding @0: different models!!!
[X2 @0] ; !intercepts of 2nd measure @0
X2 on X1@1; !autoregressive path set @1

!Y part

LCS21Y by Y2@1; !defined by the 2nd variable
Y2 @0; !residual variance of 2nd measure @0: note this is true residual, with measurement error of X1 partialled out
LCS21Y ON Y1;  !PROPORTIONAL-GROWTH estimated: can omit it too, by adding ! or adding @0: different models!!!
[Y2 @0] ; !intercepts of 2nd measure @0
Y2 on Y1@1; !autoregressive path set @1

! bivariate section

LCS21Y ON LCS21X; ! CHANGE IN X LEADING TO CHANGE IN Y
! COVARIANCES BETWEEN EXOGENOUS VARIABLES ARE ADDED BY DEFAULT: YOU MAY WANT TO DEFLECT THEM INITIALLY
! X1 WITH Y1@0 ;
! BE PREPARED TO MAKE THESE @0 IT MAY NOT RUN OTHERWISE
!LCS21X @0; 
!LCS21Y @0; 
 OUTPUT: TECH1 TECH4 stand ;

The science of cause and effect

An 'introduction' to causality for high schoolers, developed at the invitation of the Connecticut Academy of Science and Engineering, to contribute to their NIE (News in Education) Hartford Courant activities offerings (available on the Hartford Courant site, see http://bit.ly/causalityscience ). As with many recent 'ideas of mine' this flows directly from the wisdom of Judea Pearl (see some recent insights of his on his blog).
Comments and suggestions are welcome!