Page 75 - Encyclopedia of Nursing Research

42 n CAUSAL MoDELInG

are used to describe the latent variables.
Causal Modeling Exogenous variables are those whose causes
C are not represented in the model; the causes
of the endogenous variables are represented
Causal modeling refers to a class of theo- in the model.
retical and methodological techniques for Causal models contain two different
examining cause-and-effect relationships, structures. The measurement model includes
generally with nonexperimental data. Path the latent variables, their empirical indicators
analysis, structural equation modeling, (observed variables), and the associated error
covariance structure modeling, and LISREL variances. The measurement model is based
modeling have slightly different meanings on the factor analysis model. A respondent’s
but often are used interchangeably with the position on the latent variables is consid-
term causal modeling. Path analysis usu- ered to cause the observed responses on the
ally refers to a model that contains observed empirical indicators, so arrows point from
variables rather than latent (unobserved) the latent variable to the empirical indica-
variables and is analyzed with multiple tor. The part of the indicator that cannot be
regression procedures. The other three terms explained by the latent variable is the error
generally refer to models with latent vari- variance generally due to measurement.
ables with multiple empirical indicators that The structural model specifies the rela-
are analyzed with iterative programs such as tionships among the latent concepts and is
LISREL or EQS. A common misconception is based on the regression model. Each of the
that these models can be used to establish endogenous variables has an associated
causality with nonexperimental data; how- explained variance, similar to R in mul-
2
ever, statistical techniques cannot overcome tiple regression. The paths between latent
restrictions imposed by the study’s design. variables represent hypotheses about the
nonexperimental data provide weak evi- relationship between the variables. The mul-
dence of causality regardless of the analysis tistage nature of causal models allows the
techniques applied. researcher to divide the total effects of one
A causal model is composed of latent latent variable on another into direct and
concepts and the hypothesized relationships indirect effects. Direct effects represent one
among those concepts. The researcher con- latent variable’s influence on another that is
structs this model a priori on the basis of the- not transmitted through a third latent var-
oretical or research evidence for the direction iable. Indirect effects are the effects of one
and sign of the proposed effects. Although latent variable that are transmitted through
the model can be based on the observed cor- one or more mediating latent variables. Each
relations in the sample, this practice is not latent variable can have many indirect effects
recommended. Empirically derived models but only one direct effect on another latent
capitalize on sample variations and often variable.
contain paths that are not theoretically defen- Causal models can be either recursive or
sible; findings from empirically constructed nonrecursive. Recursive models have arrows
models should not be interpreted without that point in the same direction; there are no
replication in another sample. feedback loops or reciprocal causation paths.
Most causal models contain two or more nonrecursive models contain one or more
stages; they have independent variables, one feedback loops or reciprocal causation paths.
or more mediating variables, and the final out- Feedback loops can exist between latent con-
come variables. Because the mediating vari- cepts or error terms.
ables act as both independent and dependent An important issue for nonrecursive
variables, the terms exogenous and endogenous models is identification status. Identification