Nursing Research Analysis And Causal Modeling

Research Analysis and Casual Modeling

What Is Causal Modeling, Casual Model ,Stages of Causal Models,Structure of Causal Models,Purposes of Model,Recursive or Non-Recursive Model,Issues In Models ,Data Requirements For Path Analysis,Benefits of Model.

What Is Causal Modeling

    Causal modeling refers to a class of theoretical and methodological techniques for examining cause-and-effect relationships, generally with nonexperimental data. 

    Path analysis, structural equation modelling, covariance structure modeling, and LISREL modeling have slightly different meanings but often are used interchangeably with the term causal modelling. 

    Path analysis usually refers to a model that contains observed variables rather than latent (unobserved) variables and is analyzed with multiple regression procedures. The other three terms generally refer to models with latent variables with multiple empirical indicators that are analyzed with iterative programs such as LISREL or EQS. w

    A common misconception is that these models can be used to establish causality with nonexperimental data; however, statistical techniques cannot overcome restrictions imposed by the study's design. Nonexperimental data provide weak evidence of causality regardless of the analysis techniques applied.

Casual Model 

    A causal model is composed of later concepts and the hypothesized relationship! among those concepts. The researcher constructs this model a priority based on theoretical or research evidence for the direction and sign of the proposed effects. 

    Although the model can be based on the observed correlations in the sample, this practice is not recommended. Empirically derived models capitalize on sample variations and often contain paths that are not theoretically defensible; Findings from empirically constructed models should not be interpreted without replication in another sample.

 Stages of Causal Models

    Most causal models contain two or more stages; they have independent variables, one or more mediating variables, and the final outcome variables. Because the mediating variables act as both independent and dependent variables, the terms exogenous and endogenous are used to describe the latent variables. Exogenous variables are those whose causes are not represented in the model; the causes of the endogenous variables are represented in the model.

Structure of Causal Models

    Causal models contain two different structures. The measurement model includes the latent variables, their empirical indicators (observed variables), and associated error variances. 

    The measurement model is based on the factor analysis model. A respondent's position on the latent variables is considered to cause the observed responses on the empirical indicators, so arrows point from the latent variable to the empirical indicator. 

    The part of the indicator that cannot be explained by the latent variable is the error variance generally due to measurement.

 Purposes of Model

    The structural model specifies the relationships among the latent concepts and is based on the regression model. Each of the endogenous variables has an associated explained variance, similar to R in multiple regression. The paths between latent variables represent hypotheses about the relationship between the variables. 

    The multistage nature of causal models allows the researcher to divide the total effects of one latent variable on another into direct and indirect effects. Direct effects represent one latent variable's influence on another that is not transmitted through a third latent variable. 

    Indirect effects are the effects of one latent variable that are transmitted through one or more mediating latent variables. Each latent variable can have many indirect effects but only one direct effect on another latent variable.

Recursive or Non-Recursive Model

    Causal models can be either recursive or non recursive . Recursive models have arrows that point in the same direction; there are no feedback loops or reciprocal causation paths. Non recursive models contain one or more feedback loops or reciprocal causation paths. Feedback loops can exist between latent concepts or error terms.

 Issues In Models 

    An important issue for non-recursive models is identification status, Identification status refers to the amount of information (variances and covariances) available, compared to the number of parameters that are to be estimated. 

    If the amount of information equals the number of parameters to be estimated, the model is "just identified." If the amount of information exceeds the number of parameters to be estimated, the model is "overidentified." In both cases, a unique solution for the parameters can be found. With the use of standard conventions, recursive models are almost always overidentified. 

    When the amount of information is less than the number of parameters to be estimated, the model is " underidentified " or "unidentified," and a unique solution is not possible. Non recursive models are under identified unless instrumental latent variables (a latent variable for each path that has a direct effect on one of the two latent variables in the reciprocal causation relationship but only an indirect effect on the other latent variable) can be specified. 

    Causal models can be analyzed with standard multiple regression procedures or structural equation analysis programs, such as LISREL or EQS (see "Structural Equation Modeling"). Multiple regression is appropriate when each concept is measured with only one empirical indicator. 

    Path coefficients (standardized regression coefficients, or ẞs) are estimated by regressing each endogenous variable on the variables that are hypothesized to have a direct effect on it. Fit of the model is calculated by comparing total possible explained variance for the just identified model with the total explained variance of the proposed overidentified model ( Pedhazur , 1982). 

Data Requirements For Path Analysis

    Data requirements for path analysis are the same as those for multiple regression: 

(a) interval or near-interval data for the dependent measure

(b) interval, near-interval, or dummy-, effect, or orthogonally coded categorical data for the independent measures

(c) 5 to 10 cases per independent variable.

Assumptions of multiple regression must be met.

 Benefits of Model

    In summary, causal modeling techniques provide a way to more fully represent the complexities of the phenomenon, to test theoretical models specifying causal flow, and to separate the effects of one variable on another into direct and indirect effects. 

    Although causal modeling cannot be used to establish causality, it provides information on the strength and direction of the hypothesized effects. Thus, causal modeling enables investigators to explore the process by which one variable might affect another and to identify possible points for intervention.