# What is a causal analysis

## Causal analysis

In causal analysis, hypothesis testing methods are used, which are called structural equation methodology, covariance structure analysis (analysis of variance) or LISREL modeling (LISREL).

The combination of factor analysis for testing measurement hypotheses with a structural equation model for testing dependency hypotheses is characteristic of the models of causal analysis (cf. Homburg / Hildebrandt, 1998, p. 18). The causal analysis is thus an analysis technique that goes far beyond the traditional pjadanalysis. It connects regression or. Path analysis (reflection analysis) with factor analysis elements (factor analysis). »This connection makes it possible not only to examine a network of theoretically derived causal relationships, namely direct, indirect or reciprocal relationships, between relevant variables in one step, but also to include different measurement concepts for the linked variables in the analysis «(Nieschlag / Dichtl / Hörschgcn, 1997, p. 787).

It is essential for this approach that it is possible to identify and control measurement errors which can falsify the analysis of suspected causal relationships (causality). The approach "thus takes into account the fact that many of the variables of interest in marketing research represent theoretical constructs, i.e. cannot be observed directly, but can only be empirically recorded by assigning indicators" (Nieschlag / Dichtl / Hörschgen, 1997, p. 787t).

The causal analysis process essentially consists of the following steps (cf. Hildebrandt, 1992a, p. 525):

1. A verbally formulated causal theory, consisting of “if-then” or “the more” statements, is translated into a path diagram using variable relationships (path analysis). In addition, a measurement model is assumed for the latent constructs. The correctness of the assumed relationships between the variables must be reflected by the path diagram.

2. The theoretical model at hand is represented by a system of (linear) equations.

3. The structure of this system is tested using empirical data. The covariance structure analysis is used to examine whether the relationships assumed in the path diagram, the estimated model parameters, are consistent with the empirical data. "An assumed causal structure is not regarded as falsified or accepted to the extent to which the model reproduces the covariances of the data" (Hildebrandt, 1992a, p. 526).

When analyzing the relationships between variables (e.g. in models of consumer behavior), a distinction must be made between causal and merely associative relationships. A causal relationship between two variables is understood to be a relationship such that the changes in one variable y can be explained by the changes in another variable x, and the change in x is therefore the cause of the change in y with a probability bordering on certainty. In contrast, there is an associative relationship when two variables vary together (in the same direction or in opposite directions). The presumption of causality with regard to the variables x and y can generally not be rejected if • x and y covariate, • x and y are linked asymmetrically over time, • x and y are not linked via third variables and • a theoretical justification for a connection between x and y exists. Causal relationships are therefore always associative, while the reverse is not necessarily permissible. One way to empirically examine the causality of relationships is to test a causal hypothesis through experiment. However, because various restrictions, such as the restriction to mostly bivariate variable relationships and the costs of complex test arrangements, must be taken into account and, in addition, experiments with regard to the (internal and external) validity of their results must be viewed critically, applications with regard to checking causal hypotheses remain controversial . These problems are largely circumvented in the context of a causal analysis. The main idea is to separate out the effects of third variables on the causal relationship by means of a statistical procedure. The input data can be of an experimental or non-experimental nature; This means, for example, that data from time series or panel surveys can also be used. In the context of the causal analysis, an attempt is made retrospectively - in contrast to the experiment - to arrive at a causal interpretation via the relationship structure of variables in a model. In order to be able to depict causal structures based on hypotheses in a model, two things are required: • Graphic representation of the structure in a structure diagram, • Formulation of the structure in a system of linear structural equations (structural equation methods). The latter then allows the model parameters to be estimated with the help of the collected data with the aim of checking the underlying theory and its premises and developing a measure of the quality of the model. Regression analysis and path analysis are important methods of causal analysis. Literature: Backhaus, KJErichson, BJPlinke, WJ Weiber, R., Multivariate Analysis Methods, 6th edition, Berlin et al. 1990, pp.221 ff. Bagozzi, RP, Causal Models in Marketing, New York 1980. Hildebrandt, L., Confirmatory Analysis of models of consumer behavior, Berlin 1983.

The combination of factor analysis for testing measurement hypotheses with a structural equation model for testing dependency hypotheses is characteristic of the models of causal analysis (cf. Homburg / Hildebrandt, 1998, p. 18). The causal analysis is thus an analysis technique that goes far beyond the traditional pjadanalysis. It connects regression or. Path analysis (reflection analysis) with factor analysis elements (factor analysis). »This connection makes it possible not only to examine a network of theoretically derived causal relationships, namely direct, indirect or reciprocal relationships, between relevant variables in one step, but also to include different measurement concepts for the linked variables in the analysis «(Nieschlag / Dichtl / Hörschgcn, 1997, p. 787).

It is essential for this approach that it is possible to identify and control measurement errors which can falsify the analysis of suspected causal relationships (causality). The approach "thus takes into account the fact that many of the variables of interest in marketing research represent theoretical constructs, i.e. cannot be observed directly, but can only be empirically recorded by assigning indicators" (Nieschlag / Dichtl / Hörschgen, 1997, p. 787t).

The causal analysis process essentially consists of the following steps (cf. Hildebrandt, 1992a, p. 525):

1. A verbally formulated causal theory, consisting of “if-then” or “the more” statements, is translated into a path diagram using variable relationships (path analysis). In addition, a measurement model is assumed for the latent constructs. The correctness of the assumed relationships between the variables must be reflected by the path diagram.

2. The theoretical model at hand is represented by a system of (linear) equations.

3. The structure of this system is tested using empirical data. The covariance structure analysis is used to examine whether the relationships assumed in the path diagram, the estimated model parameters, are consistent with the empirical data. "An assumed causal structure is not regarded as falsified or accepted to the extent to which the model reproduces the covariances of the data" (Hildebrandt, 1992a, p. 526).

When analyzing the relationships between variables (e.g. in models of consumer behavior), a distinction must be made between causal and merely associative relationships. A causal relationship between two variables is understood to be a relationship such that the changes in one variable y can be explained by the changes in another variable x, and the change in x is therefore the cause of the change in y with a probability bordering on certainty. In contrast, there is an associative relationship when two variables vary together (in the same direction or in opposite directions). The presumption of causality with regard to the variables x and y can generally not be rejected if • x and y covariate, • x and y are linked asymmetrically over time, • x and y are not linked via third variables and • a theoretical justification for a connection between x and y exists. Causal relationships are therefore always associative, while the reverse is not necessarily permissible. One way to empirically examine the causality of relationships is to test a causal hypothesis through experiment. However, because various restrictions, such as the restriction to mostly bivariate variable relationships and the costs of complex test arrangements, must be taken into account and, in addition, experiments with regard to the (internal and external) validity of their results must be viewed critically, applications with regard to checking causal hypotheses remain controversial . These problems are largely circumvented in the context of a causal analysis. The main idea is to separate out the effects of third variables on the causal relationship by means of a statistical procedure. The input data can be of an experimental or non-experimental nature; This means, for example, that data from time series or panel surveys can also be used. In the context of the causal analysis, an attempt is made retrospectively - in contrast to the experiment - to arrive at a causal interpretation via the relationship structure of variables in a model. In order to be able to depict causal structures based on hypotheses in a model, two things are required: • Graphic representation of the structure in a structure diagram, • Formulation of the structure in a system of linear structural equations (structural equation methods). The latter then allows the model parameters to be estimated with the help of the collected data with the aim of checking the underlying theory and its premises and developing a measure of the quality of the model. Regression analysis and path analysis are important methods of causal analysis. Literature: Backhaus, KJErichson, BJPlinke, WJ Weiber, R., Multivariate Analysis Methods, 6th edition, Berlin et al. 1990, pp.221 ff. Bagozzi, RP, Causal Models in Marketing, New York 1980. Hildebrandt, L., Confirmatory Analysis of models of consumer behavior, Berlin 1983.

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