Causal Research (Explanatory research)

In order to judge the scope and nature of causality, we conduct causality study, also known as explanatory research. Causal relationship studies can be carried out to assess the impact of specific changes on existing norms and processes.

Cause-and-effect studies focus on the analysis of the situation and specific problems to explain the pattern of relationships between variables. Experiments are the most common main data collection method in causal research design studies

It is only existence of certain cause evidence that we can confirm the existence of causality. Evidence of causality has three important elements.

Time series. The reason must occur before the effect. For example, if an increase has already begun before the brand is rebuilt, it is not appropriate to attribute an increase in re-pricing sales to a brand rebuild.

2. Associated changes Changes between the two variables must be systematic. For example, if a company does not change its employee training and development practices, training and development of employees will not lead to changes in customer satisfaction.

False association The common cause between association causes and results must be real, not just other factors. In other words, there should be no "third" factor related to causality, impact, and impact.

The following table compares the main features of causality study with exploratory and descriptive research design. [1]

Determine the nature of the impact of workflow redesign on employee motivation level

Causal relationship research can play an important role in identifying the reason behind a broad process and evaluating the impact of changes on existing norms and processes.

This type of research is related to a higher level of internal validity, depending on the choice of system object.

A coincidence in an event can be regarded as a causal relationship. For example, Punxatawney Phil can predict the winter period continuously for five consecutive years, but it is only a rodent with no intelligence and predictability, which is coincidental.

It may be difficult to derive appropriate conclusions based on the result of causal research. This is due to various factors and variables in the social environment. In other words, casualties can infer, but can not prove with high confidence.

In some cases, correlation between the two variables can be effectively established, it can be a challenging task to determine which variables are attributed and which are the consequences.

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[1] Sources: Zikmund, W. G., Babin, J. , Carr, J .; & Griffin, M. (2012) "Business survey method: Use of Qualtrics Print Access Card" Cengage

Causal research is also called interpretive research, and is a study on causality in (research). In order to judge the causal relationship, it is important to observe the change in the variable that causes another variable to change, then measure the change of the other variable. Other confounding effects must be controlled so that they do not distort the results by trying to create the data or by using statistical methods. This type of research is very complex and researchers can never be convinced that other factors will not affect causality. Especially when dealing with people's attitudes and motives. Even if the respondent may not know, there is a deeper psychological factor than usual.

Interpretation Science is interested in explaining why it happens. It focuses on the causal interpretation of the framework. Explanation Science is based on observation of science. On the other hand, observing science can be said to expect explanatory science - alone - there is no purpose for future explanation - observation science is difficult to regard as science. Finally, the technical science is to apply the observation and interpretation results of science to actual work, that is, to develop technology. By the way, technology is not necessarily scientific, it may be descendants of practical technology and experience. It should be considered scientific as far as it depends on the application of science of observation and interpretation