Measure causal relationship research

measure causal relationship research

At GitHub, we frequently use experimental research designs to study the means of establishing causal relationships, but causal inference is hard! the independent variable and measure subsequent changes in the values. Here we investigate the relationship between parental models include 33 confounder variables measured when youth are in the. Correlation is a statistical measure (expressed as a number) that describes of the other event; i.e. there is a causal relationship between the two events. The objective of much research or scientific analysis is to identify the.

Understanding whether or not parental knowledge may be related to youth outcomes after controlling for confounders has important intervention implications, because if an effect is identified, parental knowledge is likely an effective mediator to target in interventions. Several studies have suggested that youth who have parents with high levels of knowledge are less likely to engage in delinquency, to select antisocial peers, and be influenced by antisocial peers Laird et al.

Yet, whether or not the link between knowledge and risky behavior holds once researchers account for many other aspects of the parent-child relationship has not yet been tested.

Parental Monitoring Measurement issues in the literature have clouded the distinction between parental knowledge of youth activities and parental attempts to monitor youth.

The lack of specificity in these constructs has made it difficult to discern the effects of knowledge alone on youth behaviors apart from parent efforts to monitor and other behaviors, such as youth disclosure of information.

This study specifically addresses whether or not parental knowledge is related to youth outcomes once we control for a broad range of confounding variables. We chose to focus on the role of parental knowledge rather than parental monitoring because knowledge is a central construct that links other monitoring-related behaviors to youth outcomes.

Statistical vs. Causal Inference: Causal Inference Bootcamp

By using propensity scores, this analysis explores if the relationship between knowledge and youth outcomes holds regardless of how parents obtain that information and regardless of other aspects of the parent-child relationship. In this framework, we view other behaviors that may lead to knowledge, such as parental monitoring, child disclosure, and supervision as potential confounders. Using propensity score methods increases our confidence that parental knowledge may be causally related to youth outcomes and that increases in knowledge will likely lead to changes in child behavior across different family contexts.

Research questions that explore relationships between knowledge and other behaviors, such as parental monitoring, are fruitful areas for future research but are beyond the scope of the current paper. Confounder Variables Despite the strong correlational evidence that parental knowledge is linked to youth outcomes, researchers have yet to determine if having greater parental knowledge is linked to risky behavior once a broad range of potential confounders are accounted for.

correlation - How do you find causal relationships in data? - Cross Validated

There may be other systematic differences between families with different levels of parental knowledge that are also related to youth outcomes. These confounder variables make it difficult to discern if it is high levels of parental knowledge that are protective against problem behavior, or if the association is driven by other aspects of the parent-child relationship, other aspects of the monitoring process, or pre-existing youth behavior.

measure causal relationship research

Issues of statistical power make it difficult to control for a broad array of confounders using traditional regression or structural equation methods. Although recent correlational studies have included measures of various behaviors related to monitoring in their models e.

Further, even though many studies include earlier measures of a particular youth problem behavior as a control variable, few studies have included measures of multiple problem behaviors. Because behaviors tend to cluster Jessor,it may be important to account for a wide range of youth problem behaviors, rather than one specific behavior.

In this study, we control for 33 confounder variables including aspects of the monitoring process, other aspects of the parent-child relationship, and pre-existing problem behaviors. Research suggests there may be several confounding variables that are related to both parental knowledge and youth outcomes.

Some studies suggest that parent attempts to monitor youth and set rules about youth behavior may lead to parental knowledge and subsequently, youth outcomes Soenens et al. It is difficult to discern from these studies if knowledge itself has a causal effect on youth outcomes, or if it reflects parent attempts to solicit information or high levels of parent-child communication and youth disclosure, as these studies only control for a very limited number of potential confounders.

Other studies have found links between parental knowledge and other parenting characteristics. Parents who are supportive and have warm parent-child relationships are more likely to have high levels of knowledge and lower levels of youth problem behavior Soenens et al.

Thus, it is possible that warm, trusting relationships between parents and youth may explain the association between parental knowledge and youth outcomes. Reciprocal relationships have been found between knowledge and delinquency, suggesting that parental knowledge influences and is influenced by youth behaviors Laird et al.

Thus, another confounder may be earlier engagement in risky behavior. Youth with problem behavior may also have low levels of knowledge, making it difficult to discern whether knowledge is a causal mechanism that protects youth from later antisocial behavior. Random assignment controls for confounders in experimental studies by evenly distributing them on average between treatment groups. However, in observational studies it is not possible to randomize parents into different levels of knowledge.

Conceptually, propensity score techniques allow researchers to estimate the causal effect of knowledge on youth risky behavior as if families were randomly assigned to different levels of knowledge. These models allow researchers to control for a larger, more diverse array of potential confounders than traditional regression methods and the ability to control for these confounders increases our confidence in drawing causal inferences.

measure causal relationship research

However, propensity score techniques have some limitations. Like traditional regression methods, these methods assume that there are no unmeasured confounders. This is a strong assumption that cannot be tested in practice.

However, the more potential confounders that are included in the propensity model, the more plausible the assumption becomes. Thus, it is imperative that researchers measure as many potential confounders as possible.

measure causal relationship research

In addition, the impact of an unmeasured confounder is mitigated if a measured potential confounder is highly correlated with the unmeasured confounder. In addition, a sensitivity analysis see e. Rosenbaum, can be conducted, which attempts to determine how influential an unmeasured confounder would need to be in order to change the estimate in a meaningful way e.

Sensitivity analysis is still being developed for continuous exposures, thus we are unable to do one. In summary, propensity scores may be particularly useful in situations where one cannot use randomization, such as this study, and may strengthen our ability to infer a causal relationship, particularly when they include a large number of confounding variables.

However, propensity scores cannot replace randomization and randomized trials are considered the gold standard for drawing causal inferences.

measure causal relationship research

Over the last month we have gone over both exploratory and descriptive research. Today we will finish off our blog series by jumping into the world of causal research.

This article will take us through the purpose of causal research, how to implement it in your research projects, and some great examples of how organizations are currently using causal research to make better business decisions.

Australian Bureau of Statistics

What is Causal Research, and Why is it Important? Causal research falls under the category of conclusive research, because of its attempt to reveal a cause and effect relationship between two variables. Like descriptive research, this form of research attempts to prove an idea put forward by an individual or organization.

measure causal relationship research

However, it significantly differs on both its methods and its purpose. Where descriptive research is broad in scope, attempting to better define any opinion, attitude, or behaviour held by a particular group, causal research will have only two objectives: Understanding which variables are the cause, and which variables are the effect. They might find through preliminary descriptive and exploratory research that both accidents and road rage have been steadily increasing over the past 5 years.

Instead of automatically assuming that road rage is the cause of these accidents, it would be important to measure whether the opposite could be true. Maybe road rage increases in light of more accidents due to lane closures and increased traffic. Determining the nature of the relationship between the causal variables and the effect predicted. The causal research could be used for two things.

Statistical Language - Correlation and Causation

First measuring the significance of the effect, like quantifying the percentage increase in accidents that can be contributed by road rage. Second, observing how the relationship between the variables works ie: These objectives are what makes causal research more scientific than its exploratory and descriptive counter parts.

In order to meet these objectives, causal researchers have to isolate the particular variable they believe is responsible for something taking place, and measure its true significance. With this information, an organization can confidently decide whether it is worth the resources to use a variable, like adding better traffic signs, or attempt to eliminate a variable, like road rage. Implementing Causal Research Effectively Causal research should be looked at as experimental research.

Remember, the goal of this research is to prove a cause and effect relationship. With this in mind, it becomes very important to have strictly planned parameters and objectives.

Without a complete understanding of your research plan and what you are trying to prove, your findings can become unreliable and have high amounts of researcher bias. Try using exploratory research or descriptive research as a tool to base your research plan on.

The cause and effect relationship will be proved or disproved by the experiment. To make sure your study will have results one way or another, observe what your normal environment is and then crank up the frequency or power of the causal variable.

You are clearly identifying which variables are being tested as independent causing effect and which are being tested as dependent being effected. Because of this, it is essential to identify which will be tested as which prior to the experiment.