Correlational Research Describes Relations Among Vari 668315

Correlational Research Describes Relations Among Variables But Cannot

Correlational research describes relations among variables but cannot indicate that one variable causes something to occur to another variable. Rather, a statistically significant correlation coefficient simply indicates there is a relation among a predictor variable and an outcome variable. In correlational research, if there is a statistically significant correlation coefficient between two variables, you want to know that the relation truly exists. This goal is challenging to achieve because other variables that you are not studying may complicate the study and the interpretation of the results. You may find a spurious relation in which one common causal variable, sometimes referred to as a third variable, is responsible for the observed relation between the predictor variable and the outcome variable.

Imagine seeing a news story reporting the findings of a study claiming that children under the age of 17 who viewed R-rated movies showed a greater likelihood of developing a smoking habit. A third variable that could explain both the predictor and outcome variables is permissive parenting. Permissive parents may allow children to view R-rated movies when they are under the age of 17. In addition, permissive parents may not attend to their children’s whereabouts enough to be aware of their smoking habit, or they may not discipline the children for smoking. Therefore, in effect, you cannot conclude that the movie viewing caused the smoking habit.

The permissive parenting may have led to the children’s movie viewing habit and their smoking habit. You may also identify extraneous variables that might influence the outcome variable but, unlike the spurious correlation described above, these variables do not relate to or influence the predictor variable. For example, consider a reported correlation that the number of books in a person’s home (predictor variable) is related to their college GPA (outcome). An extraneous variable could be, for example, a person’s IQ (intelligence quotient) score. The higher IQ might be related to higher college GPA but not necessarily related to the number of books found in a person’s home.

There are additional examples of spurious relations and extraneous variables on pages 174–176 of your course text. In this Discussion, you focus primarily on spurious relations and extraneous variables. After reviewing examples in the course text, you will find your own examples in the media and explain how they might affect the relations between the variables under consideration. To prepare: Review popular online newspapers and/or online magazines to look for articles on research studies. Look for examples of statements and claims that might reflect a spurious correlation or an extraneous variable.

Note: This is not a library assignment, nor should you look for articles that already have reported a spurious relation or extraneous variable. You need to make this connection yourself. For instance, you might see a headline in a news magazine that states something like “Fast-Paced Children’s Television Programming Linked to ADHD.” Look for examples like this. Identify one popular media example of a correlation that could be argued to be a spurious correlation or that illustrates a correlation that may have an extraneous variable. With these thoughts in mind: Post by Day 3: Briefly explain the example and the claim that has been made.

Identify the predictor variable and the outcome variable. Identify the correlation. Is it a positive or negative correlation? How did you determine this to be the case? Identify your proposed spurious (third) variable or extraneous variable.

Explain the connection you made among the spurious/extraneous variable and the outcome and predictor variables. Post the URLs for the media example at the end of your posting. Note: Be sure to support the responses within your Discussion post, and in your colleague reply, with evidence from the assigned Learning Resources. Respond by Day 6 to at least one of your colleagues’ initial Discussion assignment postings in one of the following ways: Support or offer a different perspective to a colleague’s posting on his or her selections of the variable possibly responsible for the spurious correlation or the extraneous variable. Suggest an alternative spurious variable, or extraneous variable, that may explain relations.

Paper For Above instruction

In contemporary society, media reports frequently present research findings that suggest potential correlations between behaviors or characteristics. However, such correlations often do not account for underlying variables that could erroneously link the two. An example of this is a news article claiming that children who watch a high volume of fast-paced television programs are more likely to be diagnosed with Attention Deficit Hyperactivity Disorder (ADHD). At first glance, this suggests a positive correlation: increased television watching and higher ADHD diagnosis rates. However, this relationship might be influenced by an extraneous or spurious variable, such as socioeconomic status or parental supervision, which affects both the child's television consumption and behavioral health.

The predictor variable in this example is the amount of fast-paced children’s programming viewed, and the outcome variable is the diagnosis of ADHD. The reported correlation is positive, indicating that as viewing of fast-paced television increases, so does the likelihood of an ADHD diagnosis. This positive correlation is determined by observing the trend in the statistical data, where higher levels of television viewing align with more diagnoses. However, this does not imply causality, as other unmeasured factors could influence both variables.

A potential spurious variable in this context is socioeconomic status. Children in lower socioeconomic environments might have limited access to enriching educational activities, making television programming one of the few sources of entertainment. Simultaneously, socioeconomic disadvantages are associated with higher stress, environmental chaos, and limited access to healthcare, which can increase the likelihood of an ADHD diagnosis. This third variable complicates the apparent relationship because it affects both the predictor and the outcome, creating an illusion of direct causation where none exists. Therefore, socioeconomic status acts as a confounding or extraneous variable that explains the correlation without implying causality.

This example illustrates how unaccounted variables can lead to misinterpretations of correlational data. The media's portrayal of such findings without considering confounding factors can perpetuate misconceptions about causality. To avoid such errors, researchers must employ more rigorous study designs that account for potential extraneous variables, such as randomized controlled trials or statistical controls. Recognizing the influence of spurious and extraneous variables is crucial for consumers of research to critically evaluate claims made in popular media.

References

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