Review of a Correlational Study

A correlational study is a type of study that looks at natural relationships between variables. It helps us explain and predict relationships between variables without altering them. The independent variable in this correlational study is the amount of sugar that children intake. The dependent variable is the hyperactivity of children. I can operationalize the independent variable, “amount of sugar,” as the number of candy

There are several differences between correlation and causation. Correlation is if an event happens and is not related to another event and it is a coincidence. This would be if an event happened but it was not connected to another. An example of this would be catching a foul ball at a baseball game. It would be a correlation because you just happened to be in that place where the ball was hit and were able to catch it. Causation on the other hand is a cause and effect. One thing happens because another thing previously happened. An example of this would be if a person drank caffeine late at night, then they would be up all night. Another example of this would be if someone slipped on ice coming out of class.

Researchers have studied the correlation between birth defects and tobacco. Correlation is not about cause and effect but rather how a relationship between two variables works

The quantitative research is also based on statistical reports on correlations, regression analysis, comparisons of means and variances, and statistical significance of findings.

139). It is an assumed linear association between two variables that is quantified by a single statistical number. The correlation coefficient measures the strength of the association between the two variables, 0 means there is no correlation, 1 means there is a perfect positive correlation, -1 means there is a perfect negative correlation. "The sample correlation coefficient, denoted r, ranges between -1 and +1 and quantifies the direction and strength of the linear association between the two variables. The correlation between two variables can be positive (i.e., higher levels of one variable are associated with higher levels of the other) or negative (i.e., higher levels of one variable are associated with lower levels of the other) (bumc.bu.edu, 2013)." Correlation is the most appropriate because it is easy to calculate and easy to

However with advantages come disadvantages. For example, with correlational studies there is no background information obtained. Therefore, the reliability of the correlational studies is questioned.

In this essay I will describe correlation is a measure of association as well as describe different methods of establishing a correlation between variables. In this essay I will also explain advantages and disadvantages of each method, were each must be applied, and provide particular circumstances and examples in which a researcher may want to establish correlation

Data Collection: Sample of student population, describe the relationship between these two variables, language arts scores, and measure the strength and direction of the correlation.

| Based on explicit knowledge and this can be easy and fast to capture and analyse.Results can be generalised to larger populationsCan be repeated – therefore good test re-test reliability and validityStatistical analyses and interpretation are

Data used in this study is from a previously prepared collection for a different study and hence does not require any instruments. Use of Microsoft Excel and graphpad, an online software, helped in calculating the results and analyzing the data.

Correlational analysis were used to explore the relationships between the studies key variables, the results are indicated below (Table 2).

This is a correlational study and it has quantitative character. The article consists of next sections: Abstract, Introduction, Method (this part also consists of such subsections as Subjects, PDS, Tasks, and Procedure), Results, Discussion, and References.

The purpose of this paper is to correlate the research from multiple sources over a period of time that have observed the

The objective of this chapter is to describe the procedures used in the analysis of the data and present the main findings. It also presents the different tests performed to help choose the appropriate model for the study. The chapter concludes by providing thorough statistical interpretation of the findings.

Data cleaning was included throughout the process of this correlational study, and it included checking for duplicate data and checking for internal consistency. Data editing and omission was used while analyzing data and some statistical outliers were found, this explains why some of the collected information about other networks was not mentioned in the statistical test results. Data analysis included descriptive statistics about the sample, and the statistical analysis included a T-test and a Pearson r Correlation test.