Correlation, Regression, and T-Tests
Linear regression analyses are conducted after correlation has been established (Cohen, West & Aiken, 2014). Correlation shows how two variables are related, that is how one variable behaves when the other changes. Regression analyses somehow quantify this relationship -- we use it to predict the value of the dependent variable at different values of the independent variable. Since regression analyses yield numerical values, they can only be performed on quantitative, measurable values. RQ1 in the week one assignment -- how does engagement in social media relate to loneliness -- fits the qualification for simple regression analyses -- the independent variable (engagement in social media is measured in terms of the number of hours one spends on the internet) whereas the dependent variable is measured in terms of quantified categories ranging from 'very low' to 'very high', where quantification would involve assigning numbers to categories, for instance, 'very low' is assigned a low value of 1, 'low' is assigned a relatively higher value of 2, and 'very high' is assigned the highest value, say 5.
Regression analysis in this case will help the researcher predict i) how one's degree of loneliness would change with changes in the number of hours spent on social media; and ii) the relative strength of social media as a predictor of loneliness. For this research question, we could formulate the null and alternative...
G. with the same group being tested on a pre- and post-test). Variations between t-tests are the following: a. T-test on independent samples that have unequal variance. The problem here is determining sampling error in order to accurately determine whether two samples are or are not really different. This test is also used when the number of cases in each sample is different or when the number of cases in one or
Correlation and Regression The ability to evaluate the essential general assumptions underlying statistical models and to distinguish the concepts and techniques of regression analysis is important for scholarly research. This is a more important element for a doctoral learner focused on quantitative research in order to generate appropriate and credible conclusions. Interpreting types of variables, design frameworks, and treatments in statistical regression analysis is also an essential skill for upcoming research
High School Longitudinal Study Dataset Using SPSS Software Scenarios 1. This section determines African citizen's perceptions about their current level of democracy. The study uses 2015 Afrobarometer data to determine whether the current level of African democracy is statistically different from the value of 6. Have series of reforms increased African views on the level of democracy? The study uses the following variables to answer the research question: Level of democracy: ten years ago
It includes a way to track bidirectional changes focused on the individual who operates within the two cultural environments. Methods of data reduction have been incorporated in order to develop a psychometrically strong tool. Indexes to validate the results are included in the process as well. As a part of the BAS, scales are used to measure the various areas of acculturation rather than examining items based on the
Introduction The business taken into account for the analysis is Google, Inc. The primary data being collected is on Google and on different mobile applications to evaluate the validity of whether the latter are becoming more significant and popular compared to Google. In this case, the independent variable is percentage of internet usage on Google for every period. Basically, the metric taken into consideration is the percentage of time spent on
The Difference between Causation and Correlation within the Context of DBA Doctoral Research Study Introduction For growth to occur it is important to understand the concepts of correlation and causation. Correlation can be differentiated from causation in general terms in that correlation assists in the prediction of future events since it indicates what is likely to occur. Causation on the other hand makes it possible to alter the future. Understanding the difference
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