SPSS To Applying Analytical Strategies To A Research Area Of Interest

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Part B. Applying Analytical Strategies to an Area of Research Interest

  1. 1. Briefly restate your research area of interest. (my area of interest is comparing 2 different types of couple therapy with each other as well as a control group that does not receive treatment)
    1. Pearson Correlation. Identify two variables for which you could calculate a Pearson correlation coefficient. Describe the variables and their scale of measurement. Now, assume you conducted a Pearson correlation and came up with a significant positive or negative value. Create a mock r value (for example, .3 or -.2). Report your mock finding in APA style (note the text does not use APA style) and interpret the statistic in terms of effect size and R2 while also taking into account the third variable problem and well as direction of causality.
    2. Spearman’s Correlation. Identify two variables for which you could calculate a Spearman’s correlation coefficient. Describe the variables and their scale of measurement. Now, assume you conducted a correlation and came up with a significant positive or negative value. Create a mock r value (for example, .3 or -.2). Report your mock finding in APA style (note the text does not use APA style) and interpret the statistic in terms of effect size and R2 while also taking into account the third variable problem and well as direction of causality.
    3. Partial Correlation vs. Semi-Partial Correlation. Identify three variables for which you may be interested calculating either a partial or semi-partial correlation coefficient. Compare/contrast these two types of analyses using your variables and research example. Which would you use and why?
    4. Simple Regression. Identify two variables for which you could calculate a simple regression. Describe the variables and their scale of measurement. Which variable would you include as the predictor variable and which as the outcome variable? Why? What would R2 tell you about the relationship between the two variables?
    5. Multiple Regression. Identify at least 3 variables for which you could calculate a multiple regression. Describe the variables and their scale of measurement. Which variables would you include as the predictor variables and which as the outcome variable? Why? Which regression method would you use and why? What would R2and adjusted R2 tell you about the relationship between the variables?
    6. Logistic Regression. Identify at least 3 variables for which you could calculate a logistic regression. Describe the variables and their scale of measurement. Which variables would you include as the predictor variables and which as the outcome variable? Why? Which regression method would you use and why? What would the output tell you about the relationship between the variables?

Tutor Answer

Effective_Meg
School: UIUC

All done! Lemme know if I can help you with anything else.

Running head: ANALYTICAL STRATEGIES APPLICATION

Applying Analytical Strategies to a Research Area of Interest
Name
Institution
Date

1

ANALYTICAL STRATEGIES APPLICATION

2

Statement of research area of interest

The study consists of a sample of selected couples who are randomly assigned into two
experimental therapy groups and one control group. The research area of interest entails
comparing the two different types of couple therapy with each other as well as a control group
that does not receive treatment. The results are analyzed to assess the effectiveness of the therapy
treatment on the couples. The analytical strategies used are correlation and regression.

Pearson Correlation
Pearson correlation attempts to measure the strength of a linear relationship between two
variables by drawing a line of best fit through the values of two chosen variables (Bolboacaet al.,
2006). The two variables suitable for the computation of the Pearson correlation coefficient, r,
are treatment results values and couple therapy “1” experiment values. The scale of measurement
for the two variables is Ratio since they are of equal intervals and satisfy the absolute zero
property, are normally distributed and linearly related.
Assuming a Pearson correlation coefficient of (r = 0.79) and the observed sample size is 30
we can deduce that “A Pearson product-moment correlation was run to investigate the
association between treatment results and couple therapy “1”. There was a strong, positive
correlation between the two variables, which was statistically significant (r = .79, n = 30, p =
.005). From the Pearson c...

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