To explore the relationship between assertiveness and the tendency to express anger openly

What are the means and standard deviations of the two variables, “rath” and “axout”?

The mean for axout is 2.1071 while the means for rath are: rath1= 3.26, rath2 = 3.66, rath3= 3.26, rath 4= 2.31, rath 5= 3.62, rath 6= 3.52, rath 7= 2.92, rath 8= 4.00, rath 9= 3.69, rath 10= 3.65, rath 11 = 3.26, rath 12 = 3.62, rath 13 = 3.92, rath 14= 4.15, rath 15-= 2.95, rath 16= 2.58, rath 17= 3.75, rath 18 =2.32, rath 19= 2.78, rath 20= 3.51, rath 21= 3.68, rath 22= 4.15. The standard deviation for axout is .42766 while the standard deviations for rath are: rath 1= .989, rath 2= 1.241, rath 3= .973, rath 4= 1.117, rath 5= 1.128, rath 6= 1.047, rath 7= 1.418, rath 8= .952, rath 9= 1.145, rath 10= 1.178, rath 11= 1.163, rath 12= 1.271, rath 13= 1.190, rath 14= 1.093, rath 15= 1.165, rath 16= 1.286, rath 17= 1.225, rath 18= 1.147, rath 19= 1.293, rath 20= .921, rath 21= 1.091, rath 22= 1.049.

What is the Pearson r?

The Pearson correlation coefficient for rath 1 and axout is .312, rath 2 and axout = .100, rath 3 and axout = -.054, rath 4 and axout = -.118, rath 5 and axout = -.057, rath 6 and axout = .252, rath 7 and axout = .206, rath 8 and axout = .009, rath 9 and axout = .091, rath 10 and axout = .094, rath 11 and axout = .175, rath 12 and axout .008, rath 13 and axout = -.038, rath 14 and axout = .299, rath 15 and axout = .122, rath 16 and axout = .131, rath 17 and axout = .014, rath 18 and axout = .146, rath 19 and axout .097, rath 20 and axout = .182, rath 21 and axout = .341, rath 22 and axout = .177. The Pearson correlation coefficient, r is used to determine the size of an effect. As such, a negative or positive 1Pearon correlation coefficient represents a small effect, a positive or negative .3 indicates a medium effect whereas a positive or negative .5 represents a large effect. In addition, a perfect positive relationship is indicated by +1, a perfect negative relationship by -1 whereas a 0 coefficient value indicates lack of linear relationship.

What is the p value (“significance level”)? What does this p value mean?

The two tailed significant level (p value) for rath 1 and axout is .013, rath 2 and axout = .438, rath 3 and axout = .673, rath 4 and axout = .356, rath 5 and axout = .656, rath 6 and axout = .046, rath 7 and axout = .106, rath 8 and axout = .947, rath 9 and axout = .480, rath 10 and axout = .466, rath 11 and axout = .169, rath 12 and axout = .951, rath 13 and axout = .766, rath 14 and axout = .017, rath 15 and axout = .340, rath 16 and axout = .305, rath 17 and axout = .912, rath 18 and axout = .255, rath 19 and axout = .451, rath 20 and axout = .154, rath 21 and axout = .006, rath 22 and axout = .362. The p value is used to test the level of significance under the assumption that the null hypothesis is true. A p- value less than or equal to .005 is significant while a p-value greater than .005 is considered not significant.

How does the n (sample size) of this sample affect the r and p values?

According to Field (2009), a large sample size is better is better since it gives a more true reflection of the strength of the relationship between two variables. The sample size of the selected sample is 22 and this affects the r and p values in that it provides an accurate figure about the population. a small sample may provide poor values while a larger sample provides an accurate estimate of the population parameters.

Relationship between assertiveness and whether one holds anger “in”

Now, we turn our attention to our bivariate regression question. Is there a relationship between assertiveness and whether one holds anger “in”?

Reopen the SPSS dataset complete_mooney_bp.sav.

Analyze,then Regression, then Linear.

Choose “Analyze,” then “Regression,” then “Linear.”

Select the variable “crowne-marlowe (mar)” and move it into the “Dependent” window.

Select the variable “axin,” and move it into the “Independent(s)” window.

Select “Statistics,” then “Model fit,” “Descriptives,” then “Confidence Intervals”

Select “OK” to run the analyses.

On the basis of the above, answer the following questions:

What is R and what does this particular R mean?

R is the correlation coefficient between the variables, in this case the correlation between assertiveness and holding anger “in.” The R value for this model is .246 indicating that there is a positive but weak relationship between tendency to hold anger “in” and assertiveness. In other words, the correlation between assertiveness and holding anger “in” is .246.

What is R2 and what does this particular R2 mean?

The R squared value for this model is .061 which indicates that 6.1 percent of holding anger “in” is contributed by the individual’s assertiveness. This is because the R squared value is used to determine the proportion of variance explained by the model (predictor).

What is F?

The F-test value for this model is 4.073 and the F-test determines the statistical significance of the model whereby if the F-test is significant, then the model is termed as fit. In this model, the F value is significant p = .048 indicating that an individual’s assertiveness is a significant variable in predicting the individual’s tendency to hold anger “in.”

What does this F mean?

The F ratio (4.073) means that fitting in the model (i.e. considering assertiveness) leads to a 4.073 increase in tendency to hold anger “in.”

What is the p value associated with this F?

The p value associated with the F value is .048 (F = 4.073, p = .048).

What does this p value indicate about the associated F value?

The p value (significance value) indicates that the F value is statistically significant. In other words, there is a statistically significant change in holding anger “in” as a result of an individual’s assertiveness.

What is the Standardized Coefficient (Beta weight) for the variable “axin”?

Is it significant?

What does this “significance” mean?

What do the “standardized” versus “unstandardized” coefficients mean?

The standardized coefficients are the same as the Beta coefficients and it helps in the direct comparison of the strength of one variable against another variable. It is possible to do a comparison of a Beta (stabdardized) coefficient against another standardized coefficient since they are determined in standard deviations. However with the unstandardized coefficients, it is impossible to give a direct comparison between two variables since these are not measured in standard deviations. Instead, it is only possible to state that a one unit change in the predictor variables results into a given unit change in the outcome variable.

What is the Confidence Interval for the variable “axin?” What do the Confidence Intervals tell us about this predictor?

The confidence interval (CI) for axin is 95% CI (-.083 – .000) indicating that the value of the regression coefficients in the population falls within -.083 and .000. The confidence intervals values produced for unstandardized regression coefficients are helpful in determining the likely value of the regression coefficients in the population.

Verbally summarize what these results “mean” for your reader.