stats write ups

Question 1

partial correlation write up.

what do u include in note under correlation table ?

Answer 1

Note. Pearson’s product moment correlation r, with 95% confidence intervals around r. N = x. *p < .05, **p < .001

Question 2

format for discussion of partial correlation

if decreases in strenght

Answer 2

while x increases with y, when z is controlled for, this relationship diminishes. this suggests that the relationship between x and y may be explained by z.

Question 3

what to report in results section of partial correlations

Answer 3

• table with descriptive stats and product moment correlations with 95% CI around r.
• Report Pearson’s correlation significance in text with their direction if significant. (don’t need to report p value as they’re in table)
• report partial correlation data. r(df) = .___, p < .___. report direction and change in significance

Question 4

assumptions of regression

Answer 4

normality, linearity, (multicollinearity), homoscedasticity,

Question 5

should u include correlations in regression model

Answer 5

only if multiple predictor variables

Question 6

Results section for regression / multiple regression

Answer 6

• Only include correlations when more than one predictor variable
• Assumptions = normality, linearity, (multicollinearity), homoscedasticity,
• Test used = linear regression model
• Report F statistic AND R^2 value
• Clarification of F statistic significance
• If model is significant, report coefficient stats: b = _.__ [95% CI: , ]
• IF multiple predictors and result is significant, report t-tests for each.
• Direction should be clear.
• If multiple significant predictors, then say which one is a stronger predictor (based from regression coefficients)

Question 7

Multiple regression results section

Answer 7

-descriptive stats table and correlations between study variables

• variables also including step construction: - E.g. the hierarchical regression was constructed to include workers salary in step 1, with their attendance in step 2.
• assumptions violated

Model 1, then change statistics in introducing 2. Then Final model with both.

e.g.
-salary alone (step 1) explained a significant proportion of variance in workers satisfaction, F(df, df) = ___, p < __ , R^2 = __ . Introducing attendance at stage 2 explained an additionl (R^2)% of the variance in satisfaction and change in R^2 was significant, F Δ(df, df) = __ , p = __.
-The final model, including both salary and attendance explained (R^2)% of satisfaction, F = …… .

if significant
- coefficient results b = [CI = ], t = , p = .

clarify which variable was strongest (if more than 1 signif)