Final stats deck

Question 1

Describe the processes involved in Hierarchical regression model building and explain why it is appropriate for this study?

Answer 1

Researchers look through past literature to decide which factors are more consistently influential

Provide examples what past literature says

Due to past literature enabling researchers to rank their importance

Question 2

Describe the five assumptions of multiple linear regression and, with reference to the appropriate tests provided in the SPSS output, indicate whether the assumptions have been met or violated. (5 marks)?

Answer 2

No multi-collinearity between IVs in model - Largest VIF should be <10, if two >5 then remove one IV
Average VIF should not be considerably >1

• Independence of residuals - Tested with Durbin-Watson statistic
• Statistic can range from 0 to 4, with 2 meaning errors are uncorrelated • Generally values less than 1 or greater than 3 are problematic
• Homoscedasticity of residuals - dots around 0
• Linearity of residuals - dots around 0 get more information from slides in exam
• Normality of residuals - histogram normally distributed

Question 3

Model answer for a hierarchical report?

Answer 3

Weight and years retired explained 32.1% of the variation in fitness (p = 0.001)

Within this model weight is significant p = 0.002, where as years retired is not 0.681

When adding alcohol the model now explains 59.7% of variation in fitness (p=0.000) and alcohol is highly significant (p = 0.000)

Finally adding the variable of MVPA can explain 64.5% of the variance of fitness (p = 0.030), MVPA was highly significant (p = 0.030)

Therefore the final model predicts the highest percentage of variance of fitness (64.5%) however against previous research years retired isn’t a significant predictor.

Think its actually this:

The sum4dw explains 75.1% of the variance in DXAfat%. The addition of Thigh skinfold to the regression model increases this to 85.8% (ΔR2 = 0.107, p<0.001). These findings imply that the addition of thigh skinfolds to the commonly used sum of 4 skinfolds would improve the accuracy of predicting body fat%.

Question 4

What is ‘manipulate a combination of both’ analysed by ?

Answer 4

Mixed factorial ANOVA - so when there is within (repeated measures), and between is (independent so difference in the participants)

Question 5

Independent ANOVA?

Answer 5

Between subjects (different participants) participants don’t do everything

Question 6

WHat’s repeated measures?

Answer 6

Repeated measures / within subjects (participants do everything)

Question 7

How to look at table for equation on a mixed factorial ANOVA?

Answer 7

F ( degrees of freedom at top, degrees of freedom at bottom)

P = significant value

np^2 = partial eta value at bottom

Question 8

Sphericity ?

Answer 8

the assumption that the variances of the differences between conditions are equal?

Mauchly test will indicate a problem if its significant

Question 9

What’s a post Hoc test?

Answer 9

Following a one way independent ANOVA you can perform pairwise post hoc comparisons to determine which pairs of means are different from one another

• (Normally select this one on SPS) Bonferroni correction

Question 10

How to find effect size of repeated measures ANOVA?

Answer 10

Go to Tests of Within-Subjects Effects and get the partial eta value from greenhouse geiser

Question 11

Reporting APA style?

Answer 11

If mauchly test was failed, we will report the greenhouse Geisser corrected degrees of freedom and p values

An example would be for an independent:

F(1.6,11.2) = 3.79, p=.06, np^2 = 0.35, MSE = 13.71

F(greenhouse geiser degrees of freedom, error greenhouse geisser degrees of freedom) = F value, p = 0.06, np^2 = partial eta squared, MSE = mean square of error

Only report pairwise comparisons eg Bonferroni if you have a significant ANOVA

Question 12

Assumptions of chi-square test?

Answer 12

The expected frequencies should be greater than 5. If an expected frequency is below 5, the result is a loss of statistical power, and may fail to detect a genuine difference

Question 13

How to report a chi square test when it has 2 variables?

Answer 13

Assumption:
The minimum expected frequency is 14.44 which is greater than 5, so assumption is met

Results (significance of X^2):
X^2 = 25.356, p<0.001, statistically / reject the null hypothesis. Leadership style and performance outcome are associated.

26.3% (10/38) of athletes who had a democratic coach won, whereas 70.4% (114/162) that had a autocratic coach won.

Interpretation/Conclusion

A much greater percentage of athletes won under an autocratic coach than under a democratic coach