Research Methods⚗️ Flashcards
(56 cards)
P value
Probability of finding effect in sample if no effect in population (unsystematic variation)
If probability less than 0.05 can reject null
CI
95% confidence interval around the mean difference
Range of scores likely to indicate the true population mean
T value
Calculation of p value based on calculation of t value
If mean difference increases then t value increases and p value decreases
Effect sizes
Indicate if differences are psychological significant (not just statistically)
Independent T test
Between participants
Difference between variables (continuous DV, categorical IV)
Independent t test df
Total sample size - 2
(Two means are used)
Data that can be freely varied and still get same descriptive statistics in the sample
Independent and dependent t test assumptions
Data approximately normally distributed (histogram clear skew, if not use non-parametric test)
No clear outliers (boxplot, points outside the box)
Spread of scores (variance) is relatively equal in both groups, error bars
Levene’s test affects accuracy of t test if not equal
Paired t test
Within participants
Difference between variables (continuous DV, categorical IV)
Paired t test df
Df=total sample size - 1
One mean used
Data that can be freely varied and still get same descriptive statistics in the sample
Levene’s test
Assuming variance
Not significant (p more than 0.05) no significant difference in variance in each group (assume equal variance)
Significant (p less than 0.05) significant difference in the variance in each group (do not assume equal variance)
Independent t and paired t reporting results
Group 1 were (significantly) better (mean, SD) than group 2 (mean,SD)
t (df) =T VALUE, p = SIG 2 TAILED VALUE
This suggests that group 1 were better than group 2
Cohen’s d
Interprets magnitude of an effect independent of the scale used
For both independent and paired t test
Use mean and SD for both groups/conditions
Larger value indicates more pronounced effect (can be negative if opposite direction)
Calculating effect sizes
After finding a significant effect in a null hypothesis
Calculate Cohen’s d using the Cohen’s d calculator
Compare value to levels of effect size and state magnitude of effect
Bigger effect sizes indicate more important effect
Pearson’s correlation
Relationship between two variables
Continuous IV and continuous DV
Scatterplot relationship conclusions
How much trend resembles a linear pattern
Variation in x explained by differences in y scores or vice versa
Relationship between x and y can be explained by z
Relationship is chance
Correlation coefficient
Measure of effect
Direction (positive or negative) and strength of relationship (the more it resembles a straight line, between 0 and 1)
Correlation value strengths
R= 0.01-0.39 weak R= 0.40-0.69 moderate R= 0.70-0.99 strong
Pearson’s correlation assumptions
Data normally distributed (histogram clear skew, if not use non-parametric test)
No clear outliers (boxplot, points outside the box)
Linearity (plot on scattergraph to check)
Pearson’s correlation df
Total sample size (N) -2
Pearson’s correlation shared variance
R squared
Variation in scores in one variable that can be explained by variation in the other variable
Stronger relationship = more overlap and shared variance
Pearson’s correlation reporting results
The findings show a (STRENGTH AND DIRECTION OF CORRELATION from TEST SCORE) between A and B
The relationship was (SIGNIFICANT OR INSIGNIFICANT),
r (df) = TEST SCORE, p= SIG 2 TAILED
This shows that A…
What happens if p is 0.000
You write p< .001
Chi squared test
Relationship between variables
Categorical IV and categorical DV (transformed from continuous)
Calculating chi squared test
Compare observed and expected values, bigger difference indicates larger Chi-squared value (more likely to reject null)
