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Methods in Psychological Research Unit 04

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Unit 04 lecture notes and practice questions

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  • January 7, 2025
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  • 2024/2025
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  • Kosha bramesfeld
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‭Evaluating Association Claims‬



‭Understanding Statistical Significance‬
‭➔‬ ‭significance testing‬
‭◆‬ ‭decide what decision rules to use to test hypothesis prior to analyzing data‬
‭◆‬ ‭null hypothesis significance testing (NHST)‬‭: set of‬‭decision rules that help researcher‬
‭use margin of error to determine if observed effect is extreme enough to “reject the‬
‭null” and conclude the researcher’s alternative hypothesis is‬‭supported‬‭(never proved)‬
‭➔‬ ‭statistically significant‬
‭◆‬ ‭an effect is observed, even after factoring in the margin of error‬
‭◆‬ ‭not statistically significant if margin of error is large that it questions whether the‬
‭effect exists or not‬
‭➔‬ ‭what is an effect‬
‭◆‬ ‭specific outcome being tested‬
‭◆‬ ‭group comparisons‬‭: type of effect that compares two‬‭or more groups‬
‭◆‬ ‭correlation‬‭: type of effect that examines the association‬‭between variables‬
‭➔‬ ‭to test significance of an effect, start with the assumption that no effect exists (null‬
‭hypothesis)‬
‭◆‬ ‭opposite of what the researcher’s hypothesis of there being an effect‬
‭◆‬ ‭null = no effect ; alternative (researcher’s hypothesis) = effect‬
‭➔‬ ‭general approaches for testing significance‬
‭◆‬ ‭confidence interval approach‬
‭◆‬ ‭p-values approach‬


‭Confidence Interval Approach‬
‭➔‬ ‭construct a confidence level around the effect (hand calculations or computer program)‬
‭◆‬ ‭i.e. mean difference or correlation coefficient‬
‭➔‬ ‭assess whether the confidence interval around effect includes zero‬
‭◆‬ ‭no zero, results are statistically significant‬
‭●‬ ‭fail to reject the null‬
‭●‬ ‭researcher hypothesis not supported‬
‭◆‬ ‭yes zero, results are‬‭not‬‭statistically significant‬


‭P-Values Approach‬
‭➔‬ ‭1. set significance level‬
‭◆‬ ‭similar to confidence level‬
‭◆‬ ‭identified using confidence level‬
‭●‬ ‭remainder of confidence level = significance level‬
‭○‬ ‭i.e. 95% confidence = 5% significance‬

, ‭◆‬ ‭alpha‬
‭➔‬ ‭2. calculate effect and p-value‬
‭◆‬ ‭either hand calculations or computer program to calculate effect and probability value‬
‭of an effect that large occurring if the effect were actually zero‬
‭●‬ ‭the probability value = p-value‬
‭➔‬ ‭3. compare p-value to statistical significance‬
‭◆‬ ‭if probability value of the effect that large would occur if the effect was actually zero is‬
‭lower than the significance level, the results are statistically significant‬
‭◆‬ ‭p-value < alpha = statistically significant‬
‭●‬ ‭reject the null‬
‭●‬ ‭take next steps to explore hypothesis‬
‭◆‬ ‭p-value > alpha = not statistically significant‬
‭●‬ ‭fail to reject the null‬
‭●‬ ‭alternative hypothesis not supported‬


‭➔‬ ‭though two approaches were presented, they are rooted in the same foundation‬
‭◆‬ ‭one can be used to make inferences about the other‬


‭Understanding Effect Size‬
‭➔‬ ‭considers the size of the group difference and/or strength of the association‬
‭➔‬ ‭Cohen’s d = effect size used to compare the means across two groups‬
‭◆‬ ‭small = |.20|‬
‭◆‬ ‭medium = |.50|‬
‭◆‬ ‭large = |.80|‬
‭➔‬ ‭most effects in psychology are small to medium‬
‭◆‬ ‭rarely large effects‬
‭◆‬ ‭meaning subtle differences rather than huge‬
‭➔‬ ‭power analysis‬‭: calculates the ideal sample size for‬‭the effect to be large/seen‬
‭◆‬ ‭larger sample = <statistical error‬


‭Cautions of Statistical Significance‬
‭➔‬ ‭only tells if effect is likely to differ from zero in the population that the sample represents‬
‭➔‬ ‭doesn’t say anything about effect size‬
‭◆‬ ‭in large sample sizes, small effect sizes can be significant‬
‭➔‬ ‭not reliable when sample size is low‬
‭➔‬ ‭data from samples are merely estimates of true population parameters…always at risk of‬
‭making an error‬


‭Two Types of Statistical Error‬
‭➔‬ ‭Type 1 error (false positive)‬

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