Quantitative Methods of Psychology Final Review Descriptive statistics summarize and describe stats Inferential Statistics draw conclusions and make inferences take a sample calculate stats and then apply the results to a larger group of people Most common methods of measurement mean median mode All 3 describe the middle of the group of scores Convert raw scores to Z scores subtract the mean from the raw score and divide by the standard deviation Z X M SD Nominal ratio rank order interval Percentages under the normal curve 34 14 2 The mean of ANY distribution of Z scores is 0 The standard deviation of ANY distribution of Z scores is 1 So the variance of any distribution of Z scores is 1 Distribution of null hypothesis is also called the comparison distribution How to find significance plot obtained score and decide whether to reject or fail to reject the null hypothesis How to construct a distribution of means take random samples from the population find their means put on the distribution and plot all of their means The mean of the distribution of means is always equal to the mean of the population of individuals Alpha probability of making a Type 1 error Beta probability of making a Type 2 error Type 1 error When you conclude that the study supports the research hypothesis when in reality the research hypothesis is false For example when it is believed a medicine will help a patient but in fact it is of no value Type 2 error When the hypothesis testing procedure leads you to decide that the results of the study are inconclusive when in reality the research hypothesis is true Rather you may carry out a study in which in reality the research hypothesis is true but the result does not come out extreme enough to reject the null hypothesis For example when it is decided that a medicine will not help a patient that the medicine will be ineffective for a patient is the null hypothesis when in fact it really would help Power the probability that the study will produce a statistically significant result if the research hypothesis is true Assumptions for analysis of variance Normality Each population follows a normal curve HOV Homogeneity of Variance the two populations have the same variance Independence of scores the scores must be 100 independent of each other Repeated measures ANOVA only Homogeneity of Variance and Normality NO independence of scores Advantages of factorial ANOVA looking at the combined effect of 2 or more different factors on the DV outcome variable Every combination is looked at and it is more efficient uses a single group of participants to look at many comparisons However typically we only use a 2 or 3 way ANOVA as there is no way to interpret more than that ex 20 way ANOVA you would not know what factor is affecting the outcome What is a 2X3 ANOVA X The number of spaces tells us how many independent variables there are 2 spaces so therefore 2 independent variables The actual number in the space itself tells us how many levels are in each independent variable the first IV has 2 levels and the second IV has 3 levels The grand mean is used when comparing groups averaging means 1 Way ANOVA Correlation does NOT equal causation Overlapping distributions When 2 distributions overlap a lot there is a small effect size and small power When they do not overlap or only overlap a little bit there is a large effect size and large power What goes into determining power Sample size effect size standard deviation small standard deviation higher power