Statistics in psychology Describing and analyzing the data

Process of research in Ψ 1 A research question based on previous research → aim and hypothesis (experiment) Choice of method to test hypothesis (Experiment) Data collection

Process of research in Ψ 2 Describe results (descriptive stats) Analyze results → in experiments we want to know the probability that results are due to manipulation of IV (inferential stats) Interpret results Discuss results in the light of previous studies Outline possible future areas of research

Data Raw data (what comes out of data collection) Levels of measurement of data: Nominal: discrete categories → e.g. the number of people who helped or not Ordinal: Measurements that can be ranked or put in position but intervals unknown → she came in first, he came second etc. Interval and ratio: measurements based on scales → temperature (interval) and time in seconds (ratio)

Descriptive statistics Not all stats can be applied to all levels of data Mode : most frequent score (nominal data) Median: middle value when scores are placed in rank order (ordinal data) Mean: average value of all scores (interval and ratio data)

Descriptive stats: Measures of dispersion Range (difference between smallest and largest): easy to calculate but problem if there are outliers. Standard deviation: the average amount of all scores’ deviation from the mean (the larger the SD the more spread out the scores) – can be used if data is interval.

Graphical representations Bar graphs show data for selected categories, e.g. Two different conditions in experiment. Histogram: shows data for all categories. Frequency polygon: a line graph similar to histogram but allows two sets of data to be shown on the same graph. Box-and-whisker graph shows distribution of data in quartiles.

Site with easy intro to graph creation http://nces.ed.gov/nceskids/createagraph/

Inferential statistics Used to draw inferences on the basis of the data. Statistical tests (significance tests) → the probability that results are due to chance. If less than or equal to 5% (.05) chance that results due to chance → statistically significant results → accept research hypothesis

Statistical tests used in IA Non-parametric tests since the data does not meet the requirements for a parametric test Wilcoxon (when we are testing a difference between two conditions, the design is repeated measures and the collected data is interval (or at least ordinal)). Mann-Whitney U test (when we are testing a difference between two conditions, the design is independent design and the collected data is interval (or at least ordinal)).

Interpretation of statistical test How did the results of the statistical test match the prediction of the hypothesis? Which research hypothesis should be accepted – the null hypothesis or the research hypothesis? Significance level? For example p≤ .01