1. Research Ethics:

2. Ethics in psychological research: History of Ethics and Research – WWII, Nuremberg, UN, Human and Animal rights Today - Tri-Council (NSERC, SSHRC, CIHR) Guidelines and Tutorial: http://www.pre.ethics.gc.ca/english/tutorial/index.cfm General Policy on Research Involving Human Subjects: The researcher must inform participants about all aspects of the research that are likely to influence their decision to participate in the study Participants must have the freedom to say that they do not wish to participate in a research project; they may also withdraw from the research at any time without penalty

3. The researcher must protect the participants from physical and mental harm If deception is necessary, researchers must determine whether its use is justifiable; participants must be told about any deception after completing the study Information obtained on participants must be kept confidential and researchers must be sensitive about invading the privacy of the participants

4. Data Analysis: Topics:  Scales  Samples  Populations  Frequency Distributions  Measures of Central Tendency  Variability  Probability  Hypothesis testing  Significance

5. Scales: There are four basic types of scales:  Nominal  Ordinal  Interval  Ratio

6. Nominal: based on name alone Names or classes of nominal variables may have little if any relation to one another

7. Ordinal: based on order intervals between units are not necessarily equal (e.g. places of individuals finishing a race, 1st, 2nd, 3rd,… are not separated by equal time intervals)

8. Interval: intervals between basic units on the scale are equal has ordinal properties (e.g. degrees F, degrees C)

9. Ratio: intervals between basic units on the scale are equal has ordinal properties has an absolute zero (a value below which others have no meaning) (e.g. degrees K, all weights and measures)

10. Statistics: There are two fundamental types of statistics:  Descriptive  Inferential Descriptive: Used to summarize large sets of data (e.g. correlations, frequency data, class averages etc.) Inferential: Used to determine if experimental treatments produce reliable effects or not (inferences from sample to population)

11. Population: The entire group of concern to a study Population data are called parameters Population

12. Sample: A subset of the entire group of concern If a sample is derived by random selection –every member of the population of concern has an equal chance of being selected for the sample Sample data are called statistics Population Sample

13. Descriptive Statistics:  Frequency Distributions  Measures of Central Tendency  Variability

14. Frequency Distributions: Tables, histograms, bar graphs, frequency polygons, smooth curves Xƒ 162 144 76 63 31 Frequency Distribution Table

15. HistogramsBar Graphs

17. Smooth Curves

18. Measures of Central Tendency: Estimate of where the majority of cases are in a data set

19. Mean: sum of all the individual datum divided by the number of cases:  For populations: µ and N  For samples: M or X

20. Mode: most frequently occurring score in a data set

21. Median: middle most score when data are rank ordered

22. Data: 7,6,8,6,8,6,6,6 (test scores) Rank order data:6,6,6,6,6,7,8,8  Mean = 6.625  Median = 6  Mode = 6 So what do we mean by the term average ?

23. Relative position of mean, median and mode with normal, positively and negatively skewed distributions: Normal Distribution

24. Positively Skewed Distributions:

25. Negatively Skewed Distributions:

26. Variability: Variability refers to the concept of the spread of a set of data

27. Variability can be measured in several different ways:  Range (largest number minus smallest)  Interquartile range  Semi interquartile range  Standard error of the mean (Inferential Stats)  Standard deviation (Descriptive Stats)

28. Standard Deviation: The average distance of scores in a data set from the mean  Calculating SD for a population  Calculating SD for a sample

31. Inferential Statistics: Based on hypothesis testing – making predictions Predicting whether sample effects will hold true at the population level We can never be certain that effects seen at the sample level hold true for the population Therefore we have to talk about the probability of an effect in the population (given what is observed in a sample)

32. When conducting an experiment (using samples) we create 2 opposing hypothesis Working or Alternate hypothesis (H1):  Drug X has an effect on the dependent variable Null hypothesis (Ho):  Drug X does not have an effect on the dependent variable

33. Basic procedure:  attempt to disprove Ho. If this is possible, H1 is proven  note: with sample data it is not possible to prove H0, therefore, the hypothesis testing procedure attempts to disprove H0

34. Example: Effects of a drug intended to reduce symptoms of motion sickness : Hypothesis: Prediction of an effect Working Hypothesis: H1: Drug X helps reduce the intensity of motion sickness Null Hypothesis: H0: Drug X has no effect in reducing the intensity of motion sickness

36. Significant effects: Significant means there’s a high probability of a sample effect being true at the population level Significance, however, is expressed as the probability of our sample effect being false at the population level (Type I error) The results of this study show that the drug significantly reduced the symptoms of motion sickness (p < 0.05) p < 0.05 (minimum criterion for scientific publication) p < 0.01 p < 0.001 Note: Significance does not speak to the size of effects

37. Next class: Chapter 5: Development Through the Lifespan