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Research Ethics: 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 • 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 Data Analysis: • Topics: Scales Samples Populations Frequency Distributions Measures of Central Tendency Variability Probability Hypothesis testing Significance Scales: • There are four basic types of scales: Nominal Ordinal Interval Ratio Nominal: • based on name alone • Names or classes of nominal variables may have little if any relation to one another 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) Interval: • intervals between basic units on the scale are equal • has ordinal properties • (e.g. degrees F, degrees C) 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) 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) Population: • The entire group of concern to a study • Population data are called parameters Population 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 Descriptive Statistics: Frequency Distributions Measures of Central Tendency Variability Frequency Distributions: • Tables, histograms, bar graphs, frequency polygons, smooth curves X 16 14 7 6 3 ƒ 2 4 6 3 1 Frequency Distribution Table Histograms Bar Graphs Smooth Curves Measures of Central Tendency: • Estimate of where the majority of cases are in a data set • Mean: sum of all the individual datum divided by the number of cases: For populations: µ and N For samples: M or X n • Mode: most frequently occurring score in a data set • Median: middle most score when data are rank ordered • 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 ? Relative position of mean, median and mode with normal, positively and negatively skewed distributions: Normal Distribution Positively Skewed Distributions: Negatively Skewed Distributions: Variability: • Variability refers to the concept of the spread of a set of data • 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) Standard Deviation: • The average distance of scores in a data set from the mean Calculating SD for a population Calculating SD for a sample Calculating Standard Deviation for a Population: X (X-µ) ( X - µ )² 36 32 28 24 20 20 16 12 8 4 16 12 8 4 0 0 -4 -8 -12 -16 256 144 64 16 0 0 16 64 144 256 ∑ X = 200 ∑(X-µ)=0 σ ∑ ( X - µ )² = 960 µ=∑X/N µ = 200/10 = 20 σ² = variance σ² = ∑ ( X - µ )² / N = 960 / 10 σ ² = 96 ( x ) 2 / N 9.798 Calculating Standard Deviation for a Sample: X (X-X) ( X - X )² 36 32 28 24 20 20 16 12 8 4 16 12 8 4 0 0 -4 -8 -12 -16 256 144 64 16 0 0 16 64 144 256 ∑ X = 200 S ∑ ( X - X ) = 0 ∑ ( X - X )² = 960 X=∑X/n X = 200/10 = 20 s² = variance s² = ∑ ( X - X )² / n - 1 = 960 / 9 = 106.67 s ( x x ) 2 / n 1 10.33 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) • 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 • 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 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 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 Next class: • Chapter 5: Development Through the Lifespan