research ethics:. ethics in psychological research: history of ethics and research – wwii,...
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Research Ethics:
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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
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• 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
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Data Analysis:
• Topics:
Scales Samples Populations Frequency Distributions Measures of Central Tendency Variability Probability Hypothesis testing Significance
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Scales:
• There are four basic types of scales:
Nominal Ordinal Interval Ratio
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Nominal:
• based on name alone• Names or classes of
nominal variables may have little if any relation to one another
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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)
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Interval:
• intervals between basic units on the scale are equal
• has ordinal properties• (e.g. degrees F,
degrees C)
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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)
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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)
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Population:
• The entire group of concern to a study
• Population data are called parameters Population
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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
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Descriptive Statistics:
Frequency Distributions Measures of Central Tendency Variability
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Frequency Distributions:
• Tables, histograms, bar graphs, frequency polygons, smooth curves
X ƒ
16 2
14 4
7 6
6 3
3 1
Frequency Distribution Table
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Histograms Bar Graphs
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Smooth Curves
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Measures of Central Tendency:
• Estimate of where the majority of cases are in a data set
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• Mean: sum of all the individual datum divided by the number of cases: For populations: µ and N For samples: M or X
n
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• Mode: most frequently occurring score in a data set
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• Median: middle most score when data are rank ordered
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• 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 ?
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Relative position of mean, median and mode with normal, positively and negatively skewed distributions:
Normal Distribution
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Positively Skewed Distributions:
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Negatively Skewed Distributions:
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Variability:• Variability refers to the concept of the
spread of a set of data
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• 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)
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Standard Deviation:
• The average distance of scores in a data set from the mean
Calculating SD for a population Calculating SD for a sample
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Calculating Standard Deviation for a Population: σ
X ( X - µ ) ( X - µ )²
36 16 25632 12 14428 8 6424 4 1620 0 020 0 016 -4 1612 -8 64
8 -12 1444 -16 256
∑ X = 200 ∑ ( X - µ ) = 0 ∑ ( X - µ )² = 960
µ = ∑ X / N
µ = 200/10 = 20 σ² = variance
σ² = ∑ ( X - µ )² / N = 960 / 10
σ ² = 96 798.9
/)( 2
Nx
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Calculating Standard Deviation for a Sample: S
X ( X - X ) ( X - X )²
36 16 25632 12 14428 8 6424 4 1620 0 020 0 016 -4 1612 -8 64
8 -12 1444 -16 256
∑ X = 200 ∑ ( 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
33.10
1/)( 2
nxxs
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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)
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• 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
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• 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
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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
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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
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Next class:
• Chapter 5: Development Through the Lifespan