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 ƒ

16 2

14 4

7 6

6 3

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: 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

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

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