Transcript 101_102_Data_Analysis

Research Ethics:
Ethics in psychological research:
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History of Ethics and Research – WWII, UN, Human and Animal
Rights
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Today (since 1998) in Canada- Tri-Council Policy (CIHR, SSHRC,
NSERC)
General Policy on Research Involving Human Subjects:
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The researcher must inform participants about all aspects of the
research that are likely to influence their decision to participate in the
study
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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
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If deception is necessary, researchers must determine whether
its use is justifiable; participants must be told about any
deception after completing the study
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Information obtained on participants must be kept confidential
and researchers must be sensitive about invading the privacy of
the participants
Data Analysis:
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Topics:
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Scales
Samples
Populations
Frequency Distributions
Measures of Central Tendency
Variability
Probability
Hypothesis testing
Significance
Scales:
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There are four basic types of scales:
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Nominal
Ordinal
Interval
Ratio
Nominal:
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based on name alone
 units may have little if
any relation to one
another
Ordinal:
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based on order
 intervals between
units are not
necessarily equal
 (e.g. places of
individuals finishing
a race, 1st, 2nd,
3rd,… are not
usually separated by
equal time intervals)
Interval:
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intervals between
basic units on the
scale are equal
 has ordinal
properties
 (e.g. degrees F,
degrees C)
Ratio:
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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:
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There are two fundamental types of statistics:
 Descriptive
 Inferential
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Descriptive: Used to summarize large sets of data
(e.g. class average, standard deviation)
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Inferential: Used to determine if experimental
treatments produce reliable effects or not
(inferences from sample to population)
Population:
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The entire group of
concern to a study
 Population data are
called parameters
Population
Sample:
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A subset of the entire
group of concern
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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
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Sample data are called
statistics
Population
Sample
Descriptive Statistics:
 Frequency Distributions
 Measures of Central Tendency
 Variability
Frequency Distributions:
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Tables, histograms, bar graphs, frequency
polygons, smooth curves
X
1
7
11
14
16
ƒ
2
4
6
3
1
Frequency Distribution Table
Histograms
Bar Graphs
Smooth Curves
Measures of Central Tendency:
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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 bar) and n





n
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Median: middle
most score when
data are rank
ordered
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Mode: most
frequently occurring
score in a data set
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Data:
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Rank order data:
7,6,8,6,8,6,6,6 (test scores)
6,6,6,6,6,7,8,8
 Mean = 6.625
 Median = 6
 Mode = 6
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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:
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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:
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Range (largest number minus smallest)
Interquartile range
Semi interquartile range
Standard error of the mean (Inferential Stats)
Standard deviation (Descriptive Stats)
Standard Deviation:
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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:
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Based on hypothesis testing – making predictions
about the outcome of a theory (based on sample
data)
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Predicting whether sample effects will hold true at the
population level
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We can never be certain that effects seen at the
sample level hold true for the population
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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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We create 2 opposing hypothesis
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Working or Alternate hypothesis (H1):
 Drug X has an effect on the dependent
variable
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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
Effects of a drug intended to reduce the
Symptoms of motion sickness:
Significant effects:
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Significant means there’s a high probability of a sample effect
being true at the population level
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Significance, however, is expressed as the probability of our
sample effect being false at the population level (Type I error)
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The results of this study show that the drug significantly reduced
the symptoms of motion sickness (p < 0.05)
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p < 0.05 (minimum criterion for scientific publication)
p < 0.01
p < 0.001
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Note: Significance does not speak to the size of effects
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Next class:
Neuroscience and Behaviour
 Chapter 2: The Biology of Mind
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