Psychology 218
Analysis of Behavioural Data
Introductions:
 Course Director:
Dr. Kevin Hamilton
 Teaching Assistant: Lindsay Nagamatsu
 Students:
Calculators and computers:
Approach to course:
Math review: Appendix A
Study Hints:
Course outline:
http://www.psych.ubc.ca/courses/psy
Course note outlines:
http://www.psych.ubc.ca/~kevin/
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Lecture 1:
Introduction, tables, graphs (Chapters 1 and 2)
Statistics – technical definition:
 Science consists of methods for making observations and
collecting information (measurements). Statistics are
mathematical procedures for organizing, summarizing and
interpreting information
 Statistics for organizing information are called descriptive
statistics (average, range, tables, graphs, etc.)
 Statistics for interpreting information are called inferential
statistics (do differences found between conditions in an
experiment (samples) show the same pattern as the entire
population)
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Why statistics:
 They help us to condense and summarize large amounts of
information
 They provide a way of describing variability in behavioural data
 They help us establish population effects from samples
 A basic statistical background is critical for evaluating
information in our new information based society
 Statistics provides standardized procedures for summarizing and
interpreting data that are recognized and understood throughout
the worldwide scientific community
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Statistical concepts:
Population: The set of all individuals of interest in a particular
study (you define it)
For example:
o all North American males between 20 and 30 years of
age (large)
o all Kwantlen students missing the index finger from
their left hand and the thumb from their right (small)
 Populations can be large or small but are often large.
Sample: The set of all individuals selected from a population,
usually intended to represent the population (samples provide us
with a window on and in turn a glimpse at the population).
 Samples are often used because populations are too large for
acquiring all relevant data
 When describing data, it is important to distinguish between:
1) Data from a population -population parameters
2) Data from a sample –statistics
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Sampling error:
 A sample is never identical to its population.
Sampling error is the discrepancy or amount of error between
sampling statistics and corresponding population parameters
 The purpose of inferential statistics is to compute sampling error
to determine how true the relationship is between sample
statistics and population parameters
 Basic to being able to conduct good research is the ability to
create a good sample
 The basic principle of sampling is random selection
 Random selection gives everyone in the population and equal
chance of being selected for the sample
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Other terminology:
Datum: Singular measurement or observation
Data: Plural of datum
Raw score - Original measurement = true score +error
Variable: A characteristic or condition that changes or has
different values for different individuals. For example, X Y
(height, weight)
Constant: A variable that does not change is a constant (C). For
example, adding 4 to every score (X+C)
Correlation: A statistical technique for determining the decree of
relation between two or more variables
 The simplest way to determine if there is a relationship between
variables – determined by observation not manipulation.
 Correlations tell us nothing about cause and effect (which
variable is the antecedent and which variable is the consequent)
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Experimental method:
 Procedure to determine cause and effect. Involves manipulating
variable of interest while keeping everything else constant (e.g.
location, instructions, time of day, etc.)
Subjects: Participants in a study
Independent variable: Variable manipulated or controlled by the
experimenter (always has at least 2 values or levels)
Dependent variable: Variable observed for changes
(measurement)
Control group: Condition of the independent variable that does
not receive experimental treatment
 Can receive neutral treatment or placebo
 Provides baseline for comparing experimental group effects of
treatment
Quasi-Experimental Method
 Examination of non-manipulated independent variables (quasiindependent variables)
 These variables are usually a subject or time variable – e.g. M/F
or comparing individuals at different point in time.
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Theory:
 A number of statements about underline mechanisms of
behavior
 Helps us organize, unify and understand observations
useful for promoting further research. A theory makes
predictions (hypotheses)
 Hypothesis – a tentative assumption made in order to draw out a
test of its empirical consequences
 Hypothesis testing is a way of testing theories or predicting
outcomes
 Hypothesis testing is a big part of inferential statistics
Hypothetical constructs:
 Concepts that help explain behavior - phenomena which can't be
measured directly for example: learning, intelligence,
personality types, motives
 Often hypothetical constructs take the form of intervening
variables
Operational definition:
 Defining a construct so that it can be observed and measured.
For example, intelligence, scores on an IQ test (IV, DV)
 There are two components to an operational definition:
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1) Description of operations and procedures for measuring a
construct
2) Description of construct in terms of resulting measurements
(dependent variable)
Scales:
 Either qualitative or quantitative
Nominal: A scale defined by name alone. For example, categories
such as professional, skilled trade, sales, levels of an independent
variable (control, drug)
Ordinal: Separate categories presented in rank order for example:
first-place, second-place, third-place, etc.
Difference between first and second-place not necessarily the same
as difference between third and fourth place
Interval: The same as ordinal scale but the intervals are the same
size. For example, degrees Celsius, degrees Fahrenheit
Ratio scale: The same as interval scale but it has an absolute zero.
For example weights and measures, degrees Calvin. Note:
dependent variables are usually ratio or interval scales.
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Discrete and continuous and variables:
Discrete variable: Indivisible categories for example, number of
children, number of apartment houses.
Continuous variable: Divisible categories, for example, measures
such as time, distance, etc. Divisions are possible an infinite
number of times
Mathematics review: see appendix A
Statistical notation: see appendix A-1
Scores: X,Y
Summation: 
Order of operations:
1) Parentheses
2) Exponents (squaring)
3) Multiplication and division (in order they appear from left to
right)
4) Summation 
5) Any other addition or subtraction (in order they appear from left
to right)
Readings:
Assignment:
Chapters 1 and 2
Odd numbered problems at the end of Ch 1
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