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University of Guelph
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Psychology
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PSYC 1010
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Harvey Marmurek
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Lecture

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Psychology

PSYC 1010

Harvey Marmurek

Winter

Description

Chapter 1
Two Branches of Statistics
Descriptive statistics - Organize, summarize, and communicate numerical information
Inferential statistics - Use samples to draw conclusions about a population
Population – complete set of the things in which we are interested (could be any size)
Sample – set of observations drawn from the particular sample (portion of the sample)
i.e. population of Ontario or Sample from the world
Variables – take on range of values
i.e. reaction time in the Stroop Task – the time to say the colours compared to the time to say
the word
Types of Variables
Discrete – variables that can only take on specific values
i.e. whole numbers or how many letter in your name?
Continuous – can take on full range of values
i.e. how tall are you?
Nominal – category or name
i.e. name of cookies (discrete)
Ordinal – ranking of data
i.e. ranking of favorite cookies (discrete)
Interval – used with numbers that are equally spaced
i.e. temperature of cookies (can be discrete or continuous)
Ratio – like interval, but has a meaningful 0 point
i.e. how many cookies are left? (seldomly discrete and almost always continues)
Independent – manipulate or categorize
Dependent – measure; depends on the independent variable
Confounding – try to control or randomize
Selecting and Assessing Variables
Operational definition – exactly what are you studying
Reliability – consistency of the measure
Validity – extent the test measures what it is supposed to measure
Types of Research Designs
Experiments – participants are randomly assigned to a condition or level of one or more
independent variables
-able to make causal statements and control the confounding variables
-importance of randomization One Goal, Two Strategies
Between-group designs – different people complete the tasks, and comparisons are made
between groups
Within-group designs – the same participants do things more than once, and comparisons are
made over time
Chapter 2
Distributions – four different ways to visually describe just one variable
-frequency table
-grouped frequency table
-frequency histograms
-frequency polygen
Shapes of Distributions: Specific frequency distribution
-bell shaped
-symmetrical
-unimodal
Skewed Distributions – when our data are not symmetrical
-positive: tail to the right, may represent floor effects
-negative: tail to the left, may represent ceiling effects
Chapter 3
Uses of Graphs
Positive and negative uses – can accurately and briefly present info and can reveal/conceal
complicated data
Techniques for Misleading
the false face validity lie – method seems to represent what it says, but does not actually
i.e. using yelling as a measure of aggression
the biased scale lie – scaling to knew the results
i.e. using 3 positive words out of 5 options
the sneaky sample lie – when participants are preselected to provide the desired data
the extrapolation lie – assumes knowledge outside of the study
the inaccurate values lie – using scaling to distort portions of the data
the outright lie – making up data Common types of Graphs
scatterplots
-graphs that depict the relation between two scale variables
-observing every data point
-linear relationships
-nonlinear relationships
line graphs
-searching for trends
-line of best fit
-time series plot
bar graphs
-when the independent variable is nominal and the dependent variable is interval
-pareto chart: bar graph in which categories along the x-axis are ordered from highest to light
pictorial graphs
-visual depiction of data for an independent variable when there are very few levels
pie charts
-graph in the shape of a circle with each slice representing a proportion of each category
-not necessarily the best choice
Choosing the Graph Based on Variables
One scale variable: histogram or frequency polygon
One scale independent and one scale dependent variable: scatterplot or line graph
One nominal independent and one scale dependent variable: bar graph or Parto chart
Two+ nominal independent and one scale dependent variable: bar graph
Chapter 4
Central Tendency
mean – arithmetic average, add up all scored, divide by number of scores
medium – middle score
mode – most common score
Calculating the mean
- add all scores, divide by numbe

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