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PSYC 1010 (115)
Lecture

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Department
Psychology
Course
PSYC 1010
Professor
Harvey Marmurek
Semester
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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