Textbook Notes (369,153)
Canada (162,424)
Psychology (9,699)
PSYB07H3 (12)
Chapter 1

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Dwayne Pare

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PSYB07 Sept 14/2012 Chapter 1 IMPORTANT TERMS - random sample: ensures that each and every element of the population has an equal chance of being selected; ex. Drawing names from a hat - randomly assign: which subjects are part of which groups - population  sample  random sample  random assignment - population: the entire collection of events in which you are interested; ex. All the self-esteem scores of all high school students in the US – the collection of all the students’ self-esteem scores would form a population - since the populations are usually too large to measure, we draw a sample of observations from that population and use it to infer something about the characteristics of the population - external valididty: whether the sample reflects the population to which it is intended to make inferences - non-random sample: intended to closely reflect what would be obtained with a truly random sample - one person’s sample may be another one’s population - random assignment is concerned with internal validity: ensures that the results obtained are the results of the differences in the way the groups were treated, not a result of who happened to be placed in the groups; ex. if all timid ppl were put in one group, and all assertive ppl put in the other, the results would likely be a function of group assignment, and less so about the difference in treatment - random assignment is usually far more important than random sampling - variable: a property of an object or event that can take on different values; ex. hair colour is a variable because it is a property of an object (hair) and can take on different values (brown, yellow, red, grey etc.) - independent variable: the controlled variable - dependent variable: the data - the study is about the independent variables, and the results of the study (the data) are the dependent variables - discrete variables: can take on only a limited number of values; ex. gender - continuous variables: can assume any value between the lowest and highest points on the scale; ex. age - quantitative data (aka measurement data): the results of any sort of measurement, usually measured with some sort of instrument; the interest is “how much” of some property a particular object represents; ex. peoples’ weights, grades on a test - categorical data (aka frequency data or qualitative data): categorizing things where frequencies exist for each category; ex. the categories of “highly anxious”, “neutral”, and “low anxious” DESCRIPTIVE AND INFERENTIAL STATISTICS - descriptive statistics: when the purpose is to merely describe the data - exploratory data analysis (EDA): created by John Tukey; it is necessary to pay close attention to the data and examine it in detail before invoking more technically involved procedures - inferential statistics: since it is not
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