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Lecture 1.docx

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PSYC 2002
Steven Carroll

Lecture 1 Some definitions - Population • The set of ALL the individuals of interest in a particular study • Vary in size, but can be quite large • Populations are described by parameters - Sample • Aset of individuals SELECTED from a population • Usually intended to represent the population in a research study • Samples are described by statistics - This distinction is very important in stats: the formulas change depending on whether you are dealing with a population or a sample Sampling error - Asample is never identical to a population! • People are all weird. Everyone is different • This weirdness causes data to shift in random ways - Sampling error • The discrepancy, or amount of error, that exists between a sample statistic and the corresponding population parameter Sampling error (textbook example) - Imagine a population of 1000 Carleton PSYC 2002 students - Parameters: • Average age= 21.3 • Average IQ= 112.5 • 65% female, 35% male Sampling error - Sample #1: Jen, Emily, Sue, Paul, Melissa - Sample statistics: • Average age= 19.8 • Average IQ= 104.6 • 80% female, 20% male Sampling error - Sample #1: Kermit, Fozzie, Gonzo, Piggy, Camilla - Sample statistics: • Average age= 20.4 • Average IQ= 114.2 • 40% female, 60% male This is NOT surprising - Small samples tend to have a lot of variability - We should not be surprised if a small sample does not reflect the population Variables and data - Variable • Characteristic or condition that changes • Either manipulated or observed • Dependent or independent Independent and dependent variables - Independent variables (IVs) • The variable manipulated by the researcher • You choose the level of the manipulated variable - Dependent variables (DVs) • The variables measured by the researcher • The levels of these variables tell you the effect of manipulating the IVs Variables and data - Data • Measurements of a variable - Data set • Acollection of measurements - A datum • Asingle measurement or observation • Commonly called a score or a raw score Types of statistics Descriptive - Descriptive statistics • Summarize data • Organize data • Simplify data - Familiar examples • Tables • Graphs • Averages (aka means) Types of statistics Inferential - Inferential statistics • Study samples to make generalizations about the population • Interpret experimental data • VERY USEFUL - Unfamiliar (to some) examples • Z tests • T tests • F tests Types of studies - Experiments: • Carefully controlled, usually taking place in a laboratory • IVs are directly manipulated to see if there is any affect on the DVs • Well designed experiments let you make definitive statements about “cause-and- effect” - Quasi-experiments: • When you can’t completely control one or more of the IVs • Example: non-equivalent groups  You compare groups, but you can’t control who goes into which group • In these cases, the independent variable is quasi-dependent - Correlational: • When you observe a relationship between two variables, but can’t say with certainty which is the IV and which is the DV  So no IV is actually required • Example: I notice that the more comic books people read, t
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