Intro to Statistics
Intro to Statistics Psyc-21621
Popular in Quantitative Methods Psych I
Popular in Psychlogy
This 4 page Class Notes was uploaded by Amy Turk on Wednesday April 27, 2016. The Class Notes belongs to Psyc-21621 at Kent State University taught by Dr. Gordon in Spring 2016. Since its upload, it has received 3 views. For similar materials see Quantitative Methods Psych I in Psychlogy at Kent State University.
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Date Created: 04/27/16
PSYCH STATS POWERPOINT CHAPTER ONE ● 3 types of stats ○ values ■ batting averages, crime rates, etc. ○ procedures ○ field of study ■ using and applying stats to answer questions ● statistics are the tools we use to learn about various phenomenon ● 3 main functions: ○ organize ○ summarize ○ and interpret information ● organize ○ collect and examine all info ■ graphs ■ tables ○ no actual manipulations or calculations ○ data is still in raw form ■ most basic thing you could do with data ● summarize ○ represent all info ○ reduce all info to single values ■ describes different characteristics ■ simplest manipulations and calculations ● interpret ○ compare summarized info ■ requires multiple sets of info ○ complicated manipulations and calculations ■ built from summarized info ● procedures ○ 2 main types ■ descriptive statisticto desbribe ● mean, median, mode ● summarize, organize, simplify ■ inferential statisticprocedures to interpret ● steps of research ○ experiment/data collection ○ descriptive statistics ○ inferential statistics ■ being able to describe what’s really going on with data ● what do we use statistics on? ○ subjects/participants ○ characteristics ● variables:characteristics of individuals ○ differs across people or time ○ ex. happiness throughout the week ○ a thing that we’re looking at in a study ● data: measurements or observations of a variable ○ data set: collection of measurements/observations ○ datum: a single measurement ■ raw score ● population:set of all people of interest in a study ○ the goal of any study is to draw conclusions from the population ○ can be very large or very small ■ defined by research question ○ usually impossible to examine the entire population ● sample ● parameter: value that describes the population ○ taken from population measurements ● statistic: value that describes the sample ○ taken from measurements of the sample ○ the results from the sample are generalized to the population ● sampling error: statistics do not perfectly represent parameters ○ most likely due to chance = unsystematic differences between samples ● sometimes we know set values about the population ○ most of the time we assume something about the population ● sampling error isn’t important on the descriptive statistic level ● inferential studies… sampling error plays a major role ● correlational method ○ 2+ variables from one person ○ determines if there’s a relationship between them ○ observation, not manipulation ○ does x predict y? ○ is x related to y? ● correlation does not imply causation ○ because you didn’t manipulate anything ○ there could be another variable ● the experimental method ○ when you want to demonstrate a cause and effect relationship between variables ○ requires random assignment ○ determine causality between variables through manipulation and control ○ experimental variables: ■ independent variable: manipulated by the researcher ● usually consists of 2 or more conditions ○ control ○ experimental ■ dependent variable:observed to assess the effect of the treatment (IV) ● you must have a control group to compare your observations to ○ in order to determine causation, you must have a control group ● nonexperimental method ○ quasi-independent variable: not truly manipulated, but used to create groups ■ ex. gender ■ you can’t manipulate a participant’s gender ■ you can create groups based on gender ● types of variables ○ categorical:also called discrete ■ separate categories ■ no values in between categories ○ continuous ■ individual values can be divided into fractional parts ■ infinite number of values ● different scales of measurement ○ nominal ○ ordinal ○ interval ○ ratio ○ the type of scale of measurement determines what experiment we can do ● nominal ○ categories with names ○ no quantitative differences ○ ex. college major ● ordinal ○ categories are in an ordered sequence ○ ranked in terms of magnitude or size ○ no indication of the size of the differences ○ ex. size of the tshirt ■ you may know that one is better than the other but you dont’ know how much better ● interval ○ ordered categories ○ intervals between categories are same size ○ no meaningful zero points ■ you just put the zero somewhere as a place holder ● ratio ○ intervals between categories are same size ○ meaningful zero point ■ zero means the absence of something ● primary difference between ratios and intervals ● scales of measurement ○ define what statistic to use ○ scale reduction ○ ratio… interval… ordinal ○ nothing reduces to nominal ● operational definition ○ having a number that classifies ○ defining a variable using behaviors ● the research question defines the population ● notation ○ number of scores in a population: N ○ number of scores in a sample: n ○ summation: sigma ■ add them all together ○ set of scores in general = X ○ sigma X = add all scores of X ● arithmetic rules: ○ order of operations: ■ parentheses ■ exponents ■ multiplying/dividing ■ summation using sigma ■ addition and subtraction
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