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Carleton University
PSYC 2002

Wednesday, January 8, 2014 PSYC 2002-B Introduction to Statistics in Psychology: Chapter 1:An Introduction to Statistics and Research Design Week One Readings: A1-7 pp. 1-20 Chapter 1 Statistics is a research tool for evaluating data: Data: is a set of scores or measurements Statistics: Anumerical fact derived from data - Statistics are used to evaluate the results of experiments; did the data support the hypothesis? - Quantify behaviour about data and its theoretical implications Two branches of statistics: 1. Descriptive statistics - Organizes, summarizes and communicates a group of numerical observations. - Summary of measurements (ex: average) 2. Inferential statistics - Uses sample data to make general estimates about the larger population. - Enable inferences to be drawn from data ** Descriptive statistics summarize numerical information about a sample. Inferential statistics draw conclusions about the broader population based on numerical information from a sample. Systematic versus random effects: - If two groups show a difference, is the difference attributable to a systematic difference or is the difference attribute to chance? - If the individuals were randomly assigned to the groups then it is possible to choose between these alternatives Generalization: would a set of results hold if another group of participants were used? o Sample- subset of individuals from a larger group o Population- the set of all individuals that have some characteristic Distinguishing between a sample and a population: Asample: is a set of observations drawn from the population of interest. Apopulation: includes all possible observations about which we’d like to know something. Generalization and random sampling: - Generalization from sample to population requires a random sample o Each member of the population has an equal chance of being selected o Selection of any one member is independent of the selection of the other members Scales of Measurement: - Type of observations determines appropriate type of statistic: o Nominal o Ordinal o Interval o Ratio Nominal and Ordinal Scales: 1. Nominal scale: observations categorized (ex: types of professions, political affiliation) 2. Ordinal scale: observations rank ordered (ex: Maclean’s ranking of universities 2013) (aka rank-ordered variable) Interval and Ratio Scales: 3. Interval scale: all categories have same size (Celsius temp) 4. Ratio scale: interval scale with an absolute zero point (kelvin temp, response time, percent correct) How to transform observations into variables: 1. Variables: are any observations of a physical, attitudinal, or behavioural characteristic that can take on different values. 2. Discrete observation: can take on only specific values (ex: whole numbers) no other values can exist between these numbers. Discrete and Continuous Variables: Discrete variables: indivisible categories (ex: number of participants in a condition, number of conditions in an experiment) Continuous variables: infinite number of possible values (ex: time, length) Statistical notation: N or n = number of scores, observations, ect. X Y Z = variable names ** We conduct research to see if the independent variable predicts the dependant variable ** Agood variable is both reliable and valid **Correlation research: when possible, researchers prefer to use an experiment rather than a correlational study. Experiments use random assignment, which is the only way to determine if one variable causes another. Chapter 1: Key terms Nominal variable: is a variable used for observations that have categories, or names, as their values. st nd Ordinal variable: is a variable used for observations that have rankings (ex: 1 , 2 , ect) 3. Continuous observation: can take on a full range of values (ex: numbers out to several decimal places) an infinite number of potential values exist. Interval variable: is a variable used for observations that have numbers as their values; the distance (or interval) between pairs of consecutive numbers is assumed to be equal. Ratio variable: is a variable that meets the criteria for an interval variable but also has a meaningful zero point. Variables and Research Levels: are discrete values of conditions that a variable can take on. An independent variable: Have at least two levels that we either manipulate or observe to determine its effects on the dependent variable. Adependant variable: is the outcome variable that we hypothesize to be related to or caused by changes in the independent variable. Aconfounding variable: is any variable that systematically varies with the independent variable so that we cannot logically determine which variable is at work, also called a Confound. Reliability: refers to the consistency of a measure. Validity: refers to the extent to which a test actually measures what it is intended to measure. Hypothesis testing: is the process of drawing conclusions about whether a particular relation between variables is supported by the evidence. An operational definition: specifies the operations or procedures used to measure or manipulate a variable. Acorrelation is an association between two or more variables. In random assignment: every participant in a study has an equal chance of being assigned to any of the groups, or experimental conditions in the study. An experiment: is a study in which participants are randomly assigned to a condition or level of one or more independent variables. In a between-groups design: participants experience one, and only one level of the independent variable. In a within-groups design: all participants in the study experience the different levels of the independent variable, also called a repeated measures design. Friday, January 10, 2014 PSYC 2002-B Introduction to Statistics in Psychology: Chapter 2: Frequency Distributions Week one Readings: A1-7 pp. 1-20 Chapter 1 pp. 21-42 Chapter 2 Note: Pass Workshop: Wed 6-7:30 LAA204 Thurs 2:30-4 CB 2302 Frequency Distributions: Frequency distribution: number of individual scores associated with each category in some type of measurement system; arguably, simplest type of descriptive statistic ** Afrequency table shows the pattern of the data by indicating how many participants had each possible score. The data in a frequency table can be graphed in a frequency histogram or a frequency polygon. Goal- organize data o Scores ranked lowest to highest o Same scores grouped together Benefits of frequency distributions: - Can see how all scores from group are distributed: o How many high o How many low o How they are arranged Frequency Distribution tables: components - X = score - F = number of scores for a given sco
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