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Final

FinalExam - Notes.docx

5 Pages
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Department
Psychology
Course Code
PSYC11H3
Professor
Karen Dion

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CHAPTER 17 – DATA MANAGEMENT AND EXPLORATION Data Management: - Activities concerned with preparing data for analysis - Involves putting the information generated by research into a form that can be subjected to statistical analyses of one sort or another - Involves ensuring that records are kept and the data are stored in such a way that they can be referred to and used in the future Rows: Represent units of analysis or cases (e.g. individuals who have been interviewed) - A column is assigned to each variables about with data has been collected To construct a data matrix, 3 questions need be addressed: o 1. Variables or columns of the matrix must be defined o 2. Unit of analysis in this particular study (Definition of rows of the matrix) o 3. How values on specific variables are defined (what values are used to represent different responses to each variable) Errors of Generalization: Often occur when the unit of analysis is not the same level as the unit to which we seek to generalize - Units of analysis should be statistically significant o The data from one case should not influence or have been influenced by the data from another case o Nominal units must be coded numerically for statistical analysis o Must preserve rank order in coding scheme E.g. We cannot use individual as the unit if all we have are group-level data The Codebook: - Is essential to guarantee that in the future the coded can be interpreted - Organization of the data matrix and of all scoring procedures and coding decisions - Codebook should be complete and detailed, listing names and locations of computer files containing the data as well as coding decisions, the meaning of variable labels Proofing Data: Verifying that the values in the data matrix are correct (essential step in the data management process) Steps To Data Management: 1) Data Collection 2) Codebook created 3) Data read into a statistical software package 4) Data analysis and interpretation Goal of Data Analysis: - Figure out what the data can tell us - What those answers might be - Future interesting questions Frequency Distribution: When data are sorted, counted, and displayed from lowest to highest and total at the end, with frequency and cumulative frequency - Useful for looking at the shape of a distribution of data on a variable Frequency Histogram: Placing values along the horizontal axis, the frequencies along the vertical axis (grouping values together) Stem-And-Leaf Diagram: First, the first digits for all observed values are written in a column on the left side of the diagram (stem), second digit of the number is written to the right of its stem (leaf) Descriptive Statistics: Numbers that describe the different characteristics of a distribution of scores on a variable E.g. Central Tendency: Mean and median Spread of a Distribution: - Measure of spread = interquartile range (E.g. How far apart the twenty-fifth and seventy-fifth percentile scores are) Variance: The average squared difference between the cases in a distribution and he mean of a distribution - How far in squared units the average case is from the mean - Outliers have a big effect on mean and variance Skewness: Another form of distribution looking at the direction in which the data is skewed (e.g. Positively skewed = Long tail at the upper end) Three Distributions: 1) Central tendency 2) Spread/variability 3) Skewness - When outliers are present rely on median and inter-quartile range CHAPTER 2 – EVALUATING SOCIAL SCIENCE THEORIES & RESEARCH - Theory: Set of interrelated hypotheses that is used to explain a phenomenon and make predictions about associations among constructs relevant to the phenomenon Theory About Social Behaviour Has 3 Features: 1. Contains constructs that are of theoretical interest and that it attempts to explicate or account for in some way 2. Describes associations among these constructs (frequently causal) 3. Incorporates hypothesized links between the theoretical constructs and observable variables that can be used to measure the constructs a. Links specify behaviours or other indicators used to conduct empirical research Hypotheses: Comprised of statements about associations among constructs as well as between constructs and obervable indicators E.g. 1. Contact with members of other ethnic groups decreases prejudice when in equal status settings E.g. 2. The SAT or ACT is a valid measure of scholastic achievement Productive Theory - Falsifiable (can’t have a hypothesis that explains every possible outcome) - States hypotheses as specifically as possible - Is as parsimonious (cost-effective) as possible - Addresses an important social phenomenon - Is internally consistent e.g. The hypotheses do not contradict one another - Is coherent and comprehensible - Specifies its relevant constructs and how they are measured - Agrees with what is already known about the topic - Explains data better than existing theories on the same topic - Agrees with existing theories about related topics - Generates new insights about the topic - A theory must be productive or useful and address some important or significant phenomenon or social behaviour that needs explication - The theory must provide a plausible and empirically defensible explanation for that phenomenon (at a particular historical moment) - Theory must be consistent with both existing research findings and existing theories for related phenomena - Good theory offers new insights or offers the possibility of unforeseen implications Measurement Research: Research designed to examine whether a given variable accurately or validly measures a given construct - Is conducted by examining whether two or more ways of measuring the same construct give
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