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9781506304168
Interpreting and Using Statistics in Psychological Research / Edition 1 available in Paperback, eBook
Interpreting and Using Statistics in Psychological Research / Edition 1
by Andrew N. Christopher
Andrew N. Christopher
- ISBN-10:
- 1506304168
- ISBN-13:
- 9781506304168
- Pub. Date:
- 09/29/2016
- Publisher:
- SAGE Publications
- ISBN-10:
- 1506304168
- ISBN-13:
- 9781506304168
- Pub. Date:
- 09/29/2016
- Publisher:
- SAGE Publications
Interpreting and Using Statistics in Psychological Research / Edition 1
by Andrew N. Christopher
Andrew N. Christopher
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Overview
This practical, conceptual introduction to statistical analysis by award-winning teacher Andrew N. Christopher uses published research with inherently interesting social sciences content to help students make clear connections between statistics and real life. Using a friendly, easy-to-understand presentation, Christopher walks students through the hand calculations of key statistical tools and provides step-by-step instructions on how to run the appropriate analyses for each type of statistic in SPSS and how to interpret the output. With the premise that a conceptual grasp of statistical techniques is critical for students to truly understand why they are doing what they are doing, the author avoids overly formulaic jargon and instead focuses on when and how to use statistical techniques appropriately.
Product Details
ISBN-13: | 9781506304168 |
---|---|
Publisher: | SAGE Publications |
Publication date: | 09/29/2016 |
Edition description: | First Edition |
Pages: | 584 |
Product dimensions: | 7.40(w) x 9.00(h) x 0.80(d) |
About the Author
Andrew (Drew) N. Christopher grew up in Plano, Texas. He received his undergraduate degree from Stetson University in 1992 with a major in economics and finance and a minor in psychology. He holds an MBA from Southern Methodist University and his Ph.D. from the University of Florida. Drew has taught at Albion College since 2001. In addition to teaching courses in research design and analysis, he also teaches “Introductory Psychology,” “Industrial/Organizational Psychology,” “Senior Research Seminar,” and an honor’s college course called “Black Swans and Everyday Life,” and is developing a new first-year seminar titled “Football and American Society.” He has published more than 30 peer-reviewed papers with 28 undergraduate authors since arriving at Albion. Many more undergraduate collaborators have presented their work at venues such as the International Society for the Scientific Study of Individual Differences (ISSID), Association for Psychological Science (APS), Michigan Undergraduate Psychology Research Conference (MUPRC), and Albion College’s Elkin Isaac Research Symposium. Drew has twice been named Albion College’s Phi Beta Kappa Scholar of the Year. In recognition of his work with students, he was awarded the Robert S. Daniel Excellence in Teaching Award at a 4-year college or university in 2013 and named his College’s Teacher of the Year in 2014. He has been editor-in-chief of the Society for the Teaching of Psychology’s journal, Teaching of Psychology, since 2009.Away from academic responsibilities, Drew works out regularly, not because he enjoys doing so (in fact, he hates it) but because it allows him to eat foods that he probably otherwise should not eat so much of. Toward that end, he enjoys cooking and is particularly adept at making various types of pizza and a wide range of unhealthy desserts. Any leftovers from his creations are gladly consumed by his two beagles, Sybil and Hans. Drew enjoys almost all sports, particularly college football and professional hockey. As a University of Florida graduate, Drew maintains his loyalty to the Southeastern Conference despite living in Big Ten territory. As a Tampa Bay Lightning fan living in south central Michigan, he is a regular recipient of dirty looks from Detroit Red Wings and Chicago Blackhawks fans who populate the area.
Table of Contents
PrefaceAcknowledgmentsAbout the AuthorChapter 1- Why Do I Have to Learn Statistics? The Value of Statistical Thinking in LifeStatistical Thinking and Everyday LifeFailing to Use Information About ProbabilityAvailability heuristicRepresentativeness heuristicMisunderstanding Connections Between EventsIllusory correlationsGambler’s fallacyGoals of ResearchGoal: To DescribeGoal: To PredictGoal: To ExplainGoal: To ApplyStatistical Thinking: Some Basic ConceptsParameters Versus StatisticsDescriptive Statistics Versus Inferential StatisticsSampling ErrorChapter Application QuestionsQuestions for Class DiscussionChapter 2- Basics of Quantitative Research: Variables, Scales of Measurement, and an Introduction to the Statistical Package for the Social Sciences (SPSS)The StudyVariablesOperational DefinitionsMeasurement Reliability and ValidityScales of Measurement: How We Measure VariablesNominal DataOrdinal DataInterval and Ratio (Scale) DataDiscrete Versus Continuous VariablesThe Basics of SPSSVariable ViewData ViewChapter Application QuestionsQuestions for Class DiscussionChapter 3- Describing Data With Frequency Distributions and Visual DisplaysThe StudyFrequency DistributionsFrequency Distribution TablesFrequency Distribution GraphsCommon Visual Displays of Data in ResearchBar GraphsScatterplotsLine GraphsUsing SPSS to Make Visual Displays of DataMaking a Bar GraphMaking a ScatterplotMaking a Line GraphChapter Application QuestionsQuestions for Class DiscussionChapter 4- Making Sense of Data: Measures of Central Tendency and VariabilityMeasures of Central TendencyThree Measures of Central TendencyMeanMedianModeReporting the measures of central tendency in researchChoosing a Measure of Central TendencyConsideration 1: Outliers in the dataConsideration 2: Skewed data distributionsConsideration 3: A variable’s scale of measurementConsideration 4: Open-ended response rangesMeasures of Central Tendency and SPSSMeasures of VariabilityWhat Is Variability? Why Should We Care About Variability?Three Measures of VariabilityRangeVarianceStandard deviationReporting variability in researchMeasures of Variability and SPSSChapter Application QuestionsQuestions for Class DiscussionChapter 5- Determining “High” and “Low” Scores: The Normal Curve, z Scores, and ProbabilityTypes of DistributionsNormal DistributionsSkewed DistributionsStandardized Scores (z Scores)z Scores, the Normal Distribution, and Percentile RanksLocating Scores Under the Normal DistributionPercentile Ranksz Scores and SPSSChapter Application QuestionsQuestions for Class DiscussionChapter 6- Drawing Conclusions From Data: Descriptive Statistics, Inferential Statistics, and Hypothesis TestingBasics of Null Hypothesis TestingNull Hypotheses and Research HypothesesAlpha Level and the Region of Null Hypothesis RejectionGathering Data and Testing the Null HypothesisMaking a Decision About the Null HypothesisType I Errors, Type II Errors, and Uncertainty in Hypothesis TestingThe z TestA Real-World Example of the z TestIngredients for the z TestUsing the z Test for a Directional (One-Tailed) HypothesisUsing the z Test for a Nondirectional (Two-Tailed) HypothesisOne-Sample t TestA Real-Word Example of the One-Sample t TestIngredients for the One-Sample t TestUsing the One-Sample t Test for a Directional (One-Tailed) HypothesisUsing the One-Sample t Test for a Nondirectional (Two-Tailed) HypothesisOne-Sample t Test and SPSSStatistical Power and Hypothesis TestingChapter Application QuestionsQuestions for Class DiscussionChapter 7- Comparing Two Group Means: The Independent Samples t TestConceptual Understanding of the Statistical ToolThe StudyThe ToolIngredientsHypothesis from Kasser and Sheldon (2000)Interpreting the ToolAssumptions of the toolTesting the null hypothesisExtending our null hypothesis testUsing Your New Statistical ToolHand-Calculating the Independent Samples t TestStep 1: State hypothesesStep 2: Calculate the mean for each of the two groupsStep 3: Calculate the standard error of the difference between the meansStep 4: Calculate the t test statisticStep 5: Determine degrees of freedom (dfs)Step 6: Locate the critical valueStep 7: Make a decision about the null hypothesisStep 8: Calculate an effect sizeStep 9: Determine the confidence intervalIndependent Samples t Test and SPSSEstablishing your spreadsheetRunning your analysesWhat am I looking at? Interpreting your SPSS outputChapter Application QuestionsQuestions for Class DiscussionChapter 8- Comparing Two Repeated Group Means: The Paired Samples t TestConceptual Understanding of the ToolThe StudyThe ToolIngredientsHypothesis from Stirling et al. (2014)Interpreting the ToolTesting the null hypothesisExtending our null hypothesis testAssumptions of the toolUsing Your New Statistical ToolHand-Calculating the Paired Samples t TestStep 1: State hypothesesStep 2: Calculate the mean difference scoreStep 3: Calculate the standard error of the difference scoresStep 4: Calculate the t test statisticStep 5: Determine degrees of freedom (dfs)Step 6: Locate the critical valueStep 7: Make a decision about the null hypothesisStep 8: Calculate an effect sizeStep 9: Determine the confidence intervalPaired Samples t Test and SPSSEstablishing your spreadsheetRunning your analysesWhat am I looking at? Interpreting your SPSS outputChapter Application QuestionsQuestions for Class DiscussionChapter 9- Comparing Three or More Group Means: The One-Way, Between-Subjects Analysis of Variance (ANOVA)Conceptual Understanding of the ToolThe StudyThe ToolIngredientsAssumptions of the toolHypothesis from Eskine (2012)Interpreting the ToolTesting the null hypothesisExtending our null hypothesis testGoing beyond the F ratio: Post hoc testsUsing Your New Statistical ToolHand-Calculating the One-Way, Between-Subjects ANOVAStep 1: State hypothesesStep 2: Calculate the mean for each groupStep 3: Calculate the sums of squares (SSs)Total Sums of Squares (SStotal)Within-Groups Sums of Squares (SSwithin-groups)Between-Groups Sums of Squares (SSbetween-groups)Step 4: Determine degrees of freedom (dfs)Total Degrees of Freedom (dftotal)Within-Groups Degrees of Freedom (dfwithin-groups)Between-Groups Degrees of Freedom (dfbetween-groups)Step 5: Calculate the mean squares (MSs)Step 6: Calculate your F ratio test statisticStep 7: Locate the critical valueStep 8: Make a decision about the null hypothesisStep 9: Calculate an effect sizeStep 10: Perform post hoc testsOne-Way Between-Subjects ANOVA and SPSSEstablishing your spreadsheetRunning your analysisWhat am I looking at? Interpreting your SPSS outputChapter Application QuestionsQuestions for Class DiscussionChapter 10- Comparing Three or More Repeated Group Means: The One-Way, Repeated-Measures Analysis of Variance (ANOVA)Conceptual Understanding of the ToolThe StudyThe ToolBetween-subjects versus repeated-measures ANOVAsAssumptions of the toolHypothesis from Bernard et al. (2014)Interpreting the ToolTesting the null hypothesisExtending our null hypothesis testGoing beyond the F ratio: Post hoc testsUsing Your New Statistical ToolHand-Calculating the One-Way, Repeated-Measures ANOVAStep 1: State the hypothesisStep 2: Calculate the mean for each groupStep 3: Calculate the sums of squares (SSs)Total Sums of Squares (SStotal)Between Sums of Squares (SSbetween)Error Sums of Squares (SSerror)Step 4: Determine degrees of freedom (dfs)Total Degrees of Freedom (dftotal)Between Degrees of Freedom (dfbetween)Error Degrees of Freedom (dferror)Step 5: Calculate the mean squares (MSs)Step 6: Calculate your F ratio test statisticStep 7: Locate the critical valueStep 8: Make a decision about the null hypothesisStep 9: Calculate an effect sizeStep 10: Perform post hoc testsOne-Way, Repeated-Measures ANOVA and SPSSEstablishing your spreadsheetRunning your analysisWhat am I looking at? Interpreting your SPSS outputChapter Application QuestionsQuestions for Class DiscussionChapter 11- Analyzing Two or More Influences on Behavior: Factorial Designs for Two Between-Subjects FactorsConceptual Understanding of the ToolThe StudyThe ToolFactorial notationMain effects and interactionsHypothesis from Troisi and Gabriel (2011)Interpreting the ToolTesting the null hypothesisExtending the null hypothesis testsDissecting a statistically significant interactionUsing Your New Statistical ToolHand-Calculating the Two-Way, Between-Subjects ANOVAStep 1: State the hypothesesStep 2: Calculate the mean for each group and the marginal meansStep 3: Calculate the sums of squares (SSs)Total Sums of Squares (SStotal)Within-Groups Sums of Squares (SSwithin-groups)Between-Groups Sums of Squares (SSbetween-groups)Step 4: Determine degrees of freedom (dfs)Total Degrees of Freedom (dftotal)Within-Groups Degrees of Freedom (dfwithin-groups)Between-Groups Degrees of Freedom (dfbetween-groups)Step 5: Calculate the mean squares (MSs)Step 6: Calculate your F ratio test statisticsStep 7: Locate the critical valuesStep 8: Make a decision about each null hypothesisStep 9: Calculate the effect sizesStep 10: Perform follow-up testsTwo-Way, Between-Subjects ANOVA and SPSSEstablishing your spreadsheetRunning your analysisWhat am I looking at? Interpreting your SPSS outputDissecting interactions in SPSSChapter Application QuestionsQuestions for Class DiscussionChapter 12- Determining Patterns in Data: CorrelationsConceptual Understanding of the ToolThe StudyThe ToolTypes (directions) of correlationsStrength of correlationsAssumptions of the Pearson correlationUses for correlationsUse 1: Studying naturally occurring relationshipsUse 2: Basis for predictionsUse 3: Establishing measurement reliability and validityHypotheses from Clayton et al. (2013)Interpreting the ToolTesting the null hypothesisCautions in interpreting correlationsCaution 1: Don’t confuse type (direction) and strength of a correlationCaution 2: Range restrictionCaution 3: “Person-who” thinkingCaution 4: Curvilinear relationshipsCaution 5: Spurious correlationsUsing Your New Statistical ToolHand-Calculating the Person Correlation Coefficient (r)Step 1: State hypothesesStep 2: For both variables, find each participant’s deviation score and then multiply them togetherStep 3: Sum the products in step 2Step 4: Calculate the sums of squares for both variablesStep 5: Multiply the two sums of squares and then take the square rootStep 6: Calculate the correlation coefficient (r) test statisticStep 7: Locate the critical valueStep 8: Make a decision about the null hypothesisThe Pearson Correlation (r) and SPSSEstablishing your spreadsheetRunning your analysisWhat am I looking at? Interpreting your SPSS outputChapter Application QuestionsQuestions for Class DiscussionChapter 13- Predicting the Future: Univariate and Multiple RegressionUnivariate RegressionIngredientsHand-Calculating a Univariate RegressionStep 1: Calculate the slope of the line (b)Step 2: Calculate the y-intercept (a)Step 3: Make predictionsUnivariate Regression and SPSSRunning your analysisWhat am I looking at? Interpreting your SPSS outputMultiple RegressionUnderstanding Multiple Regression in ResearchMultiple Regression and SPSSEstablishing your spreadsheetRunning your analysisWhat am I looking at? Interpreting your SPSS outputChapter Application QuestionsQuestions for Class DiscussionChapter 14- When We Have Exceptions to the Rules: Nonparametric TestsChi-Square (x2) TestsChi-Square (x2) Goodness-of-Fit TestHand-calculating the 2*2 goodness-of-fit testStep 1: State hypothesesStep 2: Determine degrees of freedom (dfs)Step 3: Calculate the x2 test statisticStep 4: Find the critical value and make a decision about the null hypothesisx2 goodness-of-fit test and SPSSEstablishing your spreadsheetRunning your analysisWhat am I looking at? Interpreting your SPSS outputChi-Square (x2) Test of IndependenceHand-calculating the x2 test of independenceStep 1: State hypothesesStep 2: Determine degrees of freedom (dfs)Step 3: Calculate expected frequenciesStep 4: Calculate the x2 test statisticStep 5: Find the critical value and make a decision about the null hypothesisStep 6: Calculate an effect sizex2 test for independence and SPSSEstablishing your spreadsheetRunning your analysisWhat am I looking at? Interpreting your SPSS outputSpearman Rank-Order Correlation CoefficientHand-Calculating the Spearman Rank-Order CorrelationStep 1: State the hypothesisStep 2: Calculate the difference (D) score between each pair of rankingsStep 3: Square and sum the difference scores in step 2Step 4: Calculate the Spearman correlation coefficient (rs) test statisticStep 5: Locate the critical value and make a decision about the null hypothesisSpearman’s Rank-Order Correlation and SPSSEstablishing your spreadsheetRunning your analysisWhat am I looking at? Interpreting your SPSS outputMann-Whitney U TestHand-Calculating the Mann-Whitney U TestStep 1: State hypothesesStep 2: Calculate the ranks for categories being comparedStep 3: Sum the ranks for each categoryStep 4: Find the U for each groupStep 5: Locate the critical value and make a decision about the null hypothesisMann-Whitney U Test and SPSSEstablishing your spreadsheetRunning your analysisWhat am I looking at? Interpreting your SPSS outputChapter Application QuestionsQuestions for Class DiscussionChapter 15- Bringing It All Together: Using Your Statistical ToolkitDeciding on the Appropriate Tool: Six ExamplesStudy 1: “Waiting for Merlot: Anticipatory Consumption of Experiential and Material PurchasesStudy 2: “Evaluations of Sexy Women in Low- and High-Status Jobs”Study 3: “Evil Genius? How Dishonesty Can Lead to Greater Creativity”Study 4: “Differential Effects of a Body Image Exposure Session on Smoking Urge Between Physically Active and Sedentary Female Smokers”Study 5: “Texting While Stressed: Implications for Students’ Burnout, Sleep, and Well-Being”Study 6: “How Handedness Direction and Consistency Relate to Declarative Memory Task Performance”Using Your Toolkit to Identify Appropriate Statistical ToolsStudy 7: “Borderline Personality Disorder: Attitudinal Change Following Training”Study 8: “Effects of Gender and Type of Praise on Task Performance Among Undergraduates”Study 9: “Please Respond ASAP: Workplace Telepressure and Employee Recovery”Answers to Studies 7, 8, and 9Appendices: Statistical TablesGlossaryReferencesIndexFrom the B&N Reads Blog
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