Details

Autor(en) / Beteiligte

• Abbott, Martin,

Titel

Using statistics in the social and health sciences with SPSS and Excel

Auflage

1st ed

Ort / Verlag

Hoboken, New Jersey : Wiley,

Erscheinungsjahr

2017

Beschreibungen/Notizen

• Bibliographic Level Mode of Issuance: Monograph
• Includes bibliographical references and index.
• Cover -- Title Page -- Copyright -- Dedication -- Contents -- Preface -- Acknowledgments -- Chapter 1 Introduction -- Big Data Analysis -- Visual Data Analysis -- Importance of Statistics for the Social and Health Sciences and Medicine -- Historical Notes: Early Use of Statistics -- Approach of the Book -- Cases from Current Research -- Research Design -- Focus on Interpretation -- Coverage of Statistical Procedures -- Chapter 2 Descriptive Statistics: Central Tendency -- What is the Whole Truth? Research Applications (Spuriousness) -- Descriptive and Inferential Statistics -- The Nature of Data: Scales of Measurement -- Nominal Data -- Ordinal Data -- Interval Data -- Ratio Data -- Choosing the Correct Statistical Procedure for the Nature of Research Data -- Descriptive Statistics: Central Tendency -- Mean -- Median -- Mode -- Central Tendency and Levels of Data -- Using SPSS® and Excel to Understand Central Tendency -- SPSS® -- Excel -- Distributions -- Describing the Normal Distribution: Numerical Methods -- Central Tendency -- Skewness -- Kurtosis -- Descriptive Statistics: Using Graphical Methods -- Frequency Distributions -- Column Charts" in Excel -- Bar Charts" and "Histograms -- Bar Charts and Histograms in SPSS® -- Terms and Concepts -- Data Lab and Examples (with Solutions) -- Data Lab: Solutions -- Chapter 3 Descriptive Statistics: Variability -- Range -- Percentile -- Scores Based on Percentiles -- Using SPSS® and Excel to Identify Percentiles -- SPSS® Procedures -- Excel Procedures -- Standard Deviation and Variance -- Calculating the Variance and Standard Deviation -- The Deviation Method -- The Average Deviation -- The Computation Method -- The Sum of Squares -- Population SD and Inferential SD -- Obtaining SD from Excel and SPSS® -- Terms and Concepts -- Data Lab and Examples (with Solutions) -- Data Lab: Solutions.
• Chapter 4 The Normal Distribution -- The Nature of the Normal Curve -- The Standard Normal Score: Z Score -- The Z Score Table of Values -- Navigating the Z Score Distribution -- Calculating Percentiles -- Creating Rules for Locating Z Scores -- Calculating Z Scores -- Working with Raw Score Distributions -- Using SPSS® to Create Z Scores and Percentiles -- Creating Z Scores -- Creating Percentiles in SPSS® -- Using Excel to Create Z Scores -- STANDARDIZE Function -- Using Excel and SPSS® for Distribution Descriptions -- NORM.S.DIST Function -- NORM.DIST Function -- Terms and Concepts -- Data Lab and Examples (with Solutions) -- Data Lab: Solutions -- Chapter 5 Probability And the Z Distribution -- The Nature of Probability -- Elements of Probability -- Empirical Probability -- Combining Probabilities -- Combining Probabilities: Addition Rule -- Combining Probabilities: Multiplication Rule -- Combinations and Permutations -- Combination -- Permutation -- Conditional Probability: Using Bayes' Theorem -- Z Score Distribution and Probability -- Transforming a Raw Score to a Z Score: Statistical Testing -- Transforming a Z Score to a Raw Score: Estimation -- Transforming Cumulative Proportions to z Scores -- Deriving Sample Scores from Cumulative Percentages -- Using SPSS® and Excel to Transform Scores -- Using the Attributes of the Normal Curve to Calculate Probability -- Calculating "Areas" of the Standard Normal Distribution -- Inclusion Area Example -- Exclusion Area Example -- Exact" Probability -- Estimating "Exact" Probabilities -- From Sample Values to Sample Distributions -- Terms and Concepts -- Data Lab and Examples (with Solutions) -- Data Lab: Solutions -- Chapter 6 Research Design and Inferential Statistics -- Research Design -- Theory -- Hypothesis -- Types of Research Designs -- Experiment -- Randomization -- Control and Treatment Groups.
• Variables -- Quasi-Experimental Design -- Non-Experimental or Post Facto Research Designs -- The Nature of Research Design -- Research Design Varieties -- Sampling -- Inferential Statistics -- One Sample from Many Possible Samples -- Central Limit Theorem and Sampling Distributions -- The Sampling Distribution and Research -- Populations and Samples -- The Standard Error of the Mean -- Transforming" the Sample Mean to the Sampling Distribution -- Example -- Findings -- Discussion -- Z Test -- The Hypothesis Test -- Statistical Significance -- Practical Significance: Effect Size -- Z Test Elements -- Using SPSS® and Excel for the Z Test -- Terms and Concepts -- Data Lab and Examples (with Solutions) -- Data Lab: Solutions -- Chapter 7 The T Test for Single Samples -- Introduction -- Z Versus T: Making Accommodations -- Research Design -- Experiment -- Post Facto: Comparative Design -- Parameter Estimation -- Estimating the Population SD -- New Symbol Sx: The Estimated SD of the Population -- Biased versus Unbiased Estimates -- New Symbol Sm: The Estimated SD of the Sampling Distribution of Means (or Simply, "Standard Error of the Mean") -- The T Test -- Degrees of Freedom -- The T Test: A Research Example -- Sample and Population Means (M- ) -- The Estimated Population SD (Sx) and Estimated Standard Error of the Mean (Sm) -- Calculating the T Ratio Value -- Interpreting the Results of the T Test for a Single Mean -- The T Distribution -- Using df with the T Distribution -- The Hypothesis Test for the Single Sample T Test -- Type I and Type II Errors -- Type I (Alpha) Errors ( ) -- Type II (Beta) Errors ( ) -- Areas of Comparison Distributions -- Effect Size -- Effect Size for the Single Sample T Test -- Another Measurement of the (Cohen's d) Effect Size -- Power, Effect Size, and Beta -- One- and Two-Tailed Tests -- Two-Tailed Tests.
• One-Tailed Tests -- Choosing a One- or Two-Tailed Test -- A Note about Power -- Point and Interval Estimates -- Calculating the Interval Estimate of the Population Mean -- The Value of Confidence Intervals -- Using SPSS® and Excel with the Single Sample T Test -- SPSS® and the Single Sample T test -- Excel and the Single Sample T test -- Terms and Concepts -- Data Lab and Examples (with Solutions) -- Data Lab: Solutions -- Chapter 8 Independent Sample T Test -- A Lot of "Ts -- Research Design -- Experimental Designs and the Independent T Test -- Dependent Sample Designs -- Between and Within Research Designs -- Using Different T Tests -- Pre-test or No Pre-test -- Example of Experiment -- Post Facto Designs -- Independent T Test: The Procedure -- Creating the Sampling Distribution of Differences -- The Nature of the Sampling Distribution of Differences -- The Mean and Standard Deviation of the Sampling Distribution of Differences -- Calculating the Estimated Standard Error of Difference with Equal Sample Size -- The Degrees of Freedom for the Independent T Test -- Using Unequal Sample Sizes -- Derivation of the Formula for Unequal Sample Sizes -- The Independent T Ratio -- Independent T Test Example -- The Setting -- The Research Data -- Hypothesis Test Elements for the Example -- The Null Hypothesis -- The Alternative Hypothesis -- The Critical Value of Comparison -- The Calculated T Ratio -- Statistical Decision -- Interpretation -- Research Design of the Example -- Before-After Convention with the Independent T Test -- Confidence Intervals for the Independent T Test -- Effect Size -- Cohen's d Method -- The Eta Squared Method -- The Assumptions for the Independent T Test -- Assumptions 1 and 2: Independence and Interval Level -- Assumption 3: Normal Distribution of Sample Groups -- Assumption 4: Equal Variance.
• SPSS® Explore for Checking the Normal Distribution Assumption -- Excel Procedures for Checking the Equal Variance Assumption -- F Distribution -- Use "Right" Side Critical Values -- What Outcome Meets the Assumption for Equality of Variances? -- SPSS® Procedure for Checking the Equal Variance Assumption -- The Homogeneity of Variance Assumption for the Independent T Test -- A Rule of Thumb -- Using SPSS® and Excel with the Independent T Test -- SPSS® Procedures for the Independent T Test -- Excel Procedures for the Independent T Test -- Effect Size for the Independent T Test Example -- Parting Comments -- Nonparametric Statistics: The Mann-Whitney U Test -- Terms and Concepts -- Data Lab and Examples (with Solutions) -- Data Lab: Solutions -- Graphics in the Data Summary -- Chapter 9 Analysis of Variance -- A Hypothetical Example of ANOVA -- The Nature of ANOVA -- The Components of Variance -- The Process of ANOVA -- Calculating ANOVA -- Calculating the Variance: Using the Sum of Squares (SS) -- Calculating Components of Variance Using SS -- Creating a Data Table -- Using Mean Squares (MS) -- Degrees of Freedom in ANOVA -- Calculating Mean Squares (MS) -- The F Ratio -- The F Distribution -- Effect Size -- Post Hoc Analyses -- Varieties" of Post Hoc Analyses -- The Post Hoc Analysis Process -- Tukey's HSD (Range) Test Calculation -- Mean Comparison Table -- Compare Mean Difference Values from HSD -- Post Hoc Summary -- Assumptions of ANOVA -- Additional Considerations with ANOVA -- The Hypothesis Test: Interpreting ANOVA Results -- Are the Assumptions Met? -- Is Population Normally Distributed? -- Are the Variances Equal? -- Are the Samples Independently Chosen? -- Does the Dependent Variable Consist of Interval Data? -- Manual Calculations -- Post Hoc Analysis -- Using SPSS® and Excel with One-Way ANOVA -- SPSS® Procedures with One-Way ANOVA.
• General Linear Model (GLM) Approach to Analyzing ANOVA.
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• English
• Description based on online resource; title from PDF title page (ebrary, viewed August 21, 2016).