Best Practices in Data Cleaning: A Complete Guide to Everything You Need to Do Before and After Collecting Your Data
Many researchers jump from data collection directly into testing hypothesis without realizing these tests can go profoundly wrong without clean data. This book provides a clear, accessible, step-by-step process of important best practices in preparing for data collection, testing assumptions, and examining and cleaning data in order to decrease error rates and increase both the power and replicability of results.
Jason W. Osborne, author of the handbook Best Practices in Quantitative Methods (SAGE, 2008) provides easily-implemented suggestions that are evidence-based and will motivate change in practice by empirically demonstrating—for each topic—the benefits of following best practices and the potential consequences of not following these guidelines.
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Debunking the Myth of Robustness
Section I Best Practices as You Prepare for Data Collection
Debunking the Myth of Adequate power
Debunking the Myth of Representativeness
debunking the Myth of Equality
Section II Best Practices in Data Cleaning and Screening
Debunking the Myth of Perfect Data
Debunking the Myth of Emptiness
Debunking the Myth of Perfect Measurement
Section III Advanced Topics in Data cleaning
debunking the Myth of the Motivated Participant
Debunking the Myth of Categorization
Lots of Pits in Which to Fall
Visions of Rational Quantitative Methodology for the 21st Century
Debunking the Myth of Equality
debunking the Myth of Distributional Irrelevance
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10th grade alpha analyses ANOVA appropriate assessing assumptions average best practices bias biased Box-Cox transformations calculated chapter coefficient of determination complex sampling continuous variables correct correlation data cleaning data points data set data transformations detect dichotomization disattenuation educational psychology effect size effect sizes error rates estimates examine example explore extreme scores Figure generalizability goal groups important individuals issues Journal kurtosis lambda logistic regression math achievement MCAR mean substitution methodology methods misestimation missing data missingness MNAR MNAR-extreme MNAR-inverse modeling multiple imputation multiple regression nonnormal normally distributed null hypothesis odds ratio Osborne outcomes outliers participants perform potential probability quantitative random responding randomly relationship relatively reliability repeated measures replicability residuals response sets restriction of range robust scale significant simple skew social sciences socioeconomic status SPSS standard deviation substantial survey test scores tion Type I error values variance accounted violations weights z score