Using a full data model/partial data model approach to address missing data Selecting variables using single-antecedent association rulesīinning scale variables to address missing data Selecting variable using the CHAID modeling node Removing redundant variables using correlation matrices Running a Statistics node on anti-join to evaluate potential missing data Using the Feature Selection node creatively to remove, or decapitate, perfect predictors Using an multiple Derive to explore missing dataĬreating an outlier report to give to SMEsĭetecting potential model instability early using the Partition node and Feature Selection Using a single cluster K-means as an alternative to anomaly detection Using CHAID stumps when interviewing an SME Using an empty aggregate to evaluate sample sizeĮvaluating the need to sample from the initial data This book is based around the needs of undergraduate students embarking on their own research project, and its self-help style is designed to boost the skills and confidence of those that will need to use SPSS in the course of their research project. Part II: Working with SPSS and Data Entry Ĭhapter 7 - Designing and conducting questionnaires with Data Entry Ĭhapter 9 - Computation and classification of variablesĬhapter 10 - Selecting, sorting and weighting casesĬhapter 11 - Merging, aggreating and transposing data files Ĭhapter 15 - Analysing multiple responses Ĭhapter 16 - Describing groups and testing the differences Īccessibly written and easy to use, Applied Statistics Using SPSS is an all-in-one self-study guide to SPSS and do-it-yourself guide to statistics. ![]() Chapter 1 - Background to SPSS for Windows Ĭhapter 2 - The use of SPSS in statistical research Ĭhapter 3 - From data source to data file Ĭhapter 5 - Session 2: Charts and computations Ĭhapter 6 - Session 3: Performing statistical analyses
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