IBM SPSS Statistics 26 Step by Step: A Simple Guide and Reference
Abstract
IBM SPSS Statistics 26 Step by Step: A Simple Guide and Reference, sixteenth edition, takes a straightforward, step-by-step approach that makes SPSS software clear to beginners and experienced researchers alike. Extensive use of four-color screen shots, clear writing, and step-by-step boxes guide readers through the program. Output for each procedure is explained and illustrated, and every output term is defined. Exercises at the end of each chapter support students by providing additional opportunities to practice using SPSS. This book covers the basics of statistical analysis and addresses more advanced topics such as multi-dimensional scaling, factor analysis, discriminant analysis, measures of internal consistency, MANOVA (between- and within-subjects), cluster analysis, Log-linear models, logistic regression and a chapter describing residuals. Back matter includes a description of data files used in exercises, an exhaustive glossary, suggestions for further reading and a comprehensive index. IMB SPSS Statistics 26 Step by Step is distributed in 85 countries, has been an academic best seller through most of the earlier editions, and has proved invaluable aid to thousands of researchers and students. New to this edition: Screenshots, explanations, and step-by-step boxes have been fully updated to reflect SPSS 26 How to handle missing data has been revised and expanded and now includes a detailed explanation of how to create regression equations to replace missing data More…
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Internal consistency for the distrust items was excellent (α = 0.92, ω = 0.92) and good for the trust items (α = 0.88, ω = 0.88).
“Internal consistency for the first five items (i.e., distrust) was excellent ( α = 0.92, 95% CI[0.90, 0.93], ω = 0.92, 95% CI[0.90, 0.93]) and good for the seven trust items ( α = 0.88, 95% CI[0.86, 0.90], ω = 0.88, 95% CI[0.86, 0.90]) according to George and Mallery (2019).”
From Exploring the effects of human-centered AI explanations on trust and reliance (2023) · cites this paper as George and Mallery (2019)
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Kaya, F., Aydin, F., Schepman, A., Rodway, P., Yetişensoy, O., & Demir Kaya, M. (2024) · International Journal of Human–Computer Interaction · doi:10.1080/10447318.2022.2151730 · Cited by 688 · see citing works
Exploring the effects of human-centered AI explanations on trust and reliance
Scharowski, N., Perrig, S. A. C., Svab, M., Opwis, K., & Brühlmann, F. (2023) · Frontiers in Computer Science · doi:10.3389/fcomp.2023.1151150 · Cited by 57 · see citing works
Help me help you: How HR analytics forecasts foster organizational creativity
Di Prima, C., Cepel, M., Kotaskova, A., & Ferraris, A. (2024) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2024.123540 · Cited by 46 · see citing works
Chat or cheat? Academic dishonesty, risk perceptions, and ChatGPT usage in higher education students
Ortiz-Bonnin, S., & Blahopoulou, J. (2025) · Social Psychology of Education · doi:10.1007/s11218-025-10080-2 · Cited by 40 · see citing works
“Also, the kurtosis value of 6.35 signifies a leptokurtic distribution with heavier tails and a more pronounced peak compared to a normal distribution (George & Mallery, 2019).”
InfluentialHuang, H.-W., Teng, D. C.-E., & Tiangco, J. A. N. Z. (2025) · Journal of Science Education and Technology · doi:10.1007/s10956-024-10179-9 · Cited by 23 · see citing works
Schwartzman, J. M., & Bonner, H. R. (2024) · Journal of Autism and Developmental Disorders · doi:10.1007/s10803-023-06039-x · Cited by 8 · see citing works
- Authors
- George, D., & Mallery, P.
- Venue
- Routledge eBooks
- Publisher
- Routledge
- Published
- 2019-12-06
- OpenAlex
- W4255990624
- Cited by
- 1,883
- References
- 0
- Citations by year
- 2026: 391 · 2025: 533 · 2024: 360 · 2023: 276 · 2022: 197 · 2021: 91 · 2020: 28 · 2019: 2 · 2017: 1 · 2015: 1 · 2014: 1 · 2013: 2
- FWCI
- 34.77
- Type
- book
- Language
- en
- Open access
- closed
- Locations
- Routledge eBooks (publishedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Applications
- Physical Sciences › Mathematics › Statistics and Probability › Statistics Education and Methodologies
- Social Sciences › Education › Educational Research and Methods
- Keywords
- IBM SPSS Statistics, statistical software, statistical analysis, missing data handling, regression equations, multivariate analysis, factor analysis, clustering, logistic regression, log-linear models, multivariate analysis of variance, discriminant analysis, multidimensional scaling, internal consistency, residuals
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