Applied Psychometrics Using SPSS and AMOS
Abstract
The book will be designed primarily for graduate students (or advanced undergraduates) who are learning psychometrics, as well as professionals in the field who need a reference for use in their practice. We would assume that users have some basic knowledge of using SPSS to read data and conduct basic analyses (e.g., descriptive statistics, frequency distributions). In addition, the reader should be familiar with basic statistical concepts such as descriptive statistics (e.g., mean, median, variance, standard deviation), percentiles and the rudiments of hypothesis testing. They should also have a passing familiarity with issues in psychometrics such as reliability, validity and test/survey scoring. We will not assume any more than basic familiarity with these issues, and will devote a portion of each chapter (as well as the entire first chapter) to reviewing many of these basic ideas for those not familiar with them. We envision the book as being useful either as a primary text for a course on applied measurement where SPSS is the main platform for instruction, or as a supplement to a more theoretical text. We also anticipate that readers working in government agencies responsible for testing and measurement issues at the local, state and national levels, and private testing, survey and market research companies, as well as faculty members needing a practical resource for psychometric practice will serve as a market for the book. In short, the readership would include graduate students, faculty members, data analysts and psychometricians responsible for analysis of survey response data, as well as educational and psychological assessments. The goal of the book is to provide readers with the tools necessary for assessing the psychometric qualities of educational and psychological measures as well as surveys and questionnaires. Each chapter will cover an issue pertinent to psychometric and measurement practice, with an emphasis on application. Topics will be briefly discussed from a theoretical/technical perspective in order to provide the reader with the background necessary to correctly use and interpret the statistical analyses that will be presented subsequently. Readers will then be presented with examples illustrating a particular concept (e.g., reliability). These examples will include a discussion of the particular analysis, along with the SPSS code necessary to conduct them. The resulting output will then be discussed in detail, focusing on the interpretation of the results. Finally, examples of how these results might be written up will also be included in the text. It is hoped that this mixture of theory with examples of actual practice will serve the reader both as a pedagogical tool and as a reference work. To our knowledge, no book outlining psychometric practice using commonly available software such as SPSS currently exists. Given that many practitioners in academia, government and private industry use SPSS for statistical analyses of testing data, we believe that our book will fill an important niche in the market. It will contain very practical information regarding how to conduct a wide variety of psychometric analyses, along with tips on interpretation of results and the appropriate format for reporting these results. We believe that it will prove useful to individuals in educational measurement, psychometrics, and survey and market research. Our text will add to the literature by providing users with a single reference containing the major ideas in applied psychometrics with instructions and examples for conducting the analyses in SPSS. In addition, we will provide original macros for estimating a variety of statistics and conducting analyses common in educational and psychological measurement.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Smaller samples potentially produce unstable correlation estimates that are more prone to outliers.
“On the contrary, smaller samples potentially produce unstable correlation estimates, more prone to outliers (Finch et al., 2016).”
From Applied Psychometrics: Sample Size and Sample Power Considerations in Factor Analysis (EFA, CFA) and SEM in General (2018) · cites this paper as Finch et al., 2016
The chi-square test is perhaps the most notoriously sensitive fit measure to sample size.
“The chi-square test is perhaps the most notoriously sensitive fit measure to sample size (Kline, 2016; Finch, et al., 2016).”
From Applied Psychometrics: Sample Size and Sample Power Considerations in Factor Analysis (EFA, CFA) and SEM in General (2018) · cites this paper as Finch, et al., 2016
ML is notorious for non-convergence and small samples are a possible cause.
“Besides ML is notorious for non-convergence and small samples are a possible cause”
From Applied Psychometrics: Sample Size and Sample Power Considerations in Factor Analysis (EFA, CFA) and SEM in General (2018) · cites this paper as Finch et al., 2016
Cited by
Sorted by citations
Kyriazos, T. A. (2018) · Psychology · doi:10.4236/psych.2018.98126 · Cited by 1,882 · see citing works
“On the contrary, smaller samples potentially produce unstable correlation estimates, more prone to outliers (Finch et al., 2016).”
Daassi, M., & Debbabi, S. (2021) · Information & Management · doi:10.1016/j.im.2021.103453 · Cited by 239 · see citing works
Dirty Dozen: A concise measure of Dark Triad traits among at-risk youths
Pechorro, P., Jonason, P. K., Raposo, V., & Maroco, J. (2021) · Current Psychology · doi:10.1007/s12144-019-00288-9 · Cited by 56 · see citing works
- Authors
- Finch, W. H., Immekus, J. C., & French, B. F.
- Affiliations
- Ball State University (US)
- University of Louisville (US)
- Washington State University (US)
- Venue
- Emerald Group Publishing Limited eBooks
- Publisher
- Emerald Publishing Limited
- Published
- 2016-05-31
- OpenAlex
- W7115597217
- Cited by
- 106
- References
- 0
- Citations by year
- 2026: 16 · 2025: 9 · 2024: 13 · 2023: 11 · 2022: 17 · 2021: 11 · 2020: 7 · 2019: 10 · 2018: 11 · 2017: 1
- FWCI
- 3.19
- Type
- book
- Language
- en
- Open access
- closed
- Locations
- Emerald Group Publishing Limited eBooks (publishedVersion)
- Topics
- Social Sciences › Decision Sciences › Management Science and Operations Research › Psychometric Methodologies and Testing
- Social Sciences › Education › Educational Assessment and Pedagogy
- Social Sciences › Sociology and Political Science › Survey Methodology and Nonresponse
- Keywords
- SPSS, Amos, reliability statistics, validity, test scoring, survey data analysis, factor analysis, confirmatory factor analysis, structural equation modeling, measurement theory, test development, test security, test score reporting
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review