Readings

1. AR: Argyrous, G. (2011). Statistics for research with a guide to SPSS. (3rd ed.). Los Angeles: Sage. [2nd edition is fine, too.] ISBN: 978-1-4129-1948-7.
2. BW: Boslaugh, S., & Watters, P. A. (2010). Statistics in a nutshell. Sebastopol: O’Reilly. ISBN: 978-0-596-51049-7 $34.99
3. Other readings, such as research journal examples and book chapters, will be assigned.
4. Labs: Complete SPSS exercises in Argyrous and others that will be distributed.

Most of the readings are concentrated at the beginning of the term so that you’ll read them and we’ll discuss them in a concentrated way. Then we practice and refine the overview in sample situations and the students’ own research interests.

The schedule may be altered as needed.

SPSS: Using SPSS is required. There are copies in the GSlis Tech Lab and in other universities’ labs. Optional: if you plan on working a lot with quantitative data then consider purchasing the SPSS Grad Pack. You can download a 30-day trial of SPSS, too.

Optional Readings: Besides the required texts, there are two other texts online.

1. The first is Hafner, A. W. (1998). Descriptive statistical techniques for librarians. (2nd ed.) Chicago: ALA.
2. The second is a paragon of statistics, Kirk, R. E. (1995). Experimental design: procedures for the behavioral sciences. (3rd ed.). Pacific Grove, CA: Brooks/Cole.
3. Other readings will be from Hair et al., Multivariate analysis. The Hafner answer key is at http://web.simmons.edu/~benoit/lis403/spring09/hafner/Answers.pdf and has a useful compendium of definitions that you ought to know by heart.

Additional SPSS software handouts 11 megs zipped file of .pdfs

Main points: The main point of the class is to introduce a broad range of concepts and specific manifestations in statistics; in addition students will practice SPSS with an eye towards learning how to read the output. The goal of the class is to enable students to conduct elementary research projects with statistics and to understand how to approach data so that they know what statistical tests are appropriate.

Class Session # - Topic & Readings.

• Read Argyrous as the baseline and B&W as a complement, providing more specifics at time and a review.
• Kirk, chapter 1: http://web.simmons.edu/~benoit/lis403/DOC110608.pdf

Variables and measurement [AR - Part 1, chapter 1; BW chapters 1-2]
Setting up SPSS [AR - Part 1, chapter 2; BW chapter 3]
Graphic description of data [AR - Part 2, chapter 3; BW chapters 4; Hafner, chapter 4]
Critiquing others’ statistical work [BW chapters 5-6]
Tabular description of data (frequencies, percentiles) [AR - Part 2, chapter 4]
Cross-tabs [AR - Part 2, chapter 5]

• the idea of quantification, clustering, and assumptions of normal distribution
• the role of statistics in research and reporting
• the first look at tabular data (descriptive statistics) - demonstration of data collection as part of research
• role of professors as part of their service activities evaluating peer-reviewed submissions to journals
• idea of data points, clustering, error when clustering, storing data in files for analysis

• chapter 1,
• chapter 2,
• chapter 3,
• chapter 4

• review of the previous class session; q&a
• examples of nominal, ordinal, interval, and ratio data (NOIR)
• data elaboration
• discussion using students’ research ideas to show how to express research question as statistical model

• AR - Part 3, chapter 9;
• Kirk, chapter 2
• Hafner, chapters 5 and chapter 6.

• review of the previous class session; q&a
• Differences of data - what happens when we move from uni- to bi- and multi-variate data
• Fundamental principles of statistics: central tendency (mean, mode, median) and variance (standard deviation); individual and sample stats
• Normal distribution (as statistical model and as expected distribution); idea of parametric and non-parametric tests
• idea of parameters, statistics; t-tests for independent pairs; for repeated pairs
• data robustness

• AR - Part 3, chapter 12;
• Hafner, chapter 7)
• Multiple regression - AR - Part 3, chapter 13;
• BW chapters 14, 15]

• Testing of multiple means; multiple dependent variables
• Pearson’s product moment; Spearman’s rho - when to use which
• discussion using students’ research questions as multivariate data source

• AR - Part 4, chapter 14;
• BW, chapter 7]
• Hypothesis testing and one sample z-test for a mean [AR - Part 4, chapter 15]
• One sample t-test for a mean [AR - Part 4, chapter 16; BW, chapter 8]

• problem area (problematic) and research questions as hypothesis
• probability
• practice evaluating t tests, critical values; p-scores, etc.

• AR - Part 4, chapter 16
• Nonparametric statistics, between and within-subjects design [BW, chapter 11]
• Two samples t-test [AR - Part 4, chapter 17; BW, chapter 8]
• F-test for the equality of more than two means: analysis of variance (ANOVA) [AR - Part 4, chapter 19; BW, chapters 12-13]
• Two dependent samples t-test [AR - Part 4, chapter 17]
• New: pdf outline and stat choice notes

• practice with t and F tests
• Discussion notes, practice, etc.
• Difference between p and α
• Practice questions
• Answers
• Criteria for paired t-test
• Notes about anova
• Intro to Anova (supplemental)
• Discussion about t and z
• Notes about t and z tests

• Inferential statistics: tests for frequency distributions, one sample tests for a binomial distribution [AR - Part 5, chapter 23]
• One sample tests for a multinomial distribution (chi-square goodness-of-fit) [AR - Part 5, chapter 23; BW, chapter 10]
• Frequency tests for two dependent samples (McNemar) [AR - Part 5, chapter 24]

• discussion of descriptive versus inferential statistics
• uses and abuses of chi-square and other stats

• Inferential stats: other tests of significance, rank-order tests for 2+ samples (Wilcoxon) [AR - Part 6, chapter 25]
• t-test for a correlation coefficient [AR - Part 6, chapter 26; BW, chapter 9]

• discussion of other tests
• practice shaping data for inferential stats

• Other statistical techniques: factor, cluster, discriminant function, multidimensional scaling [BW, chapter 16]

• discussion of multivariate techniques
• sample case studies from Hair of multivariate techniques

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• Final homework 1
• Final homework 2

1. Business and Quality Improvement Stats (time series, decision analysis, quality improvement)
2. Survey design and data [Hafner chapter 8)

• discussion applying business-oriented stats to lis
• Notes (.pdf)

Thanksgiving Week [No Class]

• Medical and Epidemiological Statistics; especially large data sets [BW, chapter 18]
• Readings about structural equation modeling and other multivariate techniques [tba]

• discussion of using large data sets
• data and text mining
• information retrieval
• case studies from Hair

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• Answers to Final Homework 1
• Answers to Final Homework 2

Session 12 - 11/17 -

• Educational and Psychological Statistics [BW, chapter 19]
• Wrap up the class.

• discussion applying stats to user groups

End of the draft syllabus (Aug 16, 2011)
File name: lis642/schedule.rtf