Session | Topics | Assignment | |
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Pre-Class #1 Prior to January 14th |
Working with basic stats tools |
Please go to http://www.youtube.com. Type the words “SPSS tutorial” into the search box. Run several of the resultant videos explaining to you how to use the basic tools on SPSS. Please pay particular attention to the regression tutorials. Next, start working with SPSS by loading a dataset into SPSS for further processing. To find a dataset, go to the “Datasets/ SPSS-ready datasets from Watertree Press” under Course Resources in my Onedrive directory you have been invited to. Choose one dataset from the choices here after reading the dataset descriptions in “AAA SPSS-ready datasets from Watertree Press.” Using this dataset in SPSS, run a regression of at least two IVs against 1 DV (choose your own IVs and DV). Your deliverable is the resulting output file with the file extension *.spv. Please create the filename with your name. Example: Straub runs a regression and names the SPSS output file Straub.spv. This is one deliverable for this assignment. **You will also want to view how to run simple descriptive statistics like frequency distributions, means, standard deviations, etc. Also how to run Spearman and Pearson correlations, t-tests, etc. Reading: TAM Exercise The set of lecture notes (“TAM Exercise”) introduces you quickly to a scientific model explaining IT use. It contains a regression run empirically testing the model at the very end of the graphics (pages 7-8). Please access the dataset “Dataset 1 TAM.sav” to run the regression of this model for yourself in SPSS. Questions for Pre-Class #1: Questions: Please answer the following questions about the SPSS output on pages 7-8 of “TAM Exercise.” What is the dependent variable? What are the predictors? What does the column labelled “Sig.” refer to? How many hypotheses can be tested with this one regression run? What are these paths? Are there any paths in TAM that are not being tested in this run? How would you interpret these results, i.e., what statistical inferences can you draw about statistical significance and strength of linkages between 2 IVs and the 1 DV? [Hint: There are two inferences. One is about statistical significance. The other is about explained variances (i.e., effect sizes.] |
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Class #1 Friday, January 17th 8:30-11:30am 12:15pm-1:15pm |
The Scientific Method and Quantitative, Positivist Research vis-à-vis Qualitative Work [Many-to-many relationships between methods, data collection techniques, and data analysis tools] The Research Process Theoretical Adaptation in QPR Review of Basic Statistics with Demos |
Readings:
Web site exploration:
Lectures:
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Class #2 January 28th at 7pm |
Understanding the Basic QPR Scientific Validities |
Readings: · Bhattacherjee, Ch. 5 (“Research Design”) · Handout: “Revision Plan to SE” Lectures: · “Theory, Internal Validity, and Endogeneity Threats” · “Validity Priorities” · “Type I and Type II Errors Simplified” · “External and Internal Validity… p-values in the Era of Big Data” |
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Class #3 February 11th at 7pm |
Comparative QPR Approaches or Methodological Tradeoffs Studies using archival data vis-à-vis field studies (using self-reports or other data sources) vis-à-vis lab experiments Working with Archival Datasets and Econometric Modeling Endogeneity and Reverse Causality (aspects of internal validity) |
Readings/Handouts:
Lecture:
Readings:
Scan:
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Class #4 February 25th at 7pm |
Measurement Methods and QPR Scientific Validation Multi-Item Measures of Latent Constructs Multi-Dimensional Constructs |
Readings: · Trochim, Ch. 5 “Intro to Measurement” · Trochim, Chs. 5 & 6 (“Scales, Tests, and Indexes”) · Polites et al. (2012) [Caveat: Their example of a second order reflective-reflective multi-dimensional scale is not correct. Otherwise, a useful paper.] · Bhattacherjee, Ch. 6 (“Measurement of Constructs,” but only pages 43-48) · Straub et al. (2004) “Validation Guidelines” Scan: · Example of instrument validation paper: Samppa et al. (2019) Lectures: · “Scales” · “Instrument Validation “ Note: Terms like items, indicators, scale items, response items, observations, proxies, surrogates, or measures have very similar meansings. They are all terms for the data attributes and data values we capture when measuring a research construct. Research constructs are abstractions and, therefore, most often NOT observable. The most interesting constructs are, in fact, said to be “latent.” That is, NOT observable. Trust is a latent construct. Yet quite real. [Trust, e.g., leads to sales.] It has even been measured through neural activity in the brain. Age and time, though, especially chronological time, is in many people’s minds observable. But chronological time is seldom an interesting construct, for that reason (IMHO-DW Straub). Marketing research studies people’s ages, certainly, but they are mostly interested in experiential ages, like cognitive age or emotional age. These are much more interesting as theoretical predictors. Handouts: · “Assessing Reliabilities and Validities” Web site exploration: • Online scales available for your use: (http://managementscales.com); search to see examples of usable scales online in the management field; try “computer self-efficacy,” for instance |
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Class #5 March 10th at 7pm |
Demos of factor analysis, construct validity (convergent and discriminant validity), reliability Interpretation of structural model results |
Handouts:
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Class #6 March 24th at 7pm |
Instrument Validation in Practice |
Scan: · Example of data analysis in journal-submitted paper: Guo et al. (2020). There are two documents here. One is the manuscript. The other is the appendices, which has a lot of the scientific apparatus for validating the instrument. |
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Class #7 April 3rd at 7pm
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External Validity and Sampling External Validity, Meta-Analysis, and Cumulative Research Traditions Assumptions of Statistical Testing; Testing for Them and Dealing with Violations Non-parametric Statistics Analytical (or Math) Modeling Contingency Theory & Qualitative Comparative Analysis (QCA) |
Readings: · Textbook Trochim, Ch. 4 (“Sampling”) · Im and Straub (2015) Lectures: · “A Few Basic, Useful Nonparametric Tests” · “Sampling Basics” · “Polling Misses in US Presidential Elections” Scan: · Example of analytical modeling (math modeling) paper: Jung et al. (2019) · Example of paper qualitative comparative analysis (QCA) paper: Lee et al. (2019) |
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Class #8 April 17th at 7pm |
Oral Presentations of Final Group Project If time, more on advanced analytical tools (CB-SEM); Preparing Manuscripts for Publication |
To complete these presentations in the time available, please limit yourselves to a 20 minute, efficient presentation by the team. Focus mostly on the major ideas of the course, that is, the methodological and validation issues. Allow for a few minutes for questions from the class. | |