Intro Topics: A Regression Primer

Welcome to another stupidly-long, but hopefully informative instructional on introductory statistical concepts. Today we tackle regression analysis.  Use the menu links below to jump around if you need/want to get a quick bit of info on any topic:


1) Background: Correlation analysis [conceptually] explained
2) Correlation analysis and OLS linear regression
3) From guesses to predictions: The logic of using linear regression
— a) Building the equation
— b) Interpreting regression
— c) What is OLS?
4) Advanced Applications
5) Conclusion & Further reading

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Mplus Coefficient Cruncher (v 1.4)

Back again with a new Excel tool. This one, which I’ve titled the “Coefficient Cruncher” is a recent development that I’ve been using to write up various sets of results, and it has greatly accelerated my output rate.  One of the most tedious things about writing up results is… well, writing up results. This helps.

The Coefficient Cruncher takes a set of model results and regurgitates the information in two formats. The first format is your basic in-text statistical report, in the style:
(B=[###], SE = [###], p < [.###]).
The second format is a row-mapped table of your results in standard APA style, which you can then edit as necesary. It has made putting together tables of coefficients and talking about findings from Mplus much much much much much much easier and faster.
Notes: The Coefficient Cruncher will report any and all model results in the “B, SE, t, p” fashion (in accordance with Mplus output styling), so be sure to change anything in your output that isn’t actually a B estimate (e.g., the correlations generated in standardized  WITH statement outputs).

Model Fit Aggregator v2.1

(Click here for information on the older version 1.2)

The Model Fit Aggregator is a tool I designed for use with Mplus model output. It compiles the results of goodness of fit tests and returns them to the user in easy-to-use APA-style for reporting in manuscripts, talks, posters, etc. It will also compare changes in goodness-of-fit across two nested models.  Continue reading

Data management with SPSS + MS Excel

Like many others, I have historically used SPSS as my go-to data management program. Many of those with whom I work do the same, and with good reason. It’s flexible and fairly easy to use for basic data management tasks (and let’s be honest, most people are trained in SPSS during their initiation into data analysis in psychology). One life changing moment for many users of SPSS is the day that one realizes the utility of the syntax window vis a vis the point and click interface. This becomes more apparent during the data management phase than perhaps at any other point. This article assumes that you’re already past this point of no return.

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Mplus Model Fit Aggregator (v 1.2)

UPDATE: The newer, better version 2.1 is now out. Click here for more info.

The Model Fit Aggregator is a tool I designed for use with Mplus. It allows you to use the model fit information from any model you estimate via maximum likelihood estimation and plug it in (where instructed). The tool will aggregate the information from the raw output and spit out a single line of model fit statistics for you to paste into a manuscript, poster, talk, or other document. Continue reading

SPSS Correlation Tabulator (v 1.4)

The Correlation Tabulator is a tool I designed for use with SPSS. It will require you to run a set of Pearson correlations in SPSS, paste the correlation table output into the tabulator (if you follow the instructions, of course). It will then take those results and compile them into an APA-style correlation table (coefficients reported to two decimal places, with asterisks indicating significance levels), which you can copy and paste into Word or a similar program.

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SPSS Regression Tabulator (v 2.2)

The Regression Tabulator is an Excel-based tool developed for use with SPSS regression analysis output. It is designed to accommodate multiple regression with a maximum of 20 predictor variables (which you will need to define). If you paste your “Coefficients” table into the worksheet (with or without confidence intervals), it will convert your SPSS output into three APA-style tables for you to choose from. The first features complete information unstandardized coefficients (B, SE, t, p). The second and third are truncated tables (unstandardized and standardized, respectively) that include reports of the model coefficients and standard errors, with asterisks indicating significance levels.

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