How to Write a Literature Review in Psychology

This post presents a set of principles on what makes a good literature review. The principles aim to assist students who are writing a literature review. Researchers preparing an academic publication may also find them a useful refresher. The principles pertain to dedicated literature reviews and introduction sections of empirical reports.

Blogs on R | Statistics

What are the current blogs on R? What do they cover?

Adjusting Correlations for Reliability | Attenuation Formula

This post discusses ways of adjusting correlations for reliability.

Difference Scores | Are They Okay to Use?

A difference score is a variable that has been formed by subtracting one variable from another.
i.e., DIFFSCORE = VAR1 - VAR2.
Some researchers have heard that difference scores are 'bad'. This post discusses some of the issues, provides some additional references, and discusses calculating reliability of difference scores.

Psychology Statistics 101 | R or SPSS

Dan Wright has placed his Quantitative Methods 1 course online. The course offers instructions both in R and SPSS (PASW).

It is an interesting case study in  how to integrate R into a psychology quantitative methods course at the undergraduate level. It's also a cool example of integrating web resources.

Classifying the Status of a Document with Quality Codes

I find it useful when preparing a journal article or thesis to think about the status of each section in terms of a rating from 0 to 10. I call this rating the quality code. To give the coding system substance each code has a label and a description. The codes and their groupings are listed below:

Preparing Student Practice Questions Using Hidden Text in Microsoft Word

Formatting a Table in Word | R to Tab-Delimited to APA Style

The following post sets out my procedure for importing a tab-delimited table of data produced in R  into Microsoft Word and formatting it.

Grammar of Tables | Tables for Results Sections - Journal Articles and Theses

This post discusses ways of thinking about tables. What is the basic conceptual structure of a table? What are the implications of this conceptual structure for communicating with tables? How can this improve the quality of results presented in journal articles and theses?

Item Parcelling in Confirmatory Factor Analysis

This post discusses item parcelling in the context of Confirmatory Factor Analysis. What is parcelling? How do you parcel? Should you parcel?

Tetrachoric Correlations | Overview and Resources

What do you do if you want to run a factor analysis on a set of binary variables?

Discriminant Function Analysis in a Nutshell | Overview, Alternatives, and Resources

This post discusses Discriminant Function Analysis (DFA). It sets out the basic purpose of DFA and provides some links to additional resources.

Spanking Lowers Child IQ | Correlation is not Causation

As I was waiting for a coffee this morning, I had a cursory glance at the paper and found The Herald Sun reporting on a study stating that:

"A SPANK on the bottom, long used by parents to discipline a naughty child, could cause more than tears... It's now thought the age-old disciplinary method may also lower a child's IQ, with those spanked up to three times a week having a lower IQ due to psychological stress."

Recovering a Corrupted Excel 2007 File | XLSX and XLSM Format

An Excel 2007 file that I was working on recently became corrupted. The following are some things that I learnt in my process of recovering the file.

Statistics and Basic Data Analysis: Summary of Earlier Posts

I've recently activated Feedburner. It has not included my posts prior to March 2009. Thus, this post lists these earlier posts for any interested readers.

Statistics:

• Formatting correlation matrices into psychological format using SPSS, Excel and Word
• One-group observational study: Basic set of analyses with links to SPSS resources
• Longitudinal data: Basic data management and analyses
• dyadic data: Basic data analysis and how to merge data in SPSS in dyadic data situations.
• Carry-over effects in repeated measures designs: what to think about and a case study from my own research
• Issues of causal inference in mediation analysis and basic resources for performing mediation and moderation analyses.

Writing:

• How to import references from Google Scholar into Endnote
• Books on writing style

And to put first things last:

• my first post.

Statistics Consulting: My Approach and Orientation

I've been a statistical consultant for postgraduate and fourth year psychology students for many years. The following post distils my method and philosophy of statistics consulting.

"Why" and "How" to Subscribe to a Blog

This post sets out how to subscribe to my blog. It is designed for people new to RSS feeds. If all you want to do is find the RSS feed, here it is.

Jeromy Anglim's Teaching Resources | Statistics

The following are some statistics teaching resources that I have developed over the years.

Jeromy Anglim's Academic Publications

My list of academic publications has moved to here:
http://jeromyanglim.blogspot.com/p/jeromy-anglims-academic-publications.html

Linking text, results, and analyses: Increasing transparency and efficiency

I have recently been thinking about the relationship between text in a final report and data analysis. The broader concern is with making the conduct and reporting of statistical analyses more transparent. I am inspired by the ideas of literate programming, Sweave, and open access to data.

Something to aspire to:

• Raw data is shared  (ethics, copyright, and other considerations permitting). 
• Code is shared that shows how the data was imported, transformed, and analysed. This code is well written, commented, and documented.
• The report is shared as opposed to requiring a paid subscription.
• Report output including tables, figures, and some text is linked directly to the analyses in code.

While the aspirations transcend R, I like the prospect of having analyses in R integrated with a final report. The inclusion of tables and figures , at least conceptually is a straightforward idea. However, the inclusion of text in a results section is a little fuzzier. Surely, text in a results section (I'll call it "results text" for short) varies in how it relates to actual analyses. Thus, I had the following questions: 1) What is the unit of results text? 2) How does results text vary and what should be automatically supplied by R?; 3) For results text that should not be supplied by R, how should it be integrated into an analysis process?

Initial thoughts: After a little reflection I had the following thoughts:

Structural Equation Modelling in R

Structural Equation Modelling (SEM) Software is frequently used in psychology. This post discusses the exciting prospect of greater support for SEM in R.

Introduction to Journal Article Deconstruction

One of the most powerful strategies that I use to learn how to write journal articles is to consciously study the writing conventions of good journal articles. I often want to communicate this strategy to other researchers who are battling the process of writing research. This is particularly the case with results sections. Thus, I plan to post various case studies using this approach to demonstrate how it works. Perhaps the principles generated from the deconstruction will also be relevant to others. I'll post all such instances with the label Article Deconstruction.

Variable Importance and Multiple Regression

Many researchers are interested in questions related to the relative importance of a set of predictors in multiple regression. This is important to both consultants and academics. I assume the motivation derives from the assumption (typically wrong at least to some extent) that the predictors flagged as important will have  larger causal effects and are therefore better targets for manipulation in an  intervention. Some examples include: a) a set of risk factors on clinical symptoms in psychology; b) a set of personality measures on performance; c) a set of beliefs on overall attitude.

R Community in Australia

One of the nice aspects of R is the community of users that has built up around it. The open-source model seems to create an orientation of sharing and contribution. Users benefit from R and then they give back in the form of new packages, free documentation, blogs, presentations, and so on.

Comments on "Introduction to Scientific Programming and Simulation Using R"

I've just been reading Introduction to Scientific Programming and Simulation Using R by Owen Jones, Robert Maillardet, and Andrew Robinson.

Setting up a Blog on Blogger

Given the number of academics in the world, there are surprisingly few blogs on psychology and research methods. There are many possible reasons. Two barriers are: 1) lack of knowledge of the ease of creating a blog; and 2) lack of knowledge of the benefits of having a blog.

The details below set out my setup for my blog account and my blogging statistics. When I set it up originally, I did look into the various options in terms of blogging providers and so on. I make no claim to my choices being optimal for me or other people. But I have found them more than adequate for my purposes. In particular, usage statistics (and comments) are a great form of feedback that is not necessarily available in other forms of academic communication. For further discussion of the benefits of blogging and related technologies in academic, Gideon Burton provides a great exposition.

Confidence Intervals and Correlations

In a previous post, I discussed the various scenarios for running significance tests on correlations.

A researcher recently asked me how to calculate confidence intervals for two correlations that share a common variable (i.e., dependent correlations).

Pen and Paper

For a long while I did almost all my thinking and writing either in my head or on the computer. More and more lately I find myself returning to pen and paper.
Examples:

Experiments with a mixture of repeated measures and between subjects factors

I often speak to researchers who need to analyse an experiment with a combination of between subjects and repeated measures factors.

Some Examples:

• 5 x 3 Design: 5 levels of task type (repeated measures); and 3 levels of group (between subjects)
• 2 x 2 x 2 Design: 2 levels of order (between subjects); by 2 levels of instructions (between subjects); by 2 levels of task feature (repeated measures)

This post directs such researchers to some resources on the web.

Resources:

• UCLA has several examples of how examine such designs using SPSS Repeated Measures ANOVA; It also talks about how to test contrasts and run follow up test more generally.
• Andy Field provides a gentle introduction to repeated measures ANOVA using SPSS.

Cluster analysis and single dominant factors

I often chat with researchers wanting to use cluster analysis to group cases. I just wanted to point out a common scenario where cluster analysis may not be a good way of proceeding.

Logistic Regression Resources in SPSS or R

Question: A researcher asked me: "What resources are available for running and interpreting a logistic regression?"

Significance Tests on Correlations

OVERVIEW: I often speak to researchers wanting to compare the significance of two correlations. The two scenarios most commonly encountered are: 1) comparing dependent correlations; and 2) comparing independent correlations.

Repeated Measures Experiments with Many trials in SPSS (PASW)

I was recently talking to a researcher who was in the process of analysing experimental data based on a 2 x 2 x 2 x 4 repeated measures design. Each combination of the levels of the repeated measures factors involved five trials. The dependent variable was reaction time. The researcher had their data laid out in a one person per participant format (wide format). I had the following advice for the researcher:

Data Format:
Create a long format data file called “trials” where each row is the combination of one participant and one trial. And have a separate data file called “subjects” that contains one row per participant and includes data on participants that is constant throughout the experiment (e.g., gender, age, personality measures, etc.).