Please Vote on the "Top Confusing Stats Terms"

Please tell me what stats terms you think are the most confusing! Please order the terms you choose, according to how confusing they are (with #1 being most confusing). The results will dictate what topics are covered in future blogs! Blog entries for Confusing Stats Terms #10, #9, and #8 are already posted, so I'm only asking for terms #7 through #1. Thanks for your input!
http://www.statsmakemecry.com/confusing-stats-terms/

Related Content:

---

Top Ten Confusing Stats Terms Explained in “Plain English” (#8: Residual) Top Ten Confusing Stats Terms Explained in “Plain English” (#9: Multicollinearity) Top Ten Confusing Stats Terms Explained in “Plain English” (#10: Standard Deviation)

How to Conduct a Repeated Measures MANCOVA in SPSS

In today's blog entry, I will walk through the basics of conducting a repeated-measures MANCOVA in SPSS. I will focus on the most basic steps of conducting this analysis (I will not address some complex side issues, such as assumptions, power…etc). If you find yourself with lingering questions after walking through this blog, feel free to leave questions in the "comments" section, or visit the MANCOVA section of my discussion forum to find answers and/or ask questions of your own. Full disclosure: the example data used is from the SPSS sample/help files, and it can be downloaded below.

RIGHT-CLICK HERE AND "SAVE AS FILE" FOR SAMPLE DATA

Let's get started:

Repeated-Measures MANCOVA is used to examine how a dependent variable (DV) varies over time, using multiple measurements of that variable, with each measurement separated by a given period of time. In addition to determining whether the DV itself varies, a MANCOVA can also determine wether other variables are predictive of variability in the DV over time. If that wasn't crystal clear, don't worry, just keep reading.

Repeated-Measures MANCOVA Example:

In our example, your local stats store Stats "R" Us launched a marketing campaign, with three different strategies (variable name: promo; value labels: Strategy A, Strategy B, Strategy C). Stats "R" Us launched campaigns in markets of three different sizes (variable name: mktsize; value labels: Small, Medium, and Large), and measured the sales in each store every three months over the course of one year (4 time points; variable names: sales.1, sales.2, sales.3, and sales.4; see data below).

NOTE: Sales are scaled in "thousands" (e.g. 70.63 is actually $70,630). Also, your data should be in person-level (a.k.a. "wide") format (as opposed to person-period, a.k.a. "long", format), meaning each row of data is a single case (store, in our example). If it were in person-period (long) format, each case (store) would have the number of rows equal to the number of repeated measures (four, in our example), because the repeated measures (sales.1, sales.2, sales.3, and sales.4) would be stacked to form a single variable (Sales).

Click to read more ...

Using Syntax to Assign 'Variable Labels' and 'Value Labels' in SPSS

Preparing a dataset for analysis is an arduous process. Besides recoding and cleaning variables, a diligent data analyst also must assign variable labels and value labels, unless they choose to wait until after your output is exported to Microsoft Word. Unfortunately, that option only leaves additional opportunity for error and confusion, not to mention the inefficiency of editing tables in Microsoft Word. Who among us have not been frustrated while wrestling with Microsoft Word?

When used in conjunction with the customizable SPSS table "Looks" function, formatting your variable labels and value labels can make your SPSS results tables nearly ready for publication, immediately after analysis (CLICK HERE FOR TUTORIAL VIDEO ON TABLE "LOOKS")! Fortunately, SPSS syntax offers a fairly straightforward method for assigning proper labels to both your variable labels and value labels.

Click to read more ...

Top Ten Confusing Stats Terms Explained in “Plain English” (#8: Residual)

When I hear the word "residual", the pulp left over after I drink my orange juice pops into my brain, or perhaps the film left on the car after a heavy rain. However, when my regression model spits out an estimate of my model's residual, I'm fairly confident it isn't referring to OJ or automobile gunk...right? Not so fast, that imagery is more similar to it's statistical meaning than you might initially think.

Click to read more ...

Top Ten Confusing Stats Terms Explained in “Plain English” (#9: Multicollinearity)

Multicollinearity said in "plain English" is redundancy. Unfortunately, it isn't quite that simple, but it's a good place to start. Put simply, multicollinearity is when two or more predictors in a regression are highly related to one another, such that they do not provide unique and/or independent information to the regression.

Click to read more ...

Top Ten Confusing Stats Terms Explained in “Plain English” (#10: Standard Deviation)

Most people find statistics to be complicated, confusing, and just generally frustrating. One of the biggest causes of confusion is the complicated vocabulary that is associated with stats. Frankly, it sometimes seems that stats terms were made to be intentionally complicated. In fact, some concepts seem perfectly understandable when described inplain English, but seem incomprehensible when described in stats lingo.

Click to read more ...

Top Ten Tips for Data Analysis to Make Your Research Life Easier!

While there is no "magic bullet" to make stats and data analysis easy to understand and helpful in our research, there are some things that you can do to avoid pitfalls and help things run smoothly. This "top ten" list offers a few of those things that I think you will find helpful! I'll be posting a video of this list later today on my Stats Videos page.

Click to read more ...