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:

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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).

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The Worst Mistake Made on a Dissertation Is...

I have a saying that I like to tell consulting clients, which is easier said than done, but I think are words for doctoral candidates to live by: "The only bad dissertation draft is one that isn't turned-in." The most common factor that unnecessarily slows progress on a dissertation proposal or defense is a propensity to strive for the perfect draft. As a graduate student, we all fantasized of turning-in our first draft and having our advisor, being so amazed at its brilliance, insist that you accept your PhD on the spot.

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Moderating Effects with Seemingly Uncorrelated Variables

I received a great question this week, as a submission to my Ask the Stats Make Me Cry Guy page, which asked: In order for a moderating relationship to exist, do the predictor IV and dependent variable need to be significantly correlated?". This is a question that I am asked a lot, partly because of the common confusion between mediators and moderators and the commonly held belief that an IV and DV should be related for mediation to be present (see my video blog on Mediators, Moderators, and Supressors for more info on this topic). However, moderators are a completely different story. In fact, a simple correlation between two variables can be very misleading, if one relies on it as an indicator of potential moderating effects and/or as an indicator that moderating effects should be tested.

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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.

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How to make SPSS produce all tables in APA format automatically!

Updated on Wednesday, June 8, 2011 at 3:43PM by Jeremy Taylor

Formatting a graph that was exported from SPSS to Microsoft Word can be an absolute pain. Since neither program is known for it's simplicity or "user-friendliness", the interaction between the two can be predictably tedious and frustrating. The process of converting a standard SPSS table to APA format might be bearable, when you are talking about a single table, but can become overwhelming when you have an entire manuscript worth of tables. Fortunately, a few minor alterations to your SPSS settings can make SPSS do most of the heavily lifting for you, making SPSS automatically produce tables that closely resemble APA format and cutting down your formatting time by as much as 90%!

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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.

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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.

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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.

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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.

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Within-Subject and Between-Subject Effects: Wanting Ice Cream Today, Tomorrow, and The Next Day…

The conceptual difference between within-subject and between-subject effects is something I am asked about quite often. So often in fact, I thought a blog posting was warranted! As a quick disclaimer, I know this is a complex issue and the description of what each type of effect actual is varies greatly based on the kind of analysis one is conducting. However, what follows is an attempt to provide a basic conceptual foundation to understand the differences.

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Bonferroni Correction In Regression: Fun To Say, Important To Do...

The Bonferroni correction is only one way to guard against the bias of repeated testing effects, but it is probably the most common method and it is definitely the most fun to say. I've come to consider it as critical to the accuracy of my analyses as selecting the correct type of analysis or entering the data accurately. Unfortunately adjustments for repeated testing of hypotheses, as a whole, remains something that is often overlooked by researchers and the consequences may very well be inaccurate results and misleading inferences. In this independence day blog, I'll discuss why the Bonferroni Correction should be as important as apple pie on the 4th of July.

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Put aside your fears and be wrong already!

First of all, if your research progress is slowed by fear of statistics, your are certainly not alone. Being afraid to "mess-up" your stats, and thus your project, is a common lament. But I'm here to tell you that your project is not that fragile! Once your data is collected, entered, cleaned, and ready for analysis, it is time for excitement, not concern! The golden rule here is: BACK UP. I'll say it again: BACK UP. In case I haven't been clear so far BACK UP! By this I mean back up your data. Make double, triple, and quadruple copies of your dataset and KEEP THEM IN DIFFERENT PLACES (e.g. on a server, on an external hard drive, on a flash drive...etc). It doesn't do much good to keep back up copies in the same place as your original, because if something goes wrong where your data is located, the back up copies are likely toast too!

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Trade Your Stats "Truths" for Stats Arguments...

The two most common questions that I receive about statistical analyses, no matter what kind or purpose, is: "Am I doing it right?" or "Am I allowed to...(fill-in a variation of a common analysis here)?" My response to these questions is usually: "Sure, you can do whatever you want, but what will it mean if you do?" I've said for a few years now that I don't see statistics as being about find truths, but instead I see it as being about building arguments. The critical things is that you understand the impact of your statistical decisions.

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Data Transformations: statistical voodoo or truth serum for your data?

Anyone that has taken a statistics class has probably learned about transforming data, at one time or another (although you may be in denial about it). In short, you may want to transform your data if you need to perform a parametric analysis, but the inherent assumptions are violated in your dataset.

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