Kendall Correlation-Examines the relationship between two or more ordinal or scale-level variables.

Partial Correlation-Conducts a partial correlation between two scale-level variables while controlling for scale-level covariates.

One Sample t Test–Used to assess if the values of a single variable ar significantly different from a tested value.

Paired Samples t Test-Examines the mean difference between two paired scale level variables (e.g., differences between math scores pretest vs. posttest).

One-Way ANOVA-Used to examine differences in a scale-level variable between two or more categories.

Repeated Measures ANOVA-Used to examine differences in a scale-level variable measured under two or more conditions.

One-Between One-Within ANOVA-Used to examine differences in a scale-level variable measured under two or more conditions and between two or more categories.

(Multiple) Linear Regression–Examines if one or more scale, ordinal, or nominal level independent variables predict a scale level dependent variable.

Binary Logistic Regression–Examines if one or more scale, ordinal, or nominal level independent variables predict a nominal level dependent variable with two levels.

Chi-Square Goodness of Fit-Examines if a nominal variable is equally distributed across all groups (e.g., testing the distribution of favorite colors).

McNemar’s Test-Examines the relationship between two dichotomous variables that can be matched together (such as by time). Both variables must have the same groups for the analysis to run properly.

Wilcoxon Signed Rank-Examines the difference in two paired ordinal or scale level dependent variables (e.g., letter grade pretest vs. posttest).

Friedman Test-Examines differences among two or more scale or ordinal level variables (e.g., differences among pretest vs. posttest vs. follow-up letter grade).

Man-Whitney U-Examines the difference in an ordinal level dependent variable by a dichotomous nominal level variable (e.g., differences on letter grade A-F by gender).

Kruskal Wallis-Examines the difference in a n ordinal or scale level dependent variable by a nominal level variable (e.g., differences in letter grade A-F by ethnicity).

Overview of Intellectus Statistics: Level of Measurement

Nominal: In this level of measurement, numbers are used to categorize the data.  For example, if gender is your variable, the responses will be male or female.  A dichotomous nominal variable has only two categories.

Ordinal: Ordinal level variables have a meaningful order to them such as rank.  For example there is an order to “drink size” (small, medium, large), however there is not a consistent distance between sizes.

Scale: Numeric variables that have equal intervals between each value, for example age.

Important

When uploading a .sav file to the application all variables with ordinal level of measurement selected will default to scale.  If you would like to change the level of measurement back to ordinal, you can do so using the drop down menu next to your variable name.

When uploading a .csv file to the application all variables with text data will be default to nominal and all variables with numeric data will default to scale.  Ordinal level variables in .csv files should be in numeric format.

Level of Measurement and Your Analysis

When your data is uploaded to the Intellectus Statistics application you should confirm your levels of measurement are as follows for each analysis:

Pearson Correlation: All variables are scale level of measurement

One way ANOVA: Independent variable is nominal level of measurement; dependent variable is scale

Independent samples t-test: Independent variable is a dichotomous nominal; dependent variable is scale

Dependent samples t-test:  both variables are scale

Chi-square: x variable (factor) is nominal; Y variable (factor) is nominal

Linear regression: Dependent variable is scale; predictor(s) scale or nominal

Mediation: All variables are scale level of measurement

Moderation: All variables are scale level of measurement

Reliability: All variables should be the same level of measurement with themselves (e.g., only ordinal variables used together)

Logistic regression: Independent variables are nominal or scale; dependent variable is dichotomous

Wilcoxon signed rank: Both variables are ordinal (or scale)

Mann Whitney U: Independent variable is dichotomous; dependent variable is ordinal (or scale)

Kruskal Wallis: Independent variable is nominal; dependent variable is ordinal (or scale)

Friedman Test: All variables are ordinal (or scale)

Repeated measures ANOVA: All variables are scale

One-within One between ANOVA: Independent variable is nominal; dependent variables are scale

ANCOVA: Independent variable is nominal; covariates are scale; dependent variable is scale

SPSS data file preparation

Excel data file preparation

During school, a lot of us don’t take a break.  Most people in the US have a type of Protestant work ethic of working hard and having our children live better than we did.  Self-reliant, preserving is the motto.  Vacation, relaxation, and time for contemplation is not part of this ethic.  Yet, we all have the experience of finally getting away, feeling our body’s relax after a few days, our face softens, and how we are in fact better able to deal with the stressors of the days and weeks.

I’m reminded of two stories about relaxation and work.  The first is about my stepbrother, Bill.  He realized that just walking to his car got him out of breath.  As it turned out, he had at least one heart attack, required several heart stints, and is in cardiac rehab.  He always pushed and ran himself ragged, rarely taking a break.

The second story is about Lee from my doctorate days.  He came over one Sunday and we talked about doing school work.  He told me he never worked on Sundays. You would have thought he told me the sky was purple–I just could not believe that anyone took off from working daily, even on Sundays.  I was accustomed to working every day, including Sundays, if even just for a few hours.

The point is that stress is only acknowledged after its let up, and that the way we think about relaxation is not necessarily the only way to think about downtime.  So learn the lesson without the rehab: take this summer, plan a break for at least 4 days and at least quarterly.  Get exercise, eat healthy food, read something not work related, reconnect with those you love, breathe deeply, get a massage….you get the idea.

I wish you all a great, relaxing summer. We’re ready to take your call when you get back.  In the meantime, enjoy!

Please click one of the videos below for a tutorial on ANOVA.

• ANOVA Tutorial
• Conduct and Interpret an ANOVA in Intellectus Statistics
• Repeated Measures ANOVA
• Mixed Model ANOVA

Data Analysis Plan

• Edit your research questions and null/alternative hypotheses
• Write your data analysis plan; specify specific statistics to address the research questions, the assumptions of the statistics, and justify why they are the appropriate statistics; provide references
• Justify your sample size/power analysis, provide references
• Explain your data analysis plan to you so you are comfortable and confident
• Two hours of additional support with your statistician

Quantitative Results Section (Descriptive Statistics, Regression, ANOVA, Correlation, Chi-square, Structural Equation Modeling, HLM, Cluster Analysis, Time Series)

• Clean and code dataset
• Conduct descriptive statistics (i.e., mean, standard deviation, frequency and percent, as appropriate)
• Conduct analyses to examine each of your research questions
• Write-up results
• Provide APA 6^th edition tables and figures
• Explain chapter 4 findings
• Ongoing support for entire results chapter statistics

Qualitative Results Section (Grounded Theory, Phenomenological)

• Thematize your transcripts
• Gain consensus on the number and name of themes
• Prepare document for two additional readers (for reliability)
• Conduct inter-rater reliability for each theme
• Write results chapter to present themes and supporting excerpts of those themes
• Explain chapter 4 findings
• Ongoing support for entire results chapter

We also offer additional assistance if needed, APA editing, committee revisions, and defense preparation. Contact Statistics Solutions today to learn how we can assist with your dissertation development.

Free Resources

You can assess reliability using the test-retest method, alternative form method, internal consistency method, split-halves method, and inter-rater reliability.

Test-retest is a method that administers the same instrument to the same sample at two different points in time, perhaps one year intervals. If you find that the scores at both time periods correlate highly (> .60), you can consider them reliable. The alternative form method requires two different instruments consisting of similar content.  You must have the same sample take both instruments, and then you correlate the scores from both instruments. If you find high correlations, you can consider the instrument reliable.  Internal consistency uses one instrument administered only once.

You use the coefficient alpha (or Cronbach’s alpha) to assess the internal consistency of the items. If the alpha value is .70 or higher, you can consider the instrument reliable.  The split-halves method also requires one test administered once.  The number of items in the scale are divided into halves and a correlation is taken to estimate the reliability of each half of the test.   To estimate the reliability of the entire survey, the Spearman-Brown correction must be applied.  Inter-rater reliability involves comparing the observations of two or more individuals and assessing the agreement of the observations.  Kappa values can be calculated in this instance.

There are no shortcuts in the dissertation process; therefore, plan to harvest well by outlining all the necessary steps to accomplish the tasks at hand. (Our free membership includes a dissertation timeline to help with planning.) Additionally, line up the support you need, build in wiggle room for unexpected events, and stay focused on completing your dissertation. After all, there are a whole lot of wonderful opportunities waiting for you ahead!

At the risk of sounding too philosophical this month, here’s one of my favorite passages.  Nietzsche, in 1882, wrote about “The Greatest Stress.”  Essentially he said to imagine that what has happened, and is happening, will happen again and again and again (eternal recurrence).

“This life as you now live it and have lived it, you will have to live once more and innumerable times more; and there will be nothing new in it, but every pain and every joy and every thought and sigh and everything unutterably small or great in your life will have to return to you, all in the same succession and sequence –Would you not throw yourself down and gnash your teeth and curse the demon who spoke thus?  Or have you once experienced a tremendous moment when you would have answered him: “You are a god and never have I heard anything more divine!”

If this thought gained possession of you, it would change you as you are or perhaps crush you; the question in each and every thing, “Do you desire this once more, and innumerable times more?” would lie upon your actions as the greatest weight! Or how well disposed would you have to become to yourself and to life to crave nothing more fervently than this ultimate eternal confirmation and seal?” — Nietzsche

He infers that we have a choice in every moment.  Those choices involve every aspect of life: family life, work life, your personal and moral life, and also in your school life.  It’s sometimes easy to feel like things are happening to us, “I have to jump through IRB hoops, endless dissertation revisions, lack of committee direction or competence….”

I’m reminded too about stories from those in Auschwitz.  While many got depressed (and had every right to be), there were others comforting others and giving up their last pieces of bread; in the middle of horror they were choosing their state of being.

So I’m inviting you to regain your sense of personal power and know that you are choosing, if nothing else, your attitude.    My job is to help you graduate—your job is to approach your education process the way you want to.

I wish you success, peace, and a speedy graduation!

Best,

James

When selecting a survey instrument, it is important to know how total scores or averages are calculated and what higher or lower scores indicate.  Oftentimes, in survey instruments, the tool can be comprised of negatively worded items as well as positively worded items.  When scoring these instruments, it is important to know which items need to be reverse scored prior to calculation.  It is important to understand how the instrument has been scored in previous studies and to duplicate that scoring method for your study.

One other factor to consider when selecting an instrument is the type of data you will be obtain.  If you are planning to use a simply descriptive study, the design of the response options can vary from question to question.  If you plan to use inferential statistics, it is beneficial to be able to create total scores.  In order to create total scores or average scores, you typically want all response options that make up a particular scale or sub-scale to have the same range, perhaps 1 – 5 where 1 = strongly disagree and 5 = strongly agree.

Remember, the two most important factors in selecting an instrument are that the instrument measures your variable of interest and that it is reliable and valid.  That information coupled with the other suggestions will assist you in the selection of an excellent instrument.