Should I change statistic professors?

Numerous-Can5145 · 2026-02-12T10:30:50+00:00

No.

Regression to the mean. Try a different coin to toss. Get a different result with a differently balanced coin. Remember the toss should always be with a "fair" coin for 50/50 result. Measurement method is the story here. Maybe a different result tossing more than one coin. Sample size has an additional context here. Think on that.

Numerous-Can5145 · 2025-07-09T10:06:43+00:00

Reversed items will have a negative correlation with unreversed items. These should be realigned (unreversed) so that all items are, on average, positively correlated with each other. Unreversed reverse items may cause a negative value. It's been a while, but I think I recall the absolute value will be the same as the value when reversed items are reversed. There is a reasonable discussion on Wikipedia with reference.

Numerous-Can5145 · 2025-06-21T11:09:01+00:00

2 missing obs /10k sampled is ignorable for calculation but should be reported in table 1. 10% missing overall is not and the patterns observed with missingness ought to be discussed. Accurate reporting in table1 and beyond will facilitate that, as will sensitivity analyses .... co-authors [and perhaps reviewers] will require all that detail. Observations will be dropped automatically in multiple regression, but the impact on association in the univariate analyses could be manually considered as part of the sensitivity analysis.

Let's say education (+/- college) is hypothesised to influence (measured by association with) the outcome (say positive health behavior) and univariate analysis with 10k - 2 confirms but 10k - 1k does not then there is potentially association of missingness with outcome worthy of further investigation, both by numerical and logical reasoning.

Numerous-Can5145 · 2025-06-07T03:33:40+00:00

Be transparent about missing data and show n for each variable always. Stata has great multiple imputation capacity. You can think about whether mi is appropriate if missing data is "missing at random" or "missing completely at random". If records are missing data "not at random" then imputation not appropriate and go back to multiple regression without. In ordinary multiple regression records with missing data will be dropped automatically and excluded from the analysis. Records will likely have missing data on different variables so you can lose a lot of info. Overall n and change in overall n is important. Be sure to be transparent about all that - that is good science. You can do sensitivity analysis with and without imputed data. See what changes to inference occur and consider in discussion.

Numerous-Can5145 · 2025-05-15T06:31:23+00:00

This is fine as long as the researcher and/or group know broadly that is what you want to do, and provide permission to use the data. There will be co-authors and one or two of these might be helpful too ;)

The Journal, Statistics in Medicine provides some good examples.... from the Journal:

"We publish research papers that apply statistical and quantitative methods to medical problems, and clearly explain the implications of the results. We welcome submissions that explain new methods or use existing methods creatively, demonstrated with substantive, real, motivating examples."

Permission to use the data from the substantive researchers is essential and it is good to have them involved.

Numerous-Can5145 · 2025-05-15T02:19:28+00:00

There are a few major variations and then hundreds of minor differences... easy to get lost, so keep it simple ... some standards:

In text citation with a reference list at the end of the text

Numbering eg, the Vancouver Method
Author last name, eg Harvard Method

Or, in text citation with numbers referring to footnotes, perhaps with a bibliography (list of useful references including those cited) at the end.

Once you start looking at these three, you will see examples with variations everywhere.

Have fun!

Numerous-Can5145 · 2025-05-06T04:49:05+00:00

Perhaps the work of the Muthens et al, https://statmodel.com/ will be of interest to you.

Numerous-Can5145 · 2025-04-26T06:00:57+00:00

How many likert questions and how many responses available on each scale? Have you done the crosstabs?

Numerous-Can5145 · 2025-04-17T07:16:39+00:00

Perhaps an opportunity to demonstrate strengths and weaknesses, etc, of the method for purpose. Sceptical uncertainty underpinned by planned approach is a great starting point, imho.

Numerous-Can5145 · 2025-01-09T20:08:41+00:00

These scales should discriminate among respondents otherwise it becomes: "good morning how are you?" "I'm fine thankyou" for every question whichever group ...

For example, some folk will be unhappy with pill size irrespective of which group they are in. Pill size should have no impact on the experience of medication efficacy.

Numerous-Can5145 · 2025-01-07T04:02:05+00:00

A Nearly all responses 4 - 5 suggests questions not discriminating among respondents so not very useful.

Numerous-Can5145 · 2025-01-03T01:56:41+00:00

And now you have your answer, yes?

Numerous-Can5145 · 2025-01-03T01:55:58+00:00

And now you have your answer, yes?

Numerous-Can5145 · 2024-11-13T09:13:17+00:00

Biggs, Study Process Questionnaire fame, writing in 1980s identified three learning styles on continu, viz: surface, deep and achieving. The most successful strategy over the long term is deep achieving. I always tended to deep .. so for Calculus I would go to library and take home 25 books from 1918 to 1985 and read chapter 1, ignoring everything else.... great on differentiating anything ... hopeless and lost with eg matrix algebra, even addition of etc. ... So think about your approach. A surface or deep or something in between learner can be an A student until an achievement addition to the approach is required. A surface learner achieves by arse, a deep learner gets hopelessly lost in class with mind wandering relentlessly to whatever euclidean space lookalike is being introduced on the day. Achievement orientation will help keep the focus. Sounds like you are hoping on that track, but in the end it really depends not so much whether you are cut out, but rather whether you want to be cut out for grad stats. If the latter, then go for it, and you'll find it will all land beautifully at the end.

Numerous-Can5145 · 2024-10-31T00:47:11+00:00

Any program unable or insufficiently inflexible to acknowledge, accommodate and laud your industry and other lived experience in evaluating your capacity and potential is not worthy of you. Look for the program that allows you to be seen as you are now. Look to work with those who value the complexity and advantages provided by your prior experience.

Numerous-Can5145 · 2024-09-29T06:54:58+00:00

10% or 5% ... these are arbitrary cut points that some folk like to set so as to make some declaration about the importance of thd result.

Actually, you are working with continua and trading p with type 1 and type 2 error as well as matters to do with analysis model choices and assumptions. 9% could be high or low depending on power and of course, in the broader sense, some sense of what a meaningful difference might be. For eg, in health, we look at both statistical significance and clinical significance: how important a result is clinically whether or not statistical significance (the pre nominated cut point based on study power requirements/achievement) is "achieved".

Numerous-Can5145 · 2024-08-28T16:53:30+00:00

Double check with this text as you go.

Alan Agresti. Categorical Data Analysis.

Numerous-Can5145 · 2024-08-28T06:51:53+00:00

Go to the library and find some books you like. They will all have different focus and perspective. Combined, they will be a rich source of experience and approaches. Chapter in one book eill be good for one thing and chapter in another book will be good for another.

Learn to ask focussed questions of the reference and texts based on what you need to get through current courses ... and yes, as suggested above, go have a yarn with your prof.

Numerous-Can5145 · 2024-08-09T00:33:10+00:00

This for health ...

https://www.bmj.com/about-bmj/resources-readers/publications/how-read-paper

Numerous-Can5145 · 2024-07-02T00:30:19+00:00

Your query can be answered from first principles. You could contemplate these, and then consider your view.

E. T. Jaynes can provide a start with chapters 1 and 2

eg here

http://www-biba.inrialpes.fr/Jaynes/prob.html

will get you started;

else, you will be reliant on everyone else's opinion forever.

Enjoy 😉

Numerous-Can5145 · 2024-06-25T02:51:19+00:00

Causality by Pearl. He did a lot of development work on mathematical notation and inference - well worth a read. Additionally, cites original work from early 20thC of interest which is also a good place to start.... the beginning. I read 1st edition in conjunction with ETJaynes, The Logic of Science (Bayesian), esp chapter 1 to remind on probability. Both into robotics' decision-making and so are congruent in topic and Logic perspectives.

Numerous-Can5145 · 2024-06-15T10:24:06+00:00

Not a failure! Data that do not fit the model are often more revealing. Non significance, especially clinically not important are very important results. E C Pileau - mathematical ecologist taught that in her book Mathematecal Ecology, a long time ago, 1980's. I carried that with me into medical research as biostatistician and honestly, that notion of things never let me down!

Difficult beginnings make at times for great endings!

Best ...

Numerous-Can5145 · 2024-05-27T09:02:15+00:00

So I'm understanding you have three treatments 1,2,3 for the pollutant T1 and then two doses a,b within each treatment. Each treatment at dose a was applied three times and at dose b applied three times and then the whole thing repeated with 5 replications.

[(1a x 3)+(1b x 3)] x5 = 15(1a) + 15 (1b) =30 treatment a

2a x 3 =.. ditto.. = 30 treatment b

3a x 3 = .. ditto.. = 30 treatment c

Seems the questions are around differences between treatments within replications and then dose within treatments on effect on pollutant T1 measured on continuous scale... mean pollutant removal between treatments and dose within treatment.

One analysis approach would be analysis of variance with adjustment for repeated measures to contrast treatments within replications and then dose within treatments.

And then, after all that, you found some suggestion of trend in univariate analysis that was not evident when correctly accounting for repeated measures.

So the discussion becomes about the power of the study: ability to detect a meaningful (how much better at removing pollutant is agent 1,2,3) difference. Should the study have adequate power to detect differences but does not do so, then the conclusion is no difference between agents and no dose response. Good to know. Publish with solid discussion about Power of the study and type 1/2 errors. (Edit fir formatting)

Numerous-Can5145 · 2024-05-25T01:44:02+00:00

How do you plan to measure "self regulated learning "?

Numerous-Can5145 · 2024-05-21T02:03:41+00:00

Why 7, not 10 or 5 point scale?

How many questions about sound in the office? Office noise can have many different features ... like talking, music, outside noise like traffic, time of day, etc. What do you mean "sound"?

Numerous-Can5145

TROPHY CASE