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Best calculator for clinical effect size:

Computation of different effect sizes like d, f, r and transformation of different effect sizes: Psychometrica Online calculator to compute different effect sizes like Cohen's d, d from dependent groups, d for pre-post intervention studies with correction of pre-test differences, effect size from ANOVAs, Odds Ratios, transformation of different effect sizes, pooled standard deviation and interpretation

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PICOT Development

PICOT stands for:

Population/Patient Problem: Who is your patient? (Disease or Health status, age, race, s*x)

Intervention: What do you plan to do for the patient? (Specific tests, therapies, medications)

Comparison: What is the alternative to your plan? (e.g., No treatment, different type of treatment, etc.)

Outcome: What outcome do you seek? ( symptoms, no symptoms, full health, etc.)

Time: What is the time frame? (This element is not always included.)

Your PICOT question will fall under one of these types:

Therapy: how to select treatment to offer patients that do more good than harm and that is worth the efforts and costs of using them.
Diagnosis: how to select and interpret diagnostic tests in order to confirm or exclude a diagnosis, based on considering their specificity, sensitivity, likelihood ratios, expense, safety, etc.
Prognosis: how to estimate the patient's likely clinical course over time and anticipate likely complications of the disease.
Etiology/Harm: how to identify causes for disease.
Prevention: how to reduce the chance of disease by identifying and modifying risk factors and how to diagnose early by screening.
The type of question is important and can help lead you to the best study design. To limit your search to a specific study design, use the database's filters/limits or add keywords to your search (e.g., lung cancer AND cohort).

Type of Question Best Type of Study

Therapy RCT -> cohort -> case control -> case series

Diagnosis prospective, blind comparison to a gold standard

Etiology/Harm RCT -> cohort -> case control -> case series

Prognosis Cohort study -> case control -> case series

Prevention RCT -> cohort study -> case control -> case series

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G*Power G*POWER Information

Behavior Research Methods
May 2007, Volume 39, Issue 2, pp 175–191
G*Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences
· Authors · Authors and affiliations
· Franz Faul Email author
· Edgar Erdfelder Email author
· Albert-Georg Lang
· Axel Buchner

References
If you use G*Power for your research, then we would appreciate your including one or both of the following references (depending on what is appropriate) to the program in the papers in which you publish your results:

Faul, F., Erdfelder, E., Lang, A.-G., & Buchner, A. (2007). G*Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behavior Research Methods, 39, 175-191. Download PDF

Faul, F., Erdfelder, E., Buchner, A., & Lang, A.-G. (2009). Statistical power analyses using G*Power 3.1: Tests for correlation and regression analyses. Behavior Research Methods, 41, 1149-1160. Download PDF

Abstract

G*Power (Erdfelder, Faul, & Buchner, 1996) was designed as a general stand-alone power analysis program for statistical tests commonly used in social and behavioral research. G*Power 3 is a major extension of, and improvement over, the previous versions. It runs on widely used computer platforms (Windows 10.0 and MAC) and covers many different statistical tests F, and χ test families. In addition, exact tests. G*Power 3 provides improved effect size calculators and options, supports both distribution-based and design-based input modes, and offers all types of power analyses in which users might be interested. Like its predecessors, G*Power 3 is free.

To download G*Power, go

Universität Düsseldorf: G*Power Die Heinrich-Heine-Universität Düsseldorf ist eine der jüngeren Hochschulen des Landes NRW – gegründet 1965. Seit 1988 trägt die Universität den Namen des großen Sohnes der Stadt

EFFECT SIZES Table of Interpretation for Different Effect Sizes

Here, you can see the suggestions of Cohen (1988) and Hattie (2009 S. 97) for interpreting the magnitude of effect sizes. Hattie refers to real educational contexts and therefore uses a more benignant classification, compared to Cohen. We slightly adjusted the intervals, in case, the interpretation did not exactly match the categories of the original authors.

d r* η2 Cohen (1988) Hatttie (2007)
__________________________________________________
< 0 < 0 - Adverse Effect

0.0 .00 .000 No Effect Developmental effects
__________________________________________________
0.1 .05 .003

0.2 .10 .010 Small Effect Teacher effects

0.3 .15 .022 _________________

0.4 .2 .039
__________________________________________________

Zone of desired effects
0.5 .24 .060
Intermediate Effect

0.6 .29 .083

0.7 .33 .110
__________________________________________________
0.8 .37 .140 Large Effect

0.9 .41 .168

≥ 1.0 .45 .200
__________________________________________________

Cohen (1988) reports the following intervals for r: .1 to .3: small effect; .3 to .5: intermediate effect; .5 and higher: strong effect.

Type I and II errors

There are two kinds of errors that can be made in significance testing: (1) a true null hypothesis can be incorrectly rejected and (2) a false null hypothesis can fail to be rejected. The former error is called a Type I error and the latter error is called a Type II error. These two types of errors are defined in the table.
Statistical Decision True State of the Null Hypothesis H True H False Reject H Type I error Correct Do not Reject H Correct Type II error
The probability of a Type I error is designated by the Greek letter alpha (α) and is called the Type I error rate; the probability of a Type II error (the Type II error rate) is designated by the Greek letter beta (ß) . A Type II error is only an error in the sense that an opportunity to reject the null hypothesis correctly was lost. It is not an error in the sense that an incorrect conclusion was drawn since no conclusion is drawn when the null hypothesis is not rejected.
A Type I error, on the other hand, is an error in every sense of the word. A conclusion is drawn that the null hypothesis is false when, in fact, it is true. Therefore, Type I errors are generally considered more serious than Type II errors. The probability of a Type I error (α) is called the significance level and is set by the experimenter. There is a tradeoff between Type I and Type II errors. The more an experimenter protects himself or herself against Type I errors by choosing a low level, the greater the chance of a Type II error. Requiring very strong evidence to reject the null hypothesis makes it very unlikely that a true null hypothesis will be rejected. However, it increases the chance that a false null hypothesis will not be rejected, thus lowering power. The Type I error rate is almost always set at .05 or at .01, the latter being more conservative since it requires stronger evidence to reject the null hypothesis at the .01 level then at the .05 level.
A type I error occurs when one rejects the null hypothesis when it is true. The probability of a type I error is the level of significance of the test of hypothesis, and is denoted by *alpha*. Usually a one-tailed test hypothesis is is used when one talks about type I error.
Examples:
If the cholesterol level of healthy men is normally distributed with a mean of 180 and a standard deviation of 20, and men with cholesterol levels over 225 are diagnosed as not healthy, what is the probability of a type one error? z=(225-180)/20=2.25; the corresponding tail area is .0122, which is the probability of a type I error.

If the cholesterol level of healthy men is normally distributed with a mean of 180 and a standard deviation of 20, at what level (in excess of 180) should men be diagnosed as not healthy if you want the probability of a type one error to be 2%? 2% in the tail corresponds to a z-score of 2.05; 2.05 × 20 = 41; 180 + 41 = 221.
A type II error occurs when one rejects the alternative hypothesis (fails to reject the null hypothesis) when the alternative hypothesis is true.
The probability of a type II error is denoted by *beta*. One cannot evaluate the probability of a type II error when the alternative hypothesis is of the form µ > 180, but often the alternative hypothesis is a competing hypothesis of the form: the mean of the alternative population is 300 with a standard deviation of 30, in which case one can calculate the probability of a type II error.
Examples:
If men predisposed to heart disease have a mean cholesterol level of 300 with a standard deviation of 30, but only men with a cholesterol level over 225 are diagnosed as predisposed to heart disease, what is the probability of a type II error (the null hypothesis is that a person is not predisposed to heart disease). Z = (225-300)/30=-2.5 which corresponds to a tail area of .0062, which is the probability of a type II error (*beta*).
If men predisposed to heart disease have a mean cholesterol level of 300 with a standard deviation of 30, above what cholesterol level should you diagnose men as predisposed to heart disease if you want the probability of a type II error to be 1%? (The null hypothesis is that a person is not predisposed to heart disease.) 1% in the tail corresponds to a z-score of 2.33 (or -2.33); -2.33 × 30 = -70; 300 - 70 = 230.

Getting Started, Surviving, and Completing Your Dissertation: Moving Beyond the 'ABD' Status
By Brian Hunter, M.A.
Have you ever found yourself spending a Saturday or any day doing everything else you can think of except working on your dissertation?
Many, if not most, graduate students have a difficult time getting started, persevering, and finishing the dissertation process. You are not alone! There are many obvious reasons for delay such as the dissertation process is new, getting access to the articles and your major professor, time pressures, financial pressures and learning new skills/techniques such as sampling methods or the dreaded statistics. You are not alone in these challenges, but the single biggest obstacle is to dissertation process and completion is in your mind. Yes, the biggest problem actually lies with you.
What makes getting started and continuing on the dissertation process seem so difficult?
First, your dissertation does involve far more research than you probably have ever done before. Remember, by the time you begin your dissertation, you have already written many essays, reports, and conference presentations. A dissertation is really a compilation of seminar papers that are linked through conceptual unity. That means you have already done most or all of the work in many classes and the objective is to bring all that work together into one unified dissertation. So, the work is not unfamiliar to you. So, you may be asking yourself why does it seem so difficult again. Well, completing the dissertation process is largely based on overcoming the difficulties through perseverance. In other words, do not giving up and keep trying! This may seem over simplified and perhaps it is, since challenges still come up that stop your resolve and perseverance to move forward.
Why do I feel so many different things when it comes to writing my dissertation?
Emotional responses to the challenges in the dissertation process can vary such as feelings of anxiety, being overwhelmed, feeling burned out and frustrated. Frustration! Frustration! Frustration! There are also many extreme highs and severe lows in the process that can make you feel like you are on a roller coaster. Guess what? All these responses are normal! Experiencing emotions while doing anything is normal! Very few, if any, people engage in any professional or educational activity devoid of emotion. So, emotional responses only become a problem when it stops you from progressing and persevering in the dissertation process.
How do I survive?
This is the 20 million dollar question or 100 million, given inflation, but there are actually many things you can proactively do to survive. The first thing is to look at what is stopping you and getting you stuck in the process. In other word, what are the barriers and obstacles you are experience like interactions with certain professors or committee members, revisions, major changes, perceived negative feedback and delays? These perceived obstacles can be turned around in avenues.
Let us begin by taking a look at how to use and view your department chair or advisor. These chairs or advisors are your primary contact in the dissertation process, so all news of your progress, regress, success, or immediate failure comes directly from them. This can create strong emotions around meeting with and using your advisor regularly and appropriately. Remember, do not hate the messenger!!! You should seek out your advisor’s candidness, critique, expertise, and trust as these are invaluable to your educational and professional development. Try to build a relationship of cooperation, mutual respect, openness and trust. This sounds easy, but we all have different personalities and dispositions.
Your advisor is not in an adversarial role with you by trying to make the dissertation process difficult. Instead, they are a main source of support for you in achieving your academic success. You cannot control what your advisor does or how they behave, but you can take a look at how you are responding to them and the dissertation process. For example, when difficult news arrives from your advisor, committee, or research site there are many common responses form students:
• Moping and pouting about it for a week;
• Being distressed, angry and offended;
• Immediate responding with irate and ill-conceived replies;
• Taking a deep breath;
• Recognizing the value of academic critique;
• Calmly review changes and deficiencies in a cooperative manner;
• Maintaining emotional control.
How do you respond? Your response to the news and progress on your dissertation not only impacts you, but also impacts your future progress and the people with whom you are working.
Further paths to take to survival are networking with other doctoral students. Going through this process with other students helps to give you a perspective as well as support on your situation. You can also gain invaluable advice and experience about dealing with advisors and committees from students further along in the dissertation process.

How do I stay motivated and finish?
Given my experience working with students who have completed the dissertation process, I have seen some common patterns to staying motivated. These are some of my suggestions:
o Distill your dissertation down to one sentence. This should be from the purpose of the paper. Say this sentence to yourself each day that you are working on the paper or are trying to get started working. Post it on your computer or phone screen saver!

o Keep writing, even when you feel stuck. When I wrote this type of paper I would continue to write by telling myself in the text what was needed, like a list of things to get for the paper or by jumping to the next section. Continuing writing would keep my mind flowing and staying on task. Going to other sections of the paper allowed me to continue to make progress.

o Do random thoughts about your life keep popping in your head? Thoughts such as I need to go grocery shopping, I have to get my oil changed, what about that wedding, there a hurricane coming this way and so on. Write them down on a notepad or into a word document as a list of things to remember or do when you are done writing that day. These real life thoughts and pressures can pull you away from writing and there you are again doing everything, but your dissertation!

o No one ever wrote a dissertation in one day. Give yourself realistic deadlines and understand that there may be setbacks. Expect the process to take a year to two years or possibly longer. Everyone is in different circumstances. Very few people get to write a dissertation full time and not work or take care of others such as significant others, spouses and children.
I have one final recommendation. No matter where you are in the dissertation process, you can benefit greatly by reaching out and contacting a professional consultant. There are many consultants, like myself, who specialize in working with you on learning how to write an introduction, understand a literature review, teach APA formatting, teach scientific writing, tutor research methods and the dreaded statistics! Contact one of us today!