You don't want Correlation you want regression. Take a look at this book and software. They are excellent. The software is the best available and is a free download. Book explains all and gives you the instructions you need. Your dependent variable is probably acceptance with quality as your IV. Ask if you have questions. Link: https://b-ok.cc/book/5825760/ed313f

Best, David Booth

Dear Sara Alaqeel,

The correlation is measured in combination of the number of observations and the differences of means from each observation of the variable. Concerning this, it is really the matter how every mean of the variables is very close to its observation and the closeness is combined with the number of observation.

Ideally, based on your data available, run the correlation analysis and check the significance for the number of observation. If the Correlation Coefficient is significant at the 5% level, the observations are sufficient to you. If the Correlation Coefficient is insignificant at the 5% level, it simply indicates that your number of observations are insufficient and needs some numbers to be added.

Hope this will help you in a simple way. Good luck in your studies.

Regards,

S. Senthilnathan

You don't want Correlation you want regression. Take a look at this book and software. They are excellent. The software is the best available and is a free download. Book explains all and gives you the instructions you need. Your dependent variable is probably acceptance with quality as your IV. Ask if you have questions. Link: https://b-ok.cc/book/5825760/ed313f

Best, David Booth

What do you want to know about this relationship? Are you interested only in linear relationships? How precise do you want your estimates to be?

Use test family key, then choose Z tests, and from statistical test key select (correlations: Two independent Pearson's rs ( Two samples ) ). Use 0.05 as alpha , effects=0.5(Cohen suggested that d = 0.2 be considered a 'small' effect size, 0.5 represents a 'medium' effect size and 0.8 a 'large' effect size), power= 0.95 0r 0.90 , Here is a link to download G*Power manual:

http://www.gpower.hhu.de/fileadmin/redaktion/Fakultaeten/Mathematisch-Naturwissenschaftliche_Fakultaet/Psychologie/AAP/gpower/GPowerManual.pdf

Am sure your study is not just correlation studies. An, @DDaniel Wright has asked a very important question, what you know about the relationship (actually, sure its linear? How do you know?)

Pearson's correlation should be used only when there is actually a linear relationship between variables, and could give misleading result if interpreting for causality. Three things are considered, Power, Effect size and Sample size. A sample size (needed in a survey) can be calculated from the Population (minimum is usually 10%) or by formula (given acceptable error rate, confidence, eg, 95%, Power effect, ...).

Pearson's Coefficient [not ranked] Requirements: A minimum of two variables . A sample size of 8 to 10 paired observations, but we know that a larger dataset may be needed if the datasets are skewed or contain nondetects. The degree of confidence in order to detect patterns in the data increases with larger sample sizes. For example, a significant Pearson product-moment correlation at a alpha= 0.05 level of significance, a power of 0.80, and a medium effect size [0.3], you will need 85 people. (using ideas from Power Primer).

Table can be used(see the attached).There are so many similar research, such as a "Bidirectional association between physical activity and symptoms of anxiety and depression: The Whitehall II study" https://www.researchgate.net/publication/225059099_Bidirectional_association_between_physical_activity_and_symptoms_ittIv_nGYTPMVWC_mle7DO_FaRzDSdKg4rbXMP_NJOdFdyJ3WUsjIE4O0EEgx-8d2tNeEI.jAfIyDEGP63O46_SwRcNlSnk5j-wpbj33m0dDdz5hsfCmdWyxDfpqCsS_bnZuiFXbgnKSM9xwOYorYxIDQetXQ&_sg%5B2%5D=XKfXCLfBpbokkcvRJWFKVuMJUMse2_htA_bzlFZwcZjy2UW7peYlAZqqoGevXMYzxnOcy0fsrFuzbh9rfQ.jAfIyDEGP63O46_SwRcNlSnk5j-wpbj33m0dDdz5hsfCmdWyxDfpqCsS_bnZuiFXbgnKSM9xwOYorYxIDQetXQ&_sgd%5Bsr%5D=1

I would recommend that you use both the Pearson's Correlation (paired relationship) and Multivariate Regression for Depression[IV1], level of acceptance [DV] and levels of Quality of life [IV2, using the Beck Depression Inventory (BDI-II) and Quality of Life Scale (SQLS) for the surveys and Questionaires. See an example here https://www.google.com/url?sa=t&source=web&rct=j&url=https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4803983/&ved=2ahUKEwjg75z71PHsAhWWFMAKHXjaDa4QFjAAegQICxAC&usg=AOvVaw0IzJ3h5ZZ5e98nN9I7E8gS

Reference to "A Power Primer ", @ResearchGate. https://www.google.com/url?sa=t&source=web&rct=j&url=https://www.researchgate.net/publication/49619430_A_Power_Primer&ved=2ahUKEwiu2fTw3PHsAhWxgVwKHaLrDOUQFjACegQIBBAB&usg=AOvVaw3lI6rl32IAnorkabcK2rT0

Thank you all so so much for your time and help!

Samithamby Senthilnathan

Daniel Wright

David Eugene Booth

Abiodun Christian Ibiloye

I am pretty sure you have made it very clear and simple, yet, I am still having a trouble knowing all the terminology of quantitative research as this is my very first research proposal.(you can find it in the attachments)

As much as I have read I still have no idea how to calculate it or even knowing linear relationship. its a bit confusing tbh.

If anyone could help me via Zoom/ Skype meeting that would be wonderful!

please let me know

Dear Sara Alaqeel,

The proposal does not help anyone to answer your question. However, I give below a link that can help you to determine your required sample size.

The link is: https://www2.ccrb.cuhk.edu.hk/stat/other/correlation.htm

If you feet the data for the required field, you can get the sample size required.

There are a few explanations too and you get them to understand.

Hope this will help in your studies. Good luck.

Regards,

S. Senthilnathan.

In addition to that, there are youtube tutorials that demonstrate (with examples) how to use G*power. Here is one: https://youtu.be/vYjWkdQZCp0 Sara Alaqeel , There is no shortcuts to baseline(basic) knowledge, concepts and terminology you need, you really show readiness to learn, am not sure zoom will work for me...but inbox through my profile if you encounter any problem again. Best wishes,

Dear Sara Alaqeel , please, read carefully what Daniel asks you and the guide Abiodun Christian Ibiloye recommends on the use of GPower, but on regression issues.

Samithamby Senthilnathan, there is something that I do not understand well in your second paragraph. Are you looking for the size and sample that reject H0?

Your proposal is that if with the sample you have at hand you reject H0, you are comfortable with this decision, but if you do not reject it, then you affirm that with a larger sample size H0 will be rejected anyway, that is, H0 is it false without having to prove it? So why test statistical hypotheses?