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# Statistical Analysis - Science topic

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Questions related to Statistical Analysis

Hello,

We are conducting a longitudinal study on resilience in children. We have taken initial measures using a single scale that results in a continuous value and will continue taking measurements each school year for the next five years. What would be the most appropriate statistical analysis for this study in which I have nominal variables (grade, subject ID, time point) and a single continuous variable (resilience score)?

Thank you!

Hi everyone,

We have implemented four metaheuristic algorithms to solve an optimization problem. Each algorithm is repeated 30 times for an instance of the problem, and we have stored the best objective function values for 30 independent runs for each algorithm.

We want to compare these four algorithms. Apart from maximum, minimum, average, and standard deviation, is there any statistical measure for comparison?

Alternatively, we have four independent samples each of size 30, and we want to test the null hypothesis that the means (or, medians) of these four samples are equal against an alternative hypothesis that they are not. What kind of statistical test should we perform?

Regards,

Soumen Atta

Hello,

I am currently writing my dissertation and have a few questions regarding reporting my results. I am not very confident in statistics, so please bear with me if this doesn't make any sense.

I am using a two-sample Wilcoxon test for my statistical analysis which I am doing through R. I am struggling with how to report the results as am only given a W and p-value. However, I have seen that most people report their results using an additional U and Z value. Is it ok to report using only the W and p value?

Additionally, I have read conflicting advice on whether to report the mean or median values! Could someone please help? My data doesn't follow a similar distribution pattern, so I am unsure if reporting medians would be viable. However, I have been told that I shouldn't report means and standard deviations on non-parametric tests. I am a little confused about what the best thing is to do.

Please can someone help!

The construct was measured via different scales in order to use age-appropriate instruments.

In short, my MSc thesis involves providing recommendations to industry and these recommendations have been put to a series of experts in the form of a survey in order to validate the recommendations.

For each recommendation, four questions are asked relating to:

1. Whether the expert would advise clients to implement the recommendation

2. Whether businesses can generally afford to implement the recommendation

3. Whether businesses would understand the recommendation

4. Whether businesses could implement the recommendation without external support

Each of the four questions utilises a 5 point likert scale ranging from very unlikely to very likely.

I have never undertaken statistical analysis before and have historically been more of a words person than a numbers person. I'm intending to use SPSS for data analysis but if anyone were able to point me in the right direction of appropriate tests to run I would be exceptionally grateful.

I have made detection for microsatellite SSR markers in patient with plasmodium...

for statistical analysis, I have used genalex 6.5 ...

I still need to make confirmation with another software...

can anyone help me within this point?

I have 2 groups I tested before and after the experiment. Comparing the HR, BP, and other anthropometric before and after in control and experiment groups. Used Wilcoxon signed-rank test to compare before and after results in each group, which method is best for statistical analysis of controls over experiment group before and after. The number of subjects is between 5 to 10 in each group.

I am conducting a study on multiple marginalized identities and mental health outcomes.

What would be the best data analysis I should use to assess the interaction between different variables and the way they intersect with each other.

Thank you

Hi

As you know, for using nonlinear regressions in statistical software like SPSS or Minitab or codes, you need to determine a start point(start value or initial guess) for regression's parameters. Actually you need to choose an optimum start value for parameters to achieve the best nonlinear equation.

How can we determine the optimum start value?

Is there an other way (the other software) to use nonlinear regression regardless to parameter's start value?

I feel like maybe this question is so easy that it's hard and I keep doubting every choice I make, so I thought I would just finally ask online. I have one dependent variable with three levels: percentage of caches a bird make in one of three different types of substrates (sand, gravel, or other). My independent variable has two levels (the three conditions the birds are in whilst caching).

I pretty much know my pattern of results and I have figures drawn up and everything (I can post a small subset of the data if that would be helpful to people in answering the question), but I cannot, for the life of me, figure out if I have chosen the right test to analyse this data and I keep having doubts every time I make any progress.

I'd ran a similar study not too long ago that was almost identical, except the independent variable only had two levels (gravel and sand). Therefore, I could just run a Friedman ANOVA (since the data violated normality assumptions) for percentage of caches in one or the other substrate.

Right now the only solution I have been able to come up with for this is running multiple Friedman ANOVAs, but, as mentioned, I am really starting to doubt this. This is primarily because--unlike when I only had 2 levels in my DV--, I cannot just run a test for one of the levels (e.g. in my first experiment, if 25% of the caches were in sand, you automatically knew the other 75% were in gravel; however, for this experiment, if 25% are in sand, that only means that 75% are in gravel or "other" and you do not know how it is divided up between the these two).

I know there are more advanced forms of analysis as well, like mixed models, but I've been specifically advised against doing those in this particular instance.

It's just that I keep writing and writing, then I get freaked out about my stats, so I do a bunch of research, then I try to re-calculate and re-write….and it's getting to the point where I can't do that anymore. So, I kind of need to know now if I need to change my method of analysis.

Could anyone give me advice?

Doing this reduces uncertainty on flat part of trend, closer to present day, because the small values at the early part of the trend have the largest uncertainty and this carries through to present day uncertainty.

I have data for about 5500± of trees flowering data for 4 consecutive flowering events. Need more idea on what statistical analysis that i can do ? or any suggestion ?

I am trying to do an analysis of the Job Demands and Resources Model (JDR) with a one mediating variable using the Hayes model.

However, I am not sure how I can do an analysis of one big model of both demands AND resources (Multiple independent variables) and Outcomes (dependent variables). Motivation is the mediating variable.

I believe perspective research is all about publishing new ideas or arguments in terms of statistical analysis, optimization of data in innovative ways or simply an opinion/perspective about existing research on an interesting topic.

I am looking for some journals/Editorial in materials science who can either accept our proposal or allow us to publish perspective research article in their journal.

Like if I use a biochemical marker at admission as predictor of mortality in the long term what statistical analysis would be bet suited?

I have measured the RMSE for both groups for different dentures, but have an issue with the statistical test that I should be using? can I use Paired T_test? or what test should I go for to combine all RMSE values of all dentures in each group? and how to compare two models for statistical significance?

I need to defend PCA against structural equation models...

Hi

I have a dataset and I need to check the goodness of fit for Pearson type3 for them.

How can I do it?

Any software? Any MATLAB code?

Thanks

I am new to statistical analysis. I came to know about the two terms: c

*orrelation*and c*ausation*. Correlation and causation seems similar but they are not the same thing. Sometimes it becomes confusing to differentiate whether the relationship between two or more phenomena are causal or correlational. What are the ways to point the differences?For example: Do rainfall and flooding have correlation or causation? How can we evaluate?

I am doing a research on the impact of social media on labour productivity, with LP broken into 3 categories. A questionnaire was administered to employees comprising of a mix of open-ended questions, as well as a 5-point Likert scale.

But I am at a loss with which statistical analysis to use to analyse the responses obtained.

How do I know if the data I collected follows a normal distribution or not through SPSS? And if it does not follow a normal distribution, how do I convert it so that parametric tests can be performed on it?

Hi

I have 4 IV (categorical data) - age, gender, ethnicity, job position

and 1 DV (continuous data) - muscle strength

What is the most suitable statistical analysis because I want to find which factor (age, gender, ethnicity, job position) contributed the most toward the muscle strength?

Thanks!

I am trying to measure the level of awareness (obtained through Likert-scale and total scores categorized as Low, Moderate, High) of participants regarding a certain topic.

Hi Reserchers,

I am doing a computer science dissertation on the topic '' Automate text tool to analysis reflective writing''.

The hypothesis set is ‘To what extent is the model valid for assessed reflective writing?’ I just want from the questionnaire( closed ended questions and one open question) to validate the proposed model.

I have used the used the 5 point likert scale for analysing the data, option given strongly agree, agree, neutral, disagree, strongly disagree. The sample size is 10 participants. I have chosen my participate based on their experience, career and knowledge of the reflective writing.

1) Which statistical analysis tool shall I use to analyse 10 sample size to validate the model? Please show me step by step on how to analyse the data?

2) What would be the associated hypothesis?

3) Can I use Content Validity Index with 10 sample size participants on the questionnaires using 5 point likert scale?

4) this step on my research Is it qualitative method or quantitative method?why?

**If you have any suggestion on my hypothesis, the sample size and the tool I need to analyse?**

**Thank you in advance !**

I have an hedonic sensory data which is generated by using 2 samples and 30 same panelist. Do I have to use the statistical independent methods (Mann-Whitney U) or dependent ones (Wilcoxon test)?

Note: the data isn't normally distributed

Thank you

A few articles with qualitative method have used a descriptive statistic analysis and a statistics profesor said that it is correct. My question is: if you use statistics in those researchs, won't it be a mixed study?

I am conducting confirmatory factor analysis using lavaan in R with several both first- and second-order factors in the model (20 latent constructs and 97 items in total; n = 511; no missing values). After running the CFA, the model fit is not sufficient, but some low factor loadings, a couple Heywood cases and several indications from the modification index give some pointers for model improvement. Deleting some items from the model and re-structuring two factors based on an EFA eliminates Heywood cases and results in acceptable model fit (Chi-square=4117, DF=2646, CFI=.94, RMSEA=.038, SRMR=.052), however, the CFA of the adjusted model now returns the following error message: "covariance matrix of latent variables is not positive definite". There aren't any negative values in the covariance matrix though and also not in the correlation matrix.

I'm fairly new to this kind of statistical analysis and have no clue what causes this error and how to fix the issue. I'd appreciate any help. Please find attached the original and adjusted CFA measurement model as well as the covariance and correlation matrices of the adjusted model causing the NPD error.

Thanks a lot for your input!

In R-studio software which statistical analysis will do for moisture content in soil?

please guide

Thanks in advance..

I want to make a stacked bar graph with showing each data value as dots. I tried with graph pad prism, version 6, but could not succeed. Does anyone have tried before making such graphs?

since i am using likert scale for both dependent and independent variable

(My research is about development of FR intumescent coating by addition of additives, my goal is to have a more thermal resistive sample than the controlled sample without neglecting the time(aim: lesser average temp or better "temperature vs time" relationship( negatively related)). I performed a horizontal burner fire test for 8 different samples, recording the temperatures in 1 hour per minute from each samples.What statistical tool/method do I use the data gathered( time vs temp) to conculde that sample a is better than the controlled sample? I am not quite sure to use correlation and not knowledgeable enough to other tests thank you.

Hello!

So, here is the story. I was give this Likert scale data for analysis, and I just can't get it how I should deal with it. It is a 1-7 scale with answers ranging from 1 being "extremely worse" to 7 being "extremely better". But here is the problem, 4 is "same as was before" and questions introduce the changes as an effect of a different variable, which is work from home (for example, "Compared to work from office, how much has your ability to plan your work so that it was done on time changed when working at home?").

Questions are separated into some groups to form variables, and mean should probably show each person's opinion on the change, right? But it just seems too strange to me to work with just 1 parameters and not go through full comparison of now vs before as 2 different constructs.

If you have any works or insight on the topic, can you please help me?

All the best and take care!

Hello, thank you in advance for any answer!

A version of the instrument, the CTS-2 (Revised Conflict Tactics Scale), adapted to my country (Portugal), was used in the study I am working on, in order to find out the tactics people use to resolve conflict in an intimate relationship. It is comprised of 39 items, which are to be answered on a Likert scale, ranging from 1 (one time in the previous year) to 8 (it never happened). Every item is to be answered 2 times, one time to specify if the individual has done what says in the item (perpetration), and one time to specify if it was ever done to them (victimization), which represents a total a 78 items. It has five sub-scales (negotiation; physical abuse; psychological agression; sexual coercion; injury) and each sub-scale has three different levels (minor; severe; total), with the items being divided by these levels and sub-scales (e.g. items 11 and 71 belong to the Injury sub-scale, minor level).

As specified by both the original author and the authors which adapted the instrument, to obtain the global prevalence of either victimization or perpetration, being that perpetration is the focus of my study, one must transform every item into a dichotomous variable (1 through 7 is 1, indicating it has happened and 8 is 0, indicating it never happened). This is where my question comes in. I have transformed every item into a dichotomous variable, but I am completely lost at to what the SPSS processes are to obtain the percentages of perpetration for the levels and sub-scales from this dichotomous items.

Thank you very much

I'm part of a team designing an observational study in which we're going to compare 2 models of intraocular lenses. The main outcome is contrast sensitivity.

I'm having some difficulty in understanding how we are going to handle this variable in statistical analysis and looking at the literature hasn't helped me much. It's also important because, being the main outcome, our sample size calculation depends on that.

Has anyone worked with something similar and can give me some pointers?

What feature analysis techniques or new approaches can be applied to analyzing cardic ultrasound images for detection of a defect.

Good day, everyone!

I'm doing a research on Personality Traits, Reading Habit, and Writing Achievement. My data were collected using Likert-scale questionnaires (for personality traits and reading habit) and a writing test. My purpose is to find the correlation between personality traits and reading habit, personality traits and writing, and reading habit and writing.

Does anyone know what kind of statistical analysis I should use? Thank you, have a nice day!

I am a Master student and for my graduation I must perform some statistical analysis. I created the questionnaire myself: it begins (section 1) with asking characteristics about the person without using the Likert scale (public/private; previous collaborations; amount of partners..) then the questionnaire is divided into 3 further sections. The second section is divided into 9 themes, and each theme contains between 1 and 4 statements (some negative and some positive - actually, will this be problematic?) to which respondents have to answer using the Likert scale and say whether they experienced this "theme" during collaboration. After, the third section, they are asked about their satisfaction in relation to the nine themes (1 statement per theme) as a measure of effectiveness. Finally, in the fourth section, which is very small, I ask them about whether benefits outweigh the drawbacks and whether they see the collaboration as helping them achieve sustainable development goals (SDGs) as a further measure of effectiveness.

1. How is it possible for me to look at whether their characteristics (independent variables, section 1) affect their perception of these 9 themes (section 2)?

2. How can I see if their perception of these nine themes (section 2) is correlated with their satisfaction (per theme, section 3)? And whether their separate characteristics (section 1) are correlated with their satisfaction (section 3)?

3. How can I see if their satisfaction (section 3) is correlated with their perception of whether the collab is helping achieve SDGs (section 4) and their overall thoughts on benefits/drawbacks (still section 4). I.e. if effectiveness can be measured by satisfaction, as well as via the perception of whether the collab helps achieve SDGs

Hi esteemed colleagues,

I am seeking assistance on computing regions of significance following a significant interaction. My model is as follows: Condition (Three levels - predictor), word count (predictor), WC*Condition (interaction), and depression ratings (outcome), and T1 depression ratings (covariate).

As noted above, the interaction is significant and now I would like to determine at what regions of word count do conditions significantly differ. For instance, the treatment only differs from controls when participants write > 2,000 words... that kind of thing.

I was thinking of using the Johnson-Neyman Technique, but I'm unsure how I would compare conditions with that approach, as the technique typically evaluates simple slopes of continuous variables... though maybe that is inaccurate. I'm most comfortable with SPSS, though am learning R as well.

Thanks in advance!

Hello,

My friend is seeking an collaborator in psychology-related statistics. Current projects including personality traits and their relations to other variables (e.g., age). You will be responsible for doing data analysis for potential publications. Preferbably you should have some knowledge about statistics and is fimaliar with software that is used to do analysis (e.g., MATLAB, R, SPSS). 10 hours a week is required. Leave your email address if interested.

I want to study the hypothesis that higher levels of all 3 IV would lead to higher levels of the 2 DV.

I also want to compare both levels of the 3 IV and 2 DV between multiple ethnic groups.

Is a three-way ANOVA all I need?

Dear Colleagues ... Greetings

I would like to compare some robust regression methods based on the bootstrap technique. the comparison will be by using Monte-Carlo simulation (the regression coefficients are known) so, I wonder how can I bootstrap the determination coefficient and MSE.

Thanks in advance

Huda

I am trying to understan if there is a latitudinal cline in the degree of melanization of a frog, but I am not quite sure of which would be the best statistical analysis to perform.

I am looking for a

**biostatistician**who is interested to work in collaboration with us for various research projects., should have experience with meta-analysis and systematic reviews?if you know someone, please recommend me. thanks

I want to optimize the experimental conditions for a qualitative (pass / fail) type of test. Kindly suggest what type of

1. design of experiments can be used to optimize such tests

2. statistical analysis can be use to check validity of the test results

Examples/literature references in the replies will be highly appreciated.

I am conducting a program evaluation plan for an outpatient pulmonary rehab program and am having trouble determining the statistical analysis methods to use. The following process and outcome evaluation questions I have are:

- How many days, on average, does it take a participant to enroll in class following referral/evaluation?
- What was the average number of initial evaluations conducted per week over the last year?
- What was the maximum class size over the last year?
- What percentage of program participants attended the pulmonary rehab program over the last year?
- What percentage of program participants are readmitted to the hospital within three months of completing the program?
- In what ways do the participants feel they are benefiting from program participation based on feedback from the feedback surveys?

Any help would be appreciated in determining the appropriate statistical test to use as well as the phrasing of questions. Thank you!

**warm greetings to all Drs , Mrs.**

did any one have and idea what supposed to do in statistical analysis for research , I have a sample composed from 135 patients , each patient I got 8 categorical variables and 8 quantitively variables , 135 patient grouped to 3 groups class I , Class II and Class III by one parameter called ANB and 3 groups Short , Average and Long face by another parameters called FMA , also two groups be gender Male and Female , I did the normality test for 8 quantitative variables and I got some of variables statistically significant and others not ,

**now my question is how can we conduct normality test for categorical variables especially I define the variable as string , did I need to change it to numeric ?****second question did I need to conduct the same test for statistically significant and statistically non significant variables in normality test ? I mean parametric and non parametric test ? which one I should choose according to p value**

**if I need to conduct non parametric test how can I do it if my data composed from three groups .**

**I'm so sorry for my English language , and i hope anyone have any helpful source just post it here .**

**thank you all.**

Dear Experts,

For my research, I have collected temperature values of 15 locations in an urban area (each point 6 data) and repeated data collection 6 times. I calculated the mean valess of each point in each time (15 mean values*6 times), then I calculated the mean of means (mean of 15 mean values*6 times) and finally, I got 15 values. I used these final values for statistical analysis such as t-test, Person and regression analysis. my results are good and meaningful to address my objectives. is it proper to report these results in a research paper? I am not confident about if it is a proper way to use Mean values for statistical analysis instead of original values.

Thank you for your time and guide.

Dear Experts,

It is stated: for 15 pairs of data, with 5% significance level, R2 values should be higher than 0.2601.

how can I know such R2 value for 540 pairs? how can I find this certain value for a different number of pairs?

Is it understandable using SPSS?

Thank you

I'm looking at the effect of the drug on lung function. What test should I perform for pre and post control vs pre and post treatment? Control group had measurements taken before and after recieving placebo and same for the tratment group, however they were administered a drug. Different subjects were used in control and treatment groups.

I'm very thankful for help.

I want to develop a relationship between induction hardening process parameters with case depth. Keeping all other variables constant such as quench delay and quench time, I want to analyze the effect of voltage percentage and heat time on case depth. Any recommendation of any statistical analysis will be highly appreciated.

I have two dependent variables.

These two are

before starting the program - "Starting Reading level" after starting the program - "Ending Reading Level". (A pre-test and post-test scenario).

After that I have one independent variable (the independent variable is "startdate")

with three groups - Cohort september 2019, Cohort february 2018, march 2018.

I would like to know which test is best for comparing the mean of the starting and ending reading level factored by each different program.

Hi

We have two time series dataset. For example mean daily temperature from two stations. Each of them have 30 data for a month.

T

_{1}={ t_{1}, t_{2},... , t_{30}}T

_{2}={ t_{1}, t_{2},... , t_{30}}Now we want to calculate the similarity between them. Some people may suggest correlation coefficient for this task but I think we use it when we consider the relation between datasets not similarity.

Is there any index to measuring the similarity?

Thanks

Hi all. First of all, I'm sorry if this question isn't phrased right or posted correctly. I am quite new to research of this calibre and need help with the statistical analysis part of it.

Let me give a brief introduction to the research that I am conducting. Earlier research has shown that lean inventory management could have a beneficial impact on the financial performance of firms. Now I want to research whether firms that practise lean inventory management have been less affected by the pandemic than those that do not practise it.

My dataset, therefore, is the financial data of US manufacturing firms from 20016-2020, with the last being the year of focus.

I have made some simple graphs (as can be seen in the attachments: Industry Plot) of the aggregated Net Sales per year to show which firms have been negatively impacted by the pandemic.

Now I need to get to the bulk of the research, the actual statistical analysis and this is where I am absolutely lost. Firm performance will be measured in ROA or ROS and Inventory Leanness by the empirical leanness indicator (as proposed by Eroglu and Hofer in Lean, Leaner, Too lean? (2011)). Total assets and percentage growth per year will be used as control variables.

How do I go to conducting this research? What type of model is best to use for this?

Hi,

I have a dataset with 25 outcome variables (derived from a 25-item scale), and am hoping to do some ordinal regressions with it. To test the proportional odds assumption beforehand I need to create 3 dichotomous split categories for each outcome variable, however this split format will be uniform across each of the 25 outcome items. Is there a way to do this in SPSS so I can 'bulk' apply the cumulative split format to each of the outcome variables, so I don't have to do it manually 25 times over?

I'm not the most proficient at SPSS, so would really appreciate some advice on this, thank you.

Dear colleagues,

From the screenshot, you can see my OLS estimations between institutional variables and oil-related predictor variables. My main hypothesis was that oil-related variables have a negative impact on institutional quality (according to Resource curse theory); however, my estimations produced mixed results, giving both positive and negative coefficients. In this case, what should I do? How do I accept or reject the alternative hypothesis that I have already mentioned.? Thank you beforehand.

Best

Ibrahim

Hi everyone! What statistical analysis method would you use if you like me had repeated an experiment 3 times. The experiment consist of control and treated with drug A, B or C. The experiment had 3 replicates per treatment group (3xcontrol, 3xdrug A etc). The experiment was repeated two more times, so in total I have results of three similar experiments (the same setup). Would you go for multivariable ANOVA?

Thanks for the help!

What methods are best used in the statistical analysis of ultrathin cell sections for transmission electron microscopy? I need to estimate the number of vesicles 50-100 nm in size in different samples. Unfortunately, other microscopic methods cannot be used here.

1. What statistical analysis can we use for a low sample size (n=3): between 2 groups and between more than 2 groups

2. If we take two samples and run them in technical replicates of say 6, can my n be considered as n=12 (2 samples x 6 = 12)

3. What is the best method to calculate sample size, in case of rare samples

We are conducting a study about the correlation of covid-19 psuedoscience theories to the proliferation of COVID-19 positive cases. Which statistical test should we use to correlate the data that we are going to gather from the Covid-19 pseudoscience theories scale (4pt likert scale) to the number of positive cases? (data of the covid-19 positive cases will be from the health departments tally). Thank you for those who could help us in advanced. ☺️

The plan is to create a 2x2 between subject research design

1. Seperate the subjects into two groups based on the measurement of the independent variable (group identification: high vs low).

2. Same Measurement for 2 dependent variable (pretest) for both group

3. Each subject on each group randomly receive 2 different treatment (T1 vs T2)

4. After receiving the treatment, all the subject take the same measurement for the 2 dependent variable (posttest).

So I have 4 cell based on their group identification (high vs low) X Treatment (T1 vs T2)

Basically, what I want to know:

1. Is there a different/change between pretest and posttest for each cell?

2. Comparing the score of both dependent variable between each cell?

I'm wondering how to do the statistical analysis. Thanks

The center offers various activities from crisis counseling to resume development. I am proposing a qualitative evaluation that uses unstructured interviews to investigate which of the services offered at the center the teens believe helped them obtain employment or make progress in school. Someone is thinking a much better methodology would be to use statistical analyses to see which activities correlate more strongly with the desired outcomes. Which would be the best choice of design?

I will be running an experiment on a pro hormones effect on BMR.

I will be measuring BMR once a week for 6 weeks in a control group and a group receiving the drug. How should I most accurately go about running statistical analysis? T-test?

Dear all,

I want to have a paper in "water contamination statistical analysis", so please share your comments on which software and which methods can I use for this topic?

Also which parameters should be more focused on this topic?

I have collected data on Hoolock gibbon population and environmental variables in their respective habita/

I am doing statistical analysis on some existing data, but my significance result seems to be different from the existing ones. What could possibly be the reason?

Hi, everyone

In relation with the statistical power analysis, the relationship between effect size and sample size has crucial aspects, which bring me to a point that, I think, most of the time, this sample size decision makes me feel confusing. Let me ask something about it! I've been working on rodents, and as far as I know, a prior power analysis based on an effect size estimate is very useful in deciding of sample size. When it comes to experimental animal studies, providing the animal refinement is a must for researchers, therefore it would be highly anticipated for those researchers to reduce the number of animals for each group, just to a level which can give adequate precision for refraining from type-2 error. If effect size obtained from previous studies prior to your study, then it's much easier to estimate. However, most of the papers don't provide any useful information neither on means and standard deviations nor on effect sizes. Thus it makes it harder to make an estimate without a plot study.
So, in my case, when taken into account the effect size which I've calculated using previous similar studies, sample size per group (4 groups, total sample size = 40 ) should be around 10 for statistical power (0.80). In this case, what do you suggest about the robustness of checking residuals or visual assessments using Q-Q plots or other approaches when the sample size is small (<10) ?

Kind regards,

I have the results of a survey that I have been asked to put on the same scale for statistical analysis. The problem is, they don't quite all fit on the same scale. For instance, some questions have the typical Likert-scale style of response like "Not much" to "A lot". Others have more of a Bipolar Likert-scale style like "It has increased" to "It has not changed" to "It has decreased". Others are Yes/No. I'm at a loss as to how to incorporate all of these on the same scale, or if it's even possible. It's been several years since I've taken a class in this, and I'm a little out of my element.

I'm working on my thesis right now about relationship between sport heritage values and supporter's sense of place (of the city they live in). I had the idea that I should first measure the general sense of place using a likert scale system of several statement similar to those of Willams and Vaske (2003) and Jorgenson & Stedman (2001).

Then I had the idea to compose statements (also in likert scale format) regarding the sports club (the ''sense of the sports club'' as it were) that I based on sport heritage factors/values I explored in the theoretical framework.

So in short: I want to measure two different sets of likert scale data from the SAME respondent group and then I want to measure if there is a correlation between the sport heritage data outcome and the sense of place data outcome. As in for example: does a high 'sense of sportsclub' result in a higher sense of place compared to a lower sense of sportsclub?

Now comes my question(s): Is this a good idea? and so yes, how to measure this? Which statistical analysis should I use or is it maybe better to make sport heritage statements with only yes/no answers? Or am I tackling it the wrong way? (statistical analysis isn't my strong suit)

Any advice is welcome!

I am suggesting a RCT parallel intervention assessing diet on diabetes symptoms. The control group (following a keto diet) and intervention (following an organic keto diet) and I’m measuring participants HbA1c, waist to hip ratio and weight. What is the best statistical test to choose to analyse results please?

Is it important to select between paired and unpaired tests on the basis of gender also?

Hello. I hope someone can help me with figuring out the latest approaches to doing a survey study. I have a couple of questions as following:

1. What would be a more advanced level of doing analysis for a survey study beyond descriptive statistical analysis and correlational analysis?

In particular, how would one decide between regression, factor analysis, decision tree analysis, and/or cluster analysis? Or something else?

2. What if the data set has many missing values...I read threads about the recommendations but I lack the knowledge and I hope to know the easiest way possible without losing N size. In fact, I'm tempted to the mean substitution despite the bad reputation regarding it.

Ultimately, if the data set can't be managed properly, I guess I should just report the descriptive analysis and the correlational findings...Would this still be publishable, if this is the new-kind topic? Any recommendation for books to read will be a great help. Alternately, if any gold-standard survey study written as an example could be useful too.

Any thoughts would be much appreciated.

Thank you so very much!

Hello all,

Although the total effect (.18) and the indirect effects through mediators are positive, we have a negative direct effect of X on Y. How can it be possible?

Note 1: The analysis was run via PROCESS model 4.

Note 2: All variables are continuous.

I once read if i have a single independent variable and two+ dependent variables, i should use multivariate analysis.

But then i read somewhere that multivariate analysis = inferential statistics (where the analysis results generalizing the whole population)

Is it possible to use statistical analysis that won't generalize the results?

Following a previous question I posted about statistical analysis for a table (image included), I conducted a chi-square test - χ2 (6, N = 317) = 28.78, p < .0001.

As these results were for a 4x3 table, I conducted a post hoc test to determine where any relationship existed. However, I've not conducted a post hoc test before so am uncertain as to how to interpret their residuals (image included from R).

Any help would be greatly appreciated.

Hi. I had done a non probability survey to collect information on retirment planning among workers about to retire and those who had already retired. I now want to compare the two populations planning strategies. My variables are categorical include aspects of financial planning, housing, living arrangement, health and so on. Please suggest a suitable method to do this.

I designed a factorial experiment involving 2 explanatory variables (A and B, qualitative). Because I couldn’t achieve the assumptions of a parametric model, I used kruskal.test on the variable to explain (VAR) for A and B like: kruskal.test(VAR ~ A, data = data) and kruskal.test(X ~ B, data = data).

But, I was also interested in the effect of “A and B interaction” on VAR. So, does anybody know if it is right to perform a kruskal-Wallis test on interactions? Here, what I did it with R:

interAB<-interaction(data$A, data$B)

kruskal.test(VAR ~ interAB, data = data)

Moreover, in order to access which level of each variable is significantly different from each other, I used as post-hoc test after the kruskal.test: pairwise.wilcoxon.test(data$VAR, data$A,p.adjust.method ="holm",exact=F, paired=F). The pairwise test didn’t work on the variable interAB and I was wondering what method I should use as post-hoc test for each variable A and B and for the interaction interAB.

Any Idea please?

Hello, I conducted a pilot study with limited number of participants n=6. originally planned 20, but due to covid got less participants. I was checking A1C before and after telephone-based intervention (two tailed t-test).

For this small sample should I even check the normality?

Also I did not see any statistical significance, also probably to the small sample size. Should I use descriptive statistics only?

Is it necessary to test for normality and homoscedasticity of variances before comparison of means in statistical analysis? If yes, how can I do?

If we have two unpaired data sets, out of which one comes under normal distribution and the other one comes under the not normal distribution. Which statistical test should be applied to analyze the data

I have a question about whether statistical analysis to conduct in SPSS.

I have a factor that includes two levels. Also, I have multiple dependent variables with repeated measures.

Which statistical analysis is more suitable? A one-way repeated measures ANOVA with the independent factor (between-subjects) and time (within-subjects) or something else?

Hello!

I have successfully developed and implemented ANFIS in R with the help of FRBS package. Just one thing that is remaining is to visualize the ANFIS network.

Currently due to some constraints because of COVID, I don't have any access to Matlab while working from home. So I was wondering if there is any way to implement it in R.

Hello,

I have a question regarding statistical analysis of the thickness of thermally evaporated organic thin films. I need to deposit a rather thick organic film (300 nm) through a slow thermal evaporation process. After a few repetitions, it becomes clear that there is always a mismatch between the target thickness and the measured thickness, and the deviation had a normal distribution. In order to estimate the standard deviation of the thickness and run capability analysis, I needed to have a large sample size of these deposited films. In the interest of saving time and material, I have done my repeated qualification runs at a smaller target thickness of 50 nm. While I now have the mean and standard deviation of the process with a target thickness of 50 nm, I'm wondering how to derive the corresponding data for a target thickness of 300 nm. I assume I can simply multiply the mean by 6. But how about standard deviation? Can I simply scale that linearly as well?

Thanks,

Pouyan

I am conducting a systematic review and metaanalysis of the prevalence of exocrine pancreatic insufficiency after pancreatic surgery.

I have used MetaXL software to calculate a pooled prevalence with 95% confidence interval assuming a random effects model.

Later, I have done a subgroup analysis, obtaining a pooled prevalence with 95%CI for each subgroup. Now I wish to compare those prevalences (more than two) to evaluate if the difference between the prevalence of each subgroup is statistically significant (p < 0.05), but I don't know how to do it.

The software MetaXL doesn't seem to have an option for comparing prevalences.

What is the statistical analysis I have to do? Can you recommend me any software? (I'm a student, so I'd appreciate if the software is open access).

Thank you.

I'm doing a paper and need to find a negative or positive relationship between attitudes (5 option likert scale) and level of education (5 options). Do I do chi-squared, spearman's rho or Kendal's tau. Please help.

I am wanting to calculate the average trend in maximum annual NDVI in Iceland from 2010-2020 using MODIS MYD13Q1 V6. How would I do this?

I have currently inserted the NDVI bands from the MODIS (which I downloaded from the earth explorer website) into ArcMap for each year and used the cell statistics tool to calculate the maximum NDVI and used the raster calculator to remove any negative values, leaving me with maximum NDVI layers for 2010 through to 2020 but I do not know how to calculate the average trend for this period.

Please can someone advise me on how to do this and also any statistical analysis I could use - I am familiar with R so preferably using RStudio to do this statistical analysis, thanks!

Hi all,

I am working with a sample size of 749, and testing relationship between a continuous DV and multiple ordinal predictors. The predictors are education level ( 4 categories) and income level (4 categories). Additionally, I might use gender (nominal) as a predictor as well. In this scenario, which regression model would be appropriate?