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# Research Statistics - Science topic

Explore the latest questions and answers in Research Statistics, and find Research Statistics experts.

Questions related to Research Statistics

Our group conducted an undergraduate research about fiber-polypropylene composite panels, we have a 4 different independent variables that includes fiber ratio (0% ; 5% ; 10%), length(1cm/2cm), fiber age(old-young), and fiber treatment (treated/untreated) to measure the strength(dependent variable) of the composite panel. Is factorial

**ANOVA**will do or**ANCOVA**? If not, what**statistical treatment**would be appropriate for our research?We have two Likert-type scales: Brief-COPE and Perceived Stress Scale.

I often read research articles that do not appropriate describe the sampling procedure. For example, the authors mention that the sampling was accidental or purposive but they don’t explain why they had only 10 participants or they say that the sampling was theoretical and describe the a priori established socio-demographic characteristics of their subjects.

I think that the sampling procedure is one of the most important elements of a research. I think that the research should be evaluated according to this procedure and I think that many pieces of research should be rejected because of sampling not being appropriate.

Please help me to clarify this and correct me if I am wrong:

The sampling procedure should state the sampling criteria and should justify the number of participants.

There are two kinds of sampling in social research: statistical – probability or non-probability (when the sampling criteria and the number of participants are established before entering the field according to some rigid sampling rules) and theoretical (when the sampling criteria and the number of participants are flexible, decided in the research process according to relevancy and saturation rules).

Dear Colleagues

Have you ever had such publishing experience that the editor evaluates your qualitative research according to the rigors of quantitative research?

My story:

The editor of one of the journals assessed (negatively) my article containing a qualitative case study research, according to the quantitative research rigor. He stated:

"The analysis falls well short of the rigor required of an academic publication. No evidence is provided about whether the results would generalize to other settings, apparently there were no control groups, there are no statistical tests, the results did not generate enough clicks or sales to have a clear interpretation, etc. "

I have answered (and waiting for reaction):

"...you assessed it according to the rigor of the quantitative research ("no statistical test", "to generalize"), while my research is qualitative ("to identify categories and/or patterns") and implemented according to the procedure suggested by K. Eisenhardt.

Please read the classic article by Bourgeois and Eisenhardt (attached), which analyzes four observations (stories) and doesn't contain tests. Eisenhardt is quoted in almost all articles made with the case study (qualitative) research method. Please rate my article according to the principles of the qualitative research rigor."

Bourgeois LJ, III and Eisenhardt KM (1988) Strategic Decision Processes in High Velocity Environments: Four Cases in the Microcomputer Industry. Management Science, Vol. 34, No. 7 (Jul., 1988), pp. 816-835

Have you ever had such experience when editor or reviewer uses quantitative research rigors to assess a qualitative research?

What is your experience as to publishing the qualitative research?

Regards

Richard Kleczek

Hi everyone,

I ask what is the software and the steps to analyse ADV measuring data?

thanks in advance

For conducting research statistics is very important. Without the proper knowledge of statistical analysis, proper interpretation of data is not possible. Just good research is not enough alone if the results are not presented in a palatable and accurate way. Only statistical knowledge can help in this regard.

Dear researchers

Is regression analysis suitable for scientific research method to the data set obtained from secondary sources (index, annual reports, statistics published by institutions, etc.)?

Best Regards...

Hello my expert friends

I am currently looking for any articles or researches or statistics on the moderating effect of legal knowledge on behavioral intention.

I would be obliged if anyone of you could kindly share your research or any research u know which specifically touch on this issue.

God bless. Thank you very much.

Hello my expert friends

I am currently looking for any articles or researches or statistics on the moderating effect of ethical knowledge on behavioral intention.

I would be obliged if anyone of you could kindly share your research or any research u know which specifically touch on this issue.

God bless. Thank you very much.

Regards

Cikgu Armand

Hey!

I'm currently doing research on academic spin-offs. I send my survey to the complete population (196 companies/founders). Normally, I would need a sample size of 132, but I'm sure this is impossible. What to do with my research when I only get +-75 responses, do I just have to change my margin of error or is the whole research not statistical anymore? I'm confused.

Thank you in advance.

Good day,

I am currently writing my bachelor thesis which involves quite a bit of research and statistics.

Could anyone please help me understand how having data that is highly skewed and having Outliers/extremes in the data affects the performance of the following two tests:

-shapiro-wilk and kolmogorov smirnov tests of normality

-levene's test for assessing homogeneity of variance for 2+ groups.

Would removing the outliers/extremes and using logarithmic transformation on the data before these tests make them more accurate/inaccurate? Or would logarithmic transformation increase the chance of Type 1 error?

Throughout my previous statistical courses, 99% of the time the problems were extremely simple and we could always assume that the data is normally distributed. When using t-test, we could simply use welch test with df when the variance could not be assumed equal. However, now that I am in a real world situation it is quite different. My analysis focus is on analyzing the current performance situation across multiple entities of the same organization and detecting where the biggest differences are, how big is the difference (CI for difference between means) and why (factors)/

For this of course, I need to look into non-parametric alternatives for ANOVA and t-test.

For ANOVA I have found that Kruskal-Walis test is the most used non-paremtric alternative to ANOVA. For post-hoc Dunn Bonferroni test, Mann-whitney U, games-howell's. - Mann-whitney U I can also use as a non-parametric t-test.

Looking forward to all the responses and suggestions.

Best regards,

Janis Frisfelds

The research group OASYS (Optimization and Analytics for Sustainable EnergY Systems) at the University of Málaga (Málaga, Spain) is currently looking for talented and entrepreneurial candidates to fill a postdoctoral position on the topic "Data-driven optimization for decision making in energy."

The Postdoctoral Researcher is expected to possess a PhD preferably in mathematics (operations research, statistics, mathematical programming), control or electrical engineering.

The appointments involve a competitive salary (commensurate with qualifications), healthcare and social security insurance.

Detailed information on the position we are offering and on the application procedure can be found at https://sites.google.com/view/groupoasys/open-positions?authuser=0

Applications should be received no later than December 2, 2018.

Which test should be run for determining statistical differences between 2 (r values) correlations (correlated correlations)?

I am looking to see if there are any differences between a placebo and an intervention in a pre-post crossover study design.

I have read that a Steiger's Z test should be used, however my samples are not independent.

Any help would be appreciated.

I am currently working on a project related to retention of bedside nurses. I would like to know if anyone has statistical data that explains the experience level of icu nurses in the US.

Hi,

I have used MANOVA to examine a hypotheses. However, I am not not very confident about its interpretation.

Any help from you guys is highly appreciated.

hi, so i'm doing my final year project for psychology but ive never learnt research statistics. my professor isnt really helping either. anyway, im investigating the interaction between gender of participants + gender of victims + country of participants and the level of tolerance for domestic violence. my question is, do i use 3 way anova? or is it mixed? or repeated measures? i googled and saw these terms a lot but not sure which is suitable for my research. please help!

I want to conduct research of 5 malnourished children (stunting, wasting and underweight)?

Can anybody help me how I will calculate mean absoulte error(MAE) in pose estimation. I know it is error between ground truth pose and predicted pose but how i will caluclate this. Thanks

I am researching for statistical theory of reliability and life testing.

I have a question about the use of publication bias modeling approaches in meta-analyses of proportions.

The traditional approaches of assessing publication bias, such as the rank correlation test, Egger’s regression model, and weight function approaches have all assumed that the likelihood of a study getting published depends on its sample size and statistical significance (Coburn and Vevea, 2015). Although it has been confirmed by empirical research that statistical significance plays a dominant role in publication (Preston et al., 2004), this is not entirely the case. Cooper et al. (1997) have demonstrated that the decision as to whether to publish a study is influenced by a variety of criteria created by journal editors regardless of methodological quality and significance, including but not limited to, the source of funding for research, social preferences at the time when research is conducted, etc. Obviously,the traditional methods fail to capture the full complexity of the selection process.

In practice, authors of meta-analyses of proportions have employed these methods in an attempt to detect publication bias. But, studies included in meta-analyses of proportions are non-comparative, thus there are no “negative” or “undesirable” results or study characteristics like significant levels that may have biased publications (Maulik et al., 2011).

Therefore, in my opinion, these traditional methods may not be able to fully explain the asymmetric distribution of effect sizes on funnel plots. It is also possible that they may fail to identify publication bias in meta-analyses of proportions in that publication bias in non-comparative studies may arise for reasons other than significance.

I'm not sure if my reasoning is correct. Can someone shed some light on this? If someone could point me to some papers regarding this topic, that'd be wonderful.

References: Coburn, K. M., & Vevea, J. L. (2015). Publication bias as a function of study characteristics. Psychological methods, 20(3), 310.

Cooper, H., DeNeve, K., & Charlton, K. (1997). Finding the missing science: The fate of studies submitted for review by a human subjects committee. Psychological Methods, 2(4), 447.

Preston, C., Ashby, D., & Smyth, R. (2004). Adjusting for publication bias: modelling the selection process. Journal of Evaluation in Clinical Practice, 10(2), 313-322.

Maulik, P. K., Mascarenhas, M. N., Mathers, C. D., Dua, T., & Saxena, S. (2011). Prevalence of intellectual disability: a meta-analysis of population-based studies. Research in developmental disabilities, 32(2), 419-436.

Is it disjoint, as in the US, or coordinated, as in the case of Statistics Canada, or the Italian National Institute of Statistics? What advantages and/or disadvantages do you see?

Need a list of research design and best statistics for analysis.

Answers inform of e books.Text or reference are welcomed.

Hello there,

I would appreciate if you could provide some tips on how to use lsmeans to make interaction plots in R.

Thanks alot

The manuals I have seen are a bit frustrating. They are either too simple, or too complex. Is there a manual that has a strong intro to R, good explanation of multivariate stats, and good intro to graphics? Should I look for separate manuals then?

I want to study climatic and hydroloic variables, I need to find out the specific time duration of the trend shown as the normalized forward and backward sequences provide that. I am not sure about the procedure, How to conduct it.

Two-way ANOVA on a dataset (codes and results given below) showed no interaction. However, the post hoc output from lsmean package showed a compact letter display which sounds like there is an interaction. The compact letter display for "treatment 2" at "week1" is "c" whereas it is "ab" at week2 and week3, respectively. Does this show an interaction (the effect of treatment changes with change in week)or am I misinterpreting the result? The codes given below.

library(lsmeans)

read.table(textConnection("time treatment effect

week1 1 664

week1 1 617

week1 1 647

week1 2 732

week1 2 819

week1 2 843

week1 3 850

week1 3 670

week1 3 722

week2 1 561

week2 1 581

week2 1 586

week2 2 597

week2 2 669

week2 2 654

week2 3 747

week2 3 708

week2 3 705

week3 1 630

week3 1 630

week3 1 664

week3 2 576

week3 2 666

week3 2 716

week3 3 776

week3 3 773

week3 3 726

"),header=T)->dat1

dat1$treatment<-as.factor(dat1$treatment)

aov(effect~time*treatment,data=dat1)->m1

summary(m1)

tk <- lsmeans(m1, pairwise ~ treatment*time,adjust="tukey",sort=F)

cld(tk,Letters=letters, sort=F)

SPSS is an important research statistic tool,some students would like to practice in poor resource setting ,any free online study packages for beginners.

I've calculated a value for user satisfaction (via CSI - Customer Satisfaction Index) and its result is expressed in percentage [%]. My calculated value (for Customer Satisfaction Index) expresses, that the general satisfaction of users of any intern system is 76 [%]. Now I am asking myself, if there is any scale or lookup table, which "compares"/"expresses" this value in a more appropriate way. I wouldn't like to leave my result just as a standalone value. I would like to moreover express my result in words and give detailled information on what it generally means (e. g. for improving). Is there any scientifically proven way to do this?

Please guide me if one can use the combination of both PLS and CB (AMOS). Like in my case, I am using the items of the instrument developed and tested in other cultures/ countries. My first objective is to confirm the items and their relevance to our culture. Here I wish to run EFA in SPSS and CFA in AMOS.My second objective is to explore the relationship between these variable through a measurement model. Here I intend to run the model in Smart PLS.

Please guide me on this also share relevant literature if possible.

I've searched Google and the question database of RG, but couldn't find an answer. Hope you can help.

In a study I use four IV's (linked to product attributes - within individuals - level 1) and several level 2 variables (to measure characteristics between individuals).

I use SPSS 'mixed > linear', following Heck et al.'s book on multilevel and longitudinal modeling with SPSS (2014). I aim to analyse how the IV's (level 1) predict the DV and how level 2 variables moderate the IV-DV main effects.

My question is twofold.

First, when trying to examine variability in intercepts across individuals (the relationship between the IV's and the DV), do you need to build a model only with the IV's as fixed-effects parameters? Or should you run a

*full*model (with IV's, control variables and interactions) and then look at the parameter coefficients and significance of the IV's? This confuses me, also because of the difference between both models in IV coefficients and significance levels.Second, when trying to explain variability in an IV-DV slope across individuals (so introducing cross-level interactions to see whether a level 2 variable moderates the IV-DV main effect), you need to specify the level1-DV slope as a randomly varying parameter in the model. In my case, the IV1-DV, IV2-DV (etc.) slopes are the randomly varying parameters. My question is, do I need to add all IV's (so all slopes) to the model? Or do I need to model the cross-level interactions on each slope, one at a time? To be more specific, the syntax in SPSS may look like this in both options:

*WIth only the IV1-DV as a random slope (see bottom line):*

MIXED DV2 WITH IV1 IV2 IV3 IV4 ctrl1 ctrl2 variablelvl2_1 variablelvl2_2

/CRITERIA=CIN(95) MXITER(100) MXSTEP(10) SCORING(1) SINGULAR(0.000000000001) HCONVERGE(0,

ABSOLUTE) LCONVERGE(0, ABSOLUTE) PCONVERGE(0.000001, ABSOLUTE)

/FIXED=IV1 IV2 IV3 IV4 ctrl1 ctrl2 variablelvl2_1 variablelvl2_2 variablelvl2_1*IV1

variablelvl2_2*IV1 | SSTYPE(3)

/METHOD=REML

/PRINT=G SOLUTION TESTCOV

/RANDOM=INTERCEPT IV1 | SUBJECT(IndividualNr) COVTYPE(VC).

*WIth all four IV-DV slopes as a random slope (see bottom line):*

MIXED DV2 WITH IV1 IV2 IV3 IV4 ctrl1 ctrl2 variablelvl2_1 variablelvl2_2

/CRITERIA=CIN(95) MXITER(100) MXSTEP(10) SCORING(1) SINGULAR(0.000000000001) HCONVERGE(0,

ABSOLUTE) LCONVERGE(0, ABSOLUTE) PCONVERGE(0.000001, ABSOLUTE)

/FIXED=IV1 IV2 IV3 IV4 ctrl1 ctrl2 variablelvl2_1 variablelvl2_2 variablelvl2_1*IV1

variablelvl2_2*IV1 | SSTYPE(3)

/METHOD=REML

/PRINT=G SOLUTION TESTCOV

/RANDOM=INTERCEPT IV1 IV2 IV3 IV4 | SUBJECT(IndividualNr) COVTYPE(VC).

Thank you very much in advance.

I'm doing a Systematic Review of Quantitative research. The research topic is very limited so to have any studies to compare the homogeneity is lost needed to do a meta-analysis (varying populations, different tools used & different factors measured. Therefore performing a meta-analysis is discounted.

My lecturers are most comfortable doing Qualitative Research and so have suggested doing a thematic analysis to discuss the findings, but this does not sit right with my theoretical perspective and use of strictly quantitative data, and seems like making it fit rather than sit naturally. Why can't I just do a Quantitative data analysis that is Quantative, not pushed into Qualitative methods.

Is there an alternative to Meta-Analysis, for example as (Quantitative Research Synthesis, Q. Systematic Review, Q Data synthesis, or Q Data Analysis) as my data analysis method akin to Thematic Analysis but for Quantitative Research. Can't find any literature to suggest an alternative method for Quantitative Evaluation apart from Meta-Analysis, and I can't find evidence that suggests Thematic Analysis can work for Quantitative even if I wanted it do.

In summary, is a Quantitative Research Synthesis (or other term) a viable data analysis method? Or do I have to do thematic analysis if meta-analysis is not possible?

Is it 'scientifically rigorous' to study the entire population instead of selecting a sample in a study?

Hello,

I have a feature set of 195 features and 17 classes. Is there any way to have graphical representation of my data? Don't want to use PCA space

I am completing my dissertation on the attitudes and beliefs of teachers to students with disabilities. My chair suggested the use of the survey however the program director is challenging the use of survey,

Dear All,

I would like to request you to highlight me how to work with longitudinal data in R. Sharing any relevant resources (book, articles, tutorials and youtube links etc) would be highly appreciated.

Sincerely

Sadik

I don't know, how to understand score of ICIQLUTSqol? What does it mean that patient has 52 scores in this questionnaire? Can you help me? :)

CMV has been relabeled common source bias and similar three-letter "invocations" continues to crowd out reasonable conversations. Analyses using single datasets are being used to make ambitious generalizations -- the importance of theoretical arguments now seems to be in the background.

Paul Spector in a 2006 piece in ORM had articulated a balanced perspective on this issue. What are you seeing in your disciplines -- thoughtful balance or labeling and quixotic quests?

I have over 5 years developing predictive models with years of experience as a researcher, statistical analyst as well as data scientist. One aspect that I have experienced within the big data sphere as well as predictive modeling landscape is that a lot of emphasis are either place on data quality and quantity, the experience and expertise of the modeler, the kind of system that is being used to build the model, validate, test, and continue to monitor and assess the model quality and performance over time. With this said, I would like to see what others here on Research Gate think are some of the challenging task building either a statistical or predictive models and what were some of the strategies you employed to help address those challenges? What were some of the tradeoffs you had to make and how would you approach similar situation in the future?

Information provided to this inquiry will be used for personal and professional growth.

I premise I'm not expert in statistics. I have two indipendent random variables, both Weibull distributed with known shape and location. How can I obtain the distribution (or an approximation) of the sum of the two random variables?Any references? Can MonteCarlo simulation be useful to obtain empirically the distribution of the sum?

1) What is the nature of the relationship between EO and the level of revenues collected from SE strategies within the NPO?

2) What is the nature of the relationship between the level of current funding and the ability to meet the organizational mission?

3) What is the relationship between the level of revenues collected from SE strategies and the levels of revenues collected from fundraising?

4) What is the nature of the relationship between education and/or business experience to the level of EO within the organization

Hello everyone! I am conducting a Social Life Cycle Assessment (S-LCA) on fresh white asparagus from Peru. The goal of the study is to identify and assess social impacts (positive or negative) along the life cycle of the product, using the S-LCA methodology proposed by UNEP (http://www.unep.org/pdf/DTIE_PDFS/DTIx1164xPA-guidelines_sLCA.pdf).

This methodology defines 5 stakeholder groups (workers, society, local community, suppliers and consumers), which have themes of interest or subcategories (i.e. The subcategories “Child labour” or “Fair salary” for the stakeholder “Workers”). In order to collect the data, indicators for each subcategory have to be designed. Currently I have initiated the data collection phase and I am having some trouble trying to define an acceptable number of elements to interview. I am only considering to interview elements from the stakeholders “Workers”, “Local Community” and “Suppliers”, being the sample frames: 250 (workers), 300,000 (inhabitants) and 20 (suppliers). Since I consider this to be a qualitative study, I think the best option is to use saturation for determining sample sizes. However, is this method enough in order to make inferences?

Thank you very much!

In multiple regression on SPSS or Mplus I have two predictors

1. Years in education

2. Years of working experience

Based on those two I am predicting annual income.

I have two coefficients for predictors (both significant). So I report that one of those two is a significant predictor of annual income while controlling for the other predictor. But what exactly does this mean - "CONTROLLING FOR".

Thank you for answers

One of the reviewers questioned the rationale of 3-way interaction in my article.

In the article I predicted that reporting one’s gender before some test completion and having an opposite gender test administrator would activate stereotype threat for women and that women would perform as well as men only in a condition when women would report gender after testing and they would be paired with a woman experimenter.

I run a 2 × 2 × 2 ANOVA with participant’s gender (man vs. woman), experimenter’s gender (man vs. woman), and location of the gender question (before the test vs. after the test) as between-participants factors.

Results of my study confirm my assumptions (significant 3-way interaction): men performed always better than women with only one exception, where the group of women who reported gender after testing and were paired with a woman experimenter outperformed the group of men assigned to the same condition. Among men, there were no statistically significant differences in scores across all conditions; whereas women achieved the best in the condition where they reported their gender after the test and were paired with women experimenters respect to all the others; no differences emerged between the other women groups).

I got also one significant main effect (participant’s gender: women achieved lower scores than men) and one 2-way interaction (participant’s gender x location of the gender question: when gender was reported before testing women yielded lower scores than men, whereas they perform exactly as men when gender was reported after testing). 2-way interaction participant’s gender x experimenter’s gender was insignificant.

I thought that my prediction that women would perform as well as men only in one condition and that in three other conditions women will underperform men will fully justify 3-way interaction. However, for my reviewers this rationale isn't sufficient. I'm not sure how else could I justify this 3-way interaction or why my current rationale isn't enough.

I would be very grateful for any help!

i have performed placket butman experimental design. But i am unable to calculate p value and confidance level. So can any one give me formula to calualte these values?

Hello,

I need urgent help to calculate absolute values of standard deviations from surface & volume calculations of cylindrical shapes. The tricky part is to calculate according to the law of error propagation:

The 2 formulas are the following:

**lenght of mantle m = root[(R-r)**

^{2}+ h^{2}]**mantle surface M = (R+r)* Pi* m**

measurement values are: r=0,89 R=1,43 h=27, as well as Dr=0,79 DR=0,08 Dh=0,5

Can somebody help me out with the exact formula for the standard deviation of the measures?

Thanks for help! Verena Hoelzer

I am investigating impacts of Climate change on water resource in Jordan using statistical downscaling models.How I can downscale and project data from CMIP5 or CORDEX. any related studies or experiments.

I am also looking for code (Matlab, R, Python) to implement morphological filtering of time series.

I want to associate patient perception of hospital quality with staff perception of hospital quality. Two different surveys were used for the 2 different population. The only common thing is that they were collected from the same hospitals.

I believe that its not possible to do correlation or regression ,but are there any other statistical methods?

Because of the non-normality of the distribution of the items I'm using to measure a latent variable, I decided to use the ULS - Unweighted Least Squares method for a CFA.

AMOS doesn't produce the RMSEA index, but an editor pretends it to me.

I found an equation to calculate it based on Chi Square, DF and Numerosity of the sample (see the file in attachment).

My question is: is it correct or it doesn't work with ULS?

Any other solution?

Thank you

Diego

For example, we have germinated several genotypes of Arabidopsis seeds. From that we get only single number (germination rate in %) per genotype. How can one determine, whether the difference is statistically significant? Shall we repeat several times it after some time and make some average from that? Or calculate the germination rate for each pot separately?

Sample (adjusted): 1/08/2002 1/16/2014

Included observations: 3138 after adjustments

Trend assumption: Linear deterministic trend

Series: KS NK SS BS

Lags interval (in first differences): 1 to 4

Unrestricted Cointegration Rank Test (Trace)

Hypothesized Trace 0.05

No. of CE(s) Eigenvalue Statistic Critical Value Prob.**

None * 0.187103 2258.186 47.85613 1.0000

At most 1 * 0.174294 1608.145 29.79707 1.0000

At most 2 * 0.156655 1007.168 15.49471 0.0001

At most 3 * 0.139790 472.5168 3.841466 0.0000

Trace test indicates 4 cointegrating eqn(s) at the 0.05 level

* denotes rejection of the hypothesis at the 0.05 level

**MacKinnon-Haug-Michelis (1999) p-values

Dear researchers

I have a random variable X that is following a bimodal distribution, when I squared each element in that variable X, I got a distribution shape similar to exponential or Chi-squared, Could you please advise me any reference or explanation on how to prove what's exactly the distribution in such case?

Best regards

Any good suggested links, books etc. for handling interactions for repeated measures data (mixed model: 3 time points, continuous outcomes, categorical and continuous predictors). Working in STATA - xtmixed.

I have a dataset of quantitative variables, and I want to study their relationship, considering one of them as dependent, and the others as independent. An examination of scatter plots, I can see that this relationship is not linear. Which approach can I use for my analysis?

Hello,

I am trying to analyses my questionnaire. It includes three components; students' experience which have 15 items, students' feeling which have 10 items, students' background which have 10 items. All these items measuring by 4 likert scale. My independent variables are; gender and students status.

I want to run Anova with my data set but I couldn’t figure out how to treat lukert scale in SPSS, and do I have to consider all these items as dependent variables?

Thank you

Tameem

Uncertainty in horological modelling is quantified by MCS. MCS results are generally presented in the form of CDF and histograms. How histograms/CDF/Quantiles reflect uncertainty?

Increased value of invertebrates - Mediating variable

There is a large amount of literature in financial statistics on sums of a continuous random variable where the number of occurrences in the sum also is a random variable. A typical application in finance involves modeling the sum of financial losses per quarter in a bank. Both the size of the loss and the number of losses are random variables, so the cumulative distribution of the sum involves an infinite sum. Standard methods for approximating this distribution include recursion (e.g., Panjer 1981) and simulation (e.g., a Bayesian approach with simulation from the joint posterior distribution). I'm interested in applying this kind of model to certain psychological research problems, but I'm wondering whether anyone has come across such applications in psychology.

If I want to estimate the uncertainties of the data (theoretical with experimental). Which is the best way to find the uncertainties of those data?

I have r-project with meta and metafor packages.

I know how to perform meta-analysis of mean differences but I need to report also the meta-analyzed means (and sd) of the two comparison groups.

Doeas anyone know how to do this?

I am studying seasonal changes in abundance of a fish species along a disturbance gradient. I sampled three locations at four seasons. My sampling sites at each location were very heterogeneous and the overall data was overdispersed . I am planning to analyze data using a GLMM with zero inflated model, considering LOCATION as a fixed factor and sampling site as a random factor. Should I also consider SEASON as a random factor (due to probable autocorrelation) or just nest it within LOCATION?

Dear researchers,

This is a silly question but I have got to ask anyway.

How would you parameterise an asymmetric peak? Would it be meaningless if I were to fit it with multiple Gaussians? Any suggestions on how I should extract my data?

I have no experience

Thank you.

Propensity matching score technique and its application in research methodology is still unknown for many researchers.

I am using proc glimmix in SAS to fit a multilevel model for a multinomial outcome with unordered response categories. Proc glimmix requires that you specify the group= option in the random statement to obtain random effects for each outcome group. However, I suspect this prevents that correlations between random effects of different outcome groups are computed, even if the type=un option is specified.

Is there any way within the glimmix procedure to circumvent this issue? Thanks in advance!

Have been given the former for a client in a psych review in a RAVLT table.

I want to compare effect of three different treatments on animal weight for 14 weeks. What statistical test I can use to compare the effect of treatment with each other.

..even if our design is ex-post facto, non-experimental?

I have observed in many papers where researchers make recommendations of intervention (e.g. increase salary of employees to improve their performance) on the basis of strong positive relationships arising from non-experiments.

I am trying to find an affordable minimum number of questionnaires that I need to be filled by tenants of social housing buildings in order to get reliable and representative results. The questionnaire is about tenants perception and their degree of interest of being part of the renovation project.

I am Professor Emeritus of Psychiatry I have interest in studying why some people are very interested in tracking down biological parents or other family members that they have not known and others who could do so and are not interested.

I have conceptualized a survey research project to examine the variables.I would like to collaborate with a research statistician who could work with me to design the form to gather the data and analyze it. I don't anticipate receiving a grant for this research but whomever I work with would be a second or third author ( if we had two additional researchers ) I would hope we could publish the results and present at appropriate meetings. I believe the results could be interesting and valuable. There is no funding - Contact me if you are interested and we could talk further

Michael Blumenfield, M.D.

My questionnaire composed of 49 items. none of them are normally distributed. Is it correct to do a CFA or EFA with non- normal data?

I found that item-trait interaction statistics were bonferroni adjusted in several recent Rasch studies. This would influence my fit analysis considerably. Is there any reason to adjust and is this an accepted procedure?

I used 5 point Likert scale Never,Rarely,Sometimes,Often,Very Often,in order to measure the women's harassment experience,.what is good analysis by using these category,Never is saying have not experienced at all, whereas rest of all are showing experience but its frequency may vary,

Structural equation modeling is extension from SPSS Software and most of the study using this method for primary data. Anyone had experience applied it for secondary data? What the difference with other software?

I am trying to find out whether there is any work published where the researchers have, after data collection, recoded attitudinal Likert scale data.

So for example if one had a 6 point Agree/Disagree Likert scale with an additional 'Don't Know option' could they recode the scale so that it became a 7 point scale with 'don't know' being recoded as a neutral midpoint?

I have been told that this has been done, but am unable to find any literature on it.