Questions related to Stratification
I am studying thermal stratification effects on a pipeline using CFX (ANSYS) and then compute the thermal stresses using transient structural analysis. This is what I plan to do:
1) Apply an arbitrary force on the pipeline (IN STATIC STRUCTURAL)to compute stresses and continue reducing the element size till my solution becomes grid independent.
2) Compute results using CFX
3) Compute thermal stresses with structural analysis.
As I am involved with two different domains of ANSYS, do I need to carry out mesh independence for these two domains separately or just doing it in STATIC STRUCTURAL(point No. 1) will do the job for CFX as well?
I am trying to develop a morphophysiological dormancy breaking protocol for some forest tree species. Which treatment is better to break dormancy viz. Stratification of seeds for a long duration (months/ Years) or Move along experiment?
“Women are in back seated in their family in south Asian countries due to extreme gender stratification and men take the opportunity to control them using their male dominant rural culture. Rural men enjoy their muscle power over their women to prove their supremacy. Violence against women (VAW) starts from the family and women are in silence in most of the cases to avoid further familial disharmony as they have no place to go without any earning opportunity. Empowerment can inspire them more to find their own identity to protect themselves by raising their voices against the violence”.
I am performing a systematic review of the literature and the data is stored as a table including the fields:
DISEASE, VIRUS, TISSUE, ASSAY.
I made a random effect model for each of the first three fields (that is: test the OR for disease x upon infection with virus y on z tissue). But further stratification by assay would be (a) cumbersome (too many groups) and (b) diluting (too few articles into each group).
Is there a statistical way to account for possible bias due to the assay?
Systematic reviews and meta-analyses of diagnostic test accuracy usually pool sensitivity and specificity estimates. However, for clinical/patient-care purposes, positive and negative predictive values (PPV and NPV) are arguably more useful.
Is including PPV and NPV as meta-analytic pooling outcomes sensible? One potential argument against this that I can think of is that these measures are influenced by disease prevalence in the studies that report them (unlike sensitivity and specificity). However, a potential counter-argument to that is some sort of stratification by pre-specified prevalence ranges can be performed.
Five environmental variables were used as predictors in MaxEnt: sst, sss, thermocline depth, stratification index, and distance to shelf edge. Where we can download these from satellite data freely?
I'm currently working with event history data studying how long after implementation countries abolish certain policies. Regarding the policies I also have an index on how far the countries went with their policies ranging from 0 to 100.
I wanted to control for this, in order be able to control for their point of departure. However the coefficient violates the proportionality assumption.
Can I stratify for the continuous variable of that index? I understand it so, that this would allow every country to have a different baseline hazard with respect to their point of departure. Playing around with the data this didn't produce an error.
Could anyone tell me if I can trust these results or if I have to categorize the variable first?
To analyze the thermal stratification of solar hot water storage tanks using numerical simulations, is it possible to apply the turbulence model? What are the parameters of using the model?
There exists an inter-individual response to dietary intervention due to which a sub population may benefit more than others. This underlying variability can be attributed to genetics, age, gender, lifestyle, environmental exposure, gut microbiome, epigenetics, metabolism nutrition derived from diet, and foods. The inter-individual variability to treatments and nutritional recommendations is largely reflected in biomarker values.
How can we screen approproately to stratify patients prior to the study?
Hi. I am trying to help provide some rough advice for selecting amongst alternative sampling designs to measure bird density or abundance. The surveys are short-term in duration such as for impact assessments and habitat association studies. In this instance, I am looking at rough guidelines for how to select where to sample (survey design) and not how to sample (survey method). Attached is a rough first draft of a decision tree (see attachment) to select between various forms of study design ranging from conducting a census to spatially balanced sampling & stratified random designs. Do you have thoughts and suggestions? Is neglecting to include simple random sampling a fatal flaw for example? If so, where would you place it? Of course, the main advice will be to consult with a biostatistician, but hopefully this (with come accompanying text and references) can provide some rough guideance and be a starting point for that conversation. Literature, debate and suggestions welcome and appreciated.
We collected data on the natural regeneration of Balanites aegyptiaca by using stratified sampling design with three levels of stratification, so which model can be better for data analysis?
It is a well- known result of population genetics that admixture, heterogeneity, or stratification in a population can make it impossible to draw valid conclusions from a conventional case-control study, since these conditions (”population structure”) can give rise to substantial association even for unlinked loci.
I have a dataset with several independent variables (IV) (possible predictors: AFADR, FAI, ALT, BMI) and an outcome variable (OV: INS). In a hierarchical regression model BMI results as a confounder of AFADR. Therefore I stratified for BMI and then interpreted the real odds ratio of AFADR for the two different stratified groups (BMI < 25; BMI > 25). However, the BMI is also a predictor for INS. But how can I interpret the real odds ratio of the BMI? Because when analyzing it together with AFADR in one model, both IVs influence each other.
Thanks for any answers.
Sample size calculations for my survey yield n = 385. If I proceed to geographical stratification (5 regions), do I need to mutiply 385 by 5 strata? I was told so where I study but could not find a line / reference supporting this, on the contrary (like James R Knaub wrote 1 year ago « If you stratify you will likely be more efficient - i.e., need a smaller overall sample size - but remember that inference for individual strata will not be as good as the overall level» https://www.researchgate.net/post/My_total_sample_size_is_1068_Do_I_need_to_distribute_more_than_1068_questionnaire_or_distribute_exactly_1068
remote sensing for water layers temperature evaluation, thermal stratification
I am working on thermal stratification of dam reservoir. Could you please help me about choosing the right turbulence model?
Our paper published in the Journal of Clinical Epidemiology clearly outlines why doing this is not just inaccurate but actually wrong because it introduces bias. This is regularly done and advocated by Cochrane . I think this erroneous practice should now stop - see link to the paper 
 Higgins JPT, Altman DG, Gøtzsche PC, J€uni P, Moher D, Oxman AD, et al. The Cochrane collaboration’s tool for assessing risk of bias in randomised trials. BMJ 2011;343:d5928.
 Stone J, Gurunathan U, Glass K, Munn Z, Tugwell P, Doi SAR. Stratification by quality induced selection bias in a meta-analysis of clinical trials. J Clin Epidemiol. 2018 Nov 17. pii: S0895-4356(18)30744-3.
Researchers in sociolinguistic variation sressed on the importance of original regional dialect ORD as the a sentimental social factor when considering sampling and data collection. This importance stems from the fact that samples in any given study should reflect the real stratification of the speech community under study. In other words, samples should be stratified and hence be pure when things come to dialects spoken in that community. And that purification will ONLY be attained through taking this factor into consideration. I am in the process of conducting a study where I will argue that though ORD is very important, it doesn't guarantee the purification required. Instead, another yet more important factor is what researchers MUST take care of besides the ORD factor. That factor is the amount of contact. To sum up, ORD alone is not enough. It should be twined and/or followed by determining the amount of contact for the sample.
I am trying to see the effect of certain covariates on the 5 year-Overall survival of a cohort of patients that were studied for second primary malignancies. However, when I test the proportional Hazard assumptions (Schoenfeld) this is what I get.
I have read that two options following this violation is stratification or two add the time dependence. However I am not familiar with this.
Thanks in advance for any help that can be provided
I am studying climate change impact on thermal stratification of Sabalan dam reservoir, Ardabil, Iran. I'm not sure which model or scenario to use. As you know there are a wide variety of models to employ for this purpose. Is there like any way that I can figure out which model is more accurate to predict climate change in this region.
I have a landuse classification and want to do an external validation of it (which is not based on the training data).
Because 1 of my 3 classes is really rare, I cannot safely implement a random sample for the validation.
How do I pick a number for the sample of a stratified validation? Does a stratified validation need to be proportional (between the strata), or can I use the same sample size for all stratas?
As my rare class is also my most important class, I do not want to under-represent it, but I am also afraid to create some bias without being conscious of it.
And I cannot find literature that gives a clear hint or maybe I just got confused about that topic. Is there any fixed that should be used? Or is there a proportion between all classified pixels and the amount of pixels that have to be validated per class.
I am thankful for all hints or literature advice!
My current research compares the detailed performance of two widely used hydrodynamic models. Our study include more than one thousand simulations by each model to see how the stratification dynamics is influenced by the future climate warming and reservoir management. Could anyone recommend me some journals for submission?
The impact factor should be around 3 to 4. Because my research mainly focused on the water temperature and didn't include other ecological indicators (e.g. nutrients, oxygen and algae), I supposed it may not be appropriate to submit to the enviromental journals (Science of the Total Environment, Journal of Environmental Sciences and so on).
Any suggestions will be highly appreciated! Many thanks!!
I am looking for some insights on this. I am doing an analysis that considering a binary dependent variable (metabolic syndrome) and a continuous independent variable (muscle mass) according to marital status (married/unmarried). My raw data has around 2 thousand observations, however after the marital status stratification married people were around 1700 observations and just 300 belonged to the unmarried group. My statistical analysis showed significant differences. However, can I rely on this results, or the low number of the unmarried group could affect somehow in the statistical outcomes? Thanks in advance.
I am trying to determine the best way to randomize my patients for a study I am conducting. There are 2 experimental groups and a control group. I work in a mental health setting so being aware of their diagnoses is critical as I dont want too many of 1 type of diagnosis in 1 group and another in another giving skewed results. I am trying to determine if the best way to do this is through stratifing my patients or doing a cluster randomization or strictly doing a single randomization and doing an anova on each group to make sure that they are equally balanced. Something else to consider is that I work in a 17-bed unit so my n for each group on a weekly basis will always be small. I plan on conducting my study over the course of 6mos weekly to increase my overall N but want to make sure that I am able to divide up my patients as evenly as possible. Thank you in advance for your thoughts!
I'm thinking that stratification is important. Even if treatment A has the same effect as treatment B, there may be a diffierence in outcome from the time effect. Could those who are given say the intervention in the first part of the study respond differently to those in the second part (accounting for washout)? This being due to a psychological effect of when the intervention was taken.
I'm trying to solve a kind of mystery in the lab... Every time I do a comassie-blue staining of polyacrylamide gels they have two different brackground color intensities separated by a perfect line (attached picture). When I destain the gel, the differences dissapear and the bands in the less stained part seem to have been stained correctly.
I think it's a kind of stratification of the gel that affects the staining process but I don't know why this is happening.
Do you have any idea?
Thank you in advance!
I am trying to get uniform growth of tomato seeds with more than 90% or 100%germination rate within 4-6 d.a.g for in vitro growth. The germinated tomato seeds should have similar growth rate with the same radical or root length. Currently I am using 20 mins sterilization in 4% bleach and 10 mins in 20mM HCl, rinse with sterile water several times. However, some genotype gave 100% germination rate and some with less than 50% germination rate after 4- 6 days. With more than 100 genotypes what is the best way to get uniform growth with 100% germination rate for every genotype?
I have recorded N, Mean, SD of a variable (X) from N studies. The size of ni varies from 4 to 784. The mean ranges from 22 to 175 and SD ranges from 0.7 to 134.9. With stratification over ni. I see that X follows normal distribution. But the distribution of SD and CV are not following Normal distribution. Both, SD and CV are positively skewed.
Now I want to have a good estimate the mean of that variable under above circumstances. Also I wish to estimate the SD of new estimate.
A variable can be characterised by Mean and SD when it is normally distributed.
In my case, the mean of means from several studies follows Normal distribution but the SD from those studies do not follow normal distribution.
How to handle this situation when my interest is to have combined estimate of mean and SD of variable? If weighted mean is required then How?
It is required to destroy the stratification and create turbulence inside water mixing tank. This is done by inserting water jet inside the tank. How can the axial velocity at the nozzle exit be measured experimentally?
stratification and minimization are two randomization options to achieve balance in terms of covariate baselines in small RCTs. in recent years, researchers seem to have favoured minimization, particularly for sequential allocation designs with a high number of covariates. in short, the method involves the choice of some imbalance criterion and then sequentially allocates each new individual to the study arm leading to the smallest new criterion value. this allocation can be done purely deterministically or involving some element of chance.
our question is if the approach could be simplified when the assignment to study arms takes place only after inclusion of all participants is completed. our idea is to use simple randomization without any constraints to generate a large number, say N = 1000, of complete and fully random allocation schemes. in the next step we would identify the, say, n = 100 schemes with the smallest imbalances (using a similar criterion as for minimization) or, alternatively, all schemes with a criterion value below some prespecified cut-off value. finally, we would choose one of these remaining schemes at random.
the whole process would be carried out by a third person not involved in the study intervention or the collection of study outcomes. the investigators would only receive the last resulting allocation scheme from the person responsible for allocation.
the rationale behind our idea is simplicity and that we would like not to sacrifice too much randomness for balance.
has anyone heard of such an allocation strategy before? what do you think of it? are there any considerations concerning bias? (in some way, we would just be rejecting allocation schemes as long as we don't like them because of intolerably low balance. on the other hand, minimization or even stratification contain similar aspects...) and what about the implications for the statistical analysis? would you still adjust for the covariates using covariance analysis? any other thoughts?
thank you very much for any feedback!
I have got the survey data “World Bank’s Enterprise Survey 2013” in SPSS form. My research objective is to find out the obstacles faced by the firms while doing business in Pakistan. There are 15 obstacles listed below:
1. Electricity to Operations of This Establishment
3. Customs and Trade Regulations
4. Practices of competitors in informal sector
5. Access to Land
6. Crime, Theft and Disorder
7. Access to Finance
8. Tax Rates
9. Tax Administrations
10. Business Licensing and Permits
11. Political Instability
14. Labor Regulations
15. Inadequately Educated Workforce
They are measured on 5-point likert scale (
No Obstacle ,Minor Obstacle , Moderate Obstacle, Major Obstacle , Very Server Obstacle).
Sampling Technique: Disproportionate Stratified Random Sampling
Three level of stratification have been used in the Survey: firm size, business sector, and geographic region within a country. Firm size levels are 5-19 (small), 20-99 (medium), and 100+ employees (large-sized firms). The business sector has been breakdown into manufacturing (Food, Textiles, Garments, Chemicals, Non‐metallic Minerals, Motor Vehicles, Other Manufacturing) and services (Retail and other services). Five regional stratification.
However, I am not interested in particular strata (groups) within the population. What kind of statistical tools can be applied here?
A researcher has developed a new treatment for some medical condition. A clinical trial is being planned, in which 60 subjects are to be enrolled, 30 to each treatment group (treatment and control), therefore, a randomization is needed to minimize bias from various sources.
The trial is multi-center trial, with 4 different sites in which the treatment will be given. A block randomization with stratification by center, yields the same amount of blocks (each block is randomized for treatment and control) for each center, i.e. the same amount of subjects in each center (default of the software). However, some sites are bigger than others, and the sponsor wish to enroll more subjects in sites A and B, and less in C and D.
Randomization need to make sure that baseline characteristics are more or less equal between the two treatment groups, and to ensure that the investigator remains blinded. I wanted to ask you, how to solve the problem of wanting to stratify by center on one hand, but wishing to have more subjects in specific centers due to the fact that they are bigger, enroll faster, etc...Is it legal to give more blocks to some centers? I couldn't find such an example in the FDA guidelines.
For the sake of the example, let's say that I use blocks of 4 subjects, I need 60 overall (30*2), and got 4 centers. How should I do a stratified block randomization without forcing the investigator to enroll an equal number of subject in each site? Can I maintain balance within center and not between centers?
Thank you !
By adding to the title and its anisotropy of this project the non-local or (singular) integral operators = my response to an ask of feedback to this paper, see below (paper put in reference to my own project)= (+ -via google translation, French text below) it is quite interesting = the anisotropy is here xi-cartesian constant (and the study comes out of the 1d to speak of the true multidimensional) and one can imagine more without difficulties The localized varying declinations of this anisotropy with Cartesian bases and ellipsoids/paraboloids and weights and functions, and your localized exponents ai, varying in x all of them in their own way, in centers, angles and Rotations, values etc, for your operators and various weights and functions on which your operators apply, variabilities more or less slow, fast, (ir /) regular, adapted, local, nice or not etc. It is quite speaking when the function is the derivative or the gradient of another or with studies in spaces of besov, sobolev or all functional spaces Es,p,q, with s not zero, s being the index (Integral) of derivation (instead of the spaces with s = 0, Lp, Lq etc) with s, p and p variables in x = we approach geometric anisotropy, foliations and associated irregularities which can eg to account for very natural situations in mathematical physics such as vortex patches and many others. (+-via google traduction, texte francais plus bas) c'est tout a fait interessant = l'anisotropie est ici xi-cartesienne constante (et l'etude sort du 1d pour parler du vrai multidimensionnel) et on peut imaginer de plus sans trop de difficultés, les declinaisons variables localisées de cette anisotropie avec des bases cartesiennes et des ellipsoïdes/paraboloides et des poids et fonctions et vos exposents 'ai', tous localisés, variant en x tous chacun d'eux a sa facon, en centres, angles et en rotations, valeurs etc, pour vos operateurs et poids et fonctions diverses sur lesquels s'appliquent vos operateurs, variabilités plus ou moins lentes, rapides, (ir/)regulieres, adaptées, locales, gentilles ou pas etc. c'est assez parlant quand la fonction est la derivée ou le gradient d'une autre ou avec des etudes dans des espaces de besov, de sobolev ou tous espaces fonctionnels Es,p,q, avec s pas nul, s etant l'indice (integral) de derivation (au lieu des espaces a s=0, Lp, Lq etc) avec donc s, p et p variables en x = on s'approche de l'anisotropie geometrique, les feuilletages et foliations et des irregularités associées qui peuvent eg bien rendre compte de situations tres naturelles en physique mathematique comme les vortex patches et beaucoup d'autres.
(paper and project "Weighted Anisotropic Morrey Spaces Estimates for Anisotropic Maximal Operators" and "weighted anisotropic Morrey spaces...." by Ferit Gürbüz= I pronounce my self on its/their subject and not on the novelty that its author brings to it). the rg-profile of the author has 2 (slightly different) papers with same title, but one of them has a "full text" with the title ending with: "...AND 0 -ORDER ANISOTROPIC PSEUDO-DIFFERENTIAL OPERATORS WITH SMOOTH SYMBOLS" with an additional math paragraph.
What are the ecological interpretations one can work out when herringbone stratification and hummocky cross stratification are observed in carbonate facies?
I was sowing some seeds of bitter almond after stratification for 45 days, Now the seedlings colored with brown , and leaves are small, bent , also the they grow slowly .
Our kidney transplant centre is trying to make a DCD(including DBCD) kidney evaluation formula from the data in a multi-centre retrospective study.Since the retrospective feature, some of the variables is not complete. So some univariate analysis is not seeing statistical differences. I'm afraid this may probably effect the multi-variate analysis in the next step. Should i do stratification to solve the data incomplete problem?or do you have any better method?
Could any expert give suggestions on the formula or the statistical methods?
One MSc student face this phenomenon in field and in embryo culture, I advice him to treat seeds with previous cool stratification, Is it correct advice and why?
I want to study ramicolous liverworts (specifically Lejeuneaceae family) in Peruvian rainforests, considering morphological and anatomical aspects, however, I only have information about reports made by other researchers in vertical stratification on trees in South America. I hope anyone can help me with information and suggestions about this topic.
It seems all conditions in definition of a stratifiable space hold. But the product of 2 copy of Sorgenfrey lines is not normal, countably many products of stratifiable spaces is stratifiable. One has always Stratifiable =>Normal
It's common for marketers insist on a stratified sample design to ensure adequate representation of the relevant segments for analysis, even when it would lead to a rift with random methods. Some insist on resorting to procedures as closely as possible attached to a probabilistic method and, in any case, carry out post-stratification processes if it is requiee. What is your regular survey roll, if any, and what your view about it?
I have a most at risk population (MARP) of about 8,013 with sampling frames as follows.
Strata 1. MSM – 1674
Strata 2: FSW – 1977
Strata 3: BB- 737
Strata 4: DU-3625
Total = 8,013
If I calculate total sample size with 95% CI, 50% assumed proportion, and acceptable difference as 5% and total population size as 8,013. The sample size would come as 367. With 10% refusal It will be 404.
My question is after stratified random sampling in this scenario, Is it possible me to analyse the strata data independently (MSM, FSW, BB, or DU separately) and generalize the values to the strata?
What would you recommend If I want to analyse the groups independently.
are 7 days alot?
1 day is too short?
when should i start to worry that stratification might affect my results?
what are best conditions of cold stratification: distilled water/agar/plates/soil/dry seeds?
The attached file consists of an image which taken from paleogene phosphatic limestones of Pabdeh Formation in Lar mountains (south west of Iran). Nominated layer belongs to middle part of this Formation and based on planktonic foraminiferal studies (Daneshian et al., 2015) estimated Lutetian-Bartonian stage. Field and petrographic studies denote that there are some sedimentary structures such as: Hummocky cross stratification, cross lamination, ripple marks and amalgamation which can be categorized as tempestites. Please, if you find any mistakes in my opinion, could you please correct them?
I'll conduct a study among university students . First I'll stratified them according to knowledge are "theoretical, practical and medical" then randomly chose one or two college from each area. The point is that i need to know the difference between first and fourth year students. So after stratification can i further stratify each group to 1st and 4th year student and to take a group"cluster" from each year???
And in this stuation can i generalize the result among all university students or should i mention that for 1st and 4th year only as they my target group??
Finally i need the help in calculation of such sample , how to calculate design effect and how to determine the size of each cluster??
Thanks in advance,
I am running an opposed jet case, where a stream of fresh reactants is opposed to a stream of burnt products. I am looking at the effects of stratification on the flammability limits of methane.
In some cases, in the reaction zone, the heat release is about 10^3-10^4 lower than compared to my reference case, which is far from the lean or rich flammability limits. Since I am sitting on or close to those limits, it is important for me to define one (or multiple) parameter for which I can say that the fuel is indeed burning.
Here is my reasoning right now, but it might be incomplete or even erroneous. To check if the fuel is indeed burning, I have been looking at the distribution of key radicals. If the mass fraction of say H2, does not have peak in the reaction zone and only diffuses from one stream to another, then I can definitely say there is no flame. But a contrario, if there is a peak, can I definitely say there is a flame? And how big does this peak needs to be compared to its corresponding value in the reactant and products stream? (I am guessing at least one full order of magnitude).
If there is a standard definition of a flame in numerical combustion, please share it, preferably with an attached reference.
Recently, I have been working on a GWAS experiment design. The samples come from the same population, but have extreme phenotypes (classified as 20 cases and 20 controls). When I used Plink (--assoc --adjust) to analyze the data, it reported the genomic inflation factor is about 1.7. I wonder how can there exists population stratification, since the samples come from the same population? How can I solve this "large genomic inflation factor" popular?
Actually the study area has a total population of about 1 million, consisting of seven blocks (community development blocks) and I want to stratify the universe. After stratification, the population remains six lakhs.
This is eqn.(17) in the paper
Zilitinkevich (1972) Bound.-Layer Meteorology 3, 141-145 --see eqn. (12) link attached--
and it is used to estimate the height h_u of the turbulent Ekman boundary layer in unstable stratification from the condition that the main variation of the horizontal mean wind speed (U,V) remains within the height range z0 < z < h_u.
The notation: (U,V) are the wind components, (Ug,Vg) are the geostrophic wind components, c is a constant, k is the von Karman constant, u* is the friction velocity, b is the buoyancy parameter, Q is the kinetic turbulent vertical heat flux, z is the height.
In finite population survey statistics, for survey methodology and data analysis, data are stratified to reduce overall variance. But sometimes publishing the individual categories becomes important, and this may negate the role of reducing overall variance for a given overall sample size. Further, the categories chosen may not be best for stratification purposes. In the case of regression model-based methods, the goal is the same. In that case, scatterplots and estimated regression coefficients with their standard errors can be used to sort out which data should go into which strata. Thus, regression analysis is important for model-based sampling and/or estimation by prediction.
For design-based sampling and estimation, there is Neymann allocation, but my question is not so much about allocating to strata already defined, but more how to define the strata in the first place. There must be some categorical-type heading, but one might do better by being imaginative as to what such data groupings could be tried.
Sometimes better ways to stratify become apparent after-the-fact, and poststratification is used.
What tips/methods do you propose, and/or examples do you have for stratifying either for design-based sampling and estimation methods, or model-based methodology, or model-assisted design-based methods?
I have some data for a travel cost application and I would like to use the variant of the negative binomial model that accounts for endogenous stratification. I know that for STATA there is the gnbstrat package... but I don´t have STATA!
The data analysis of 1H- and 13C-NMR spectra of the tumor extracts demonstrated a significant increase in the concentration of the 2HG in IDH mutated tumors. On 13C-NMR spectra, 2HG signals were detected in the IDH1 mutated but not IDH wild type tumors. It is expected that 2HG may be actively being produced during the period of 13C-substrate infusion (e.g., [U-13C]-glucose).
Hi everyone, I want to ask about proportionate stratified sampling.
This is my sampling frame: strata into 4 (manager,supervisor, engineer, QA&QC executive ) population is 188, manager =63,supervisor=65, engineer 27 and QA&QC=33, 45% proportionate for each strata, then sample should be 85, but due to high non-respond rate, I manage to get only 79 sample , should I change 45% for 85 to 40% for 79(successful respond) ? Or just follow 85, but justify on my writing?
pPease, I need advice,
Thank you in advance;
I have found both to be high suspect it only drops after differentiation is stimulated by media that encourages this (increasing CaCl2 conc.). Any shared experience will be appreciated. Thanks
We are trying to find a source that can easily explain how to calculate and apply post stratification weights for survey data in SAS. Does anyone know of a source with clear explanations and example code? All we could find on the SAS website was conceptual (no examples), and we have not had much luck with any other sources we have found.
Hypertension is the most common medical problem encountered in pregnancy and remains an important cause of maternal and fetal morbidity and mortality. It complicates up to 15% of pregnancies. assessment of arterial stiffness, PWV and some biomarkers may help in prediction and stratification of pregnancy induced hypertension/Pre-eclampsia.
I am testing for statistical interaction between two variables in a logistic regression model and I am finding that from a biologically plausible level the interaction changes. However, I want to back up this finding statistically so I have an additional argument for stratification. Therefore I added a three way interaction as continuous1*continuous2*cut-off continuous2, with p=0.01, so fitting with my observation.
Can anybody tell me whether this is valid and perhaps provide me with some evidence (I am so far unable to find any papers)?
I have some Arabidopsis line with less than a 10 % germination rate. I have tried to increase the germination extending the stratification period (until 2 weeks) and treating with GA. The ratio didn´t increase. I´m now wondering if my seeds have normal embryos or not but I cannot find an easy way to see the embryos (I have mature seeds already). Could anybody help me with this? Maybe an staining?
Thank you very much!