theanalysisfactor.com Report : Visit Site


  • Ranking Alexa Global: # 52,941,Alexa Ranking in United States is # 25,386

    Server:Apache...

    The main IP address: 66.33.205.101,Your server United States,Brea ISP:New Dream Network LLC  TLD:com CountryCode:US

    The description :statistical consulting, resources, and statistics workshops for researchers...

    This report updates in 01-Aug-2018

Created Date:2009-07-16
Changed Date:2018-06-08

Technical data of the theanalysisfactor.com


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Latitude: 33.930221557617
Longitude: -117.88842010498
Country: United States (US)
City: Brea
Region: California
ISP: New Dream Network LLC

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the analysis factor home about our team our privacy policy employment membership statistically speaking membership program programs center login workshops programs center login live online workshops on demand tutorials consulting free webinars contact customer login statistically speaking login programs center login all logins august 2018 member webinar: power analysis and sample size determination using simulation by karen grace-martin this webinar will show you strategies and steps for using simulations to estimate sample size and power. you will learn: a review of basic concepts of statistical power and effect size a simulation-based approach to power analysis an overview of how to implement simulations in various popular software programs. [click to continue…] { 0 comments } what is survival analysis and when can it be used? there are two features of survival models. first is the process of measuring the time in a sample of people, animals, or machines until a specific event occurs. in fact, many people use the term “time to event analysis” or “event history analysis” instead of “survival analysis” to emphasize the broad range of areas where you can apply these techniques. read the full article → the problem with using tests for statistical assumptions every statistical model and hypothesis test has assumptions. and yes, if you’re going to use a statistical test, you need to check whether those assumptions are reasonable to whatever extent you can. some assumptions are easier to check than others. some are so obviously reasonable that you don’t need to do much to check them […] read the full article → using marginal means to explain an interaction to a non-statistical audience you show this table in your powerpoint presentation because you know your audience is expecting some statistics, though they don’t really understand them. you begin by explaining that the constant (_cons) represents the mean bmi of small frame women. you have now lost half of your audience because they have no idea why the constant represents small frame women. by the time you start explaining the interaction you have lost 95% of your audience. read the full article → july 2018 member webinar: logistic regression for count and proportion data in this webinar you will learn what these variables are, introduce the relationships between the poisson, bernoulli, binomial, and normal distributions, and see an example of how to actually set up the data and specify and interpret the logistic model for these kinds of variables. read the full article → life after exploratory factor analysis: estimating internal consistency by christos giannoulis, phd after you are done with the odyssey of exploratory factor analysis (aka a reliable and valid instrument)…you may find yourself at the beginning of a journey rather than the ending. the process of performing exploratory factor analysis usually seeks to answer whether a given set of items form a coherent factor […] read the full article → confirmatory factor analysis: how to measure something we cannot observe or measure directly anytime we want to measure something in science we have to take into account that our measurements contains various kinds of error. that error can be random and/or systematic. so what we want to do in our statistical approach to the data is to isolate the true score in a variable and remove the error. this is really what we’re trying to do using latent variables for measurement. read the full article → three myths and truths about model fit in confirmatory factor analysis by christos giannoulis, phd we mentioned before that we use confirmatory factor analysis to evaluate whether the relationships among the variables are adequately represented by the hypothesized factor structure. the factor structure (relationships between factors and variables) can be based on theoretical justification or previous findings. once we estimate the relationship indicators of those factors, the […] read the full article → four common misconceptions in exploratory factor analysis by christos giannoulis, phd today, i would like to briefly describe four misconceptions that i feel are commonly perceived by novice researchers in exploratory factor analysis: misconception 1: the choice between component and common factor extraction procedures is not so important. in principal component analysis, a set of variables is transformed into a smaller set […] read the full article → june 2018 member webinar: the fundamentals of sample size calculations sample size estimates are one of those data analysis tasks that look straightforward, but once you try to do one, make you want to bang your head against the computer in frustration. or, maybe that’s just me. regardless of how they make you feel, they are super important to do for your study before you collect the data. read the full article → ← previous entries statistically speaking webinar july 2018: logistic regression for count and proportion data upcoming workshops introduction to generalized linear mixed models survival analysis: models for time to event data search read our book data analysis with spss (4th edition) by stephen sweet and karen grace-martin statistical resources by topic analysis of variance and covariance complex surveys & sampling count regression models effect size statistics, power, and sample size calculations linear regression logistic regression missing data mixed and multilevel models principal component analysis and factor analysis r spss stata survival analysis and event history analysis copyright © 2008-2018 the analysis factor . all rights reserved. 877-272-8096 contact us wordpress admin ×

URL analysis for theanalysisfactor.com


http://www.theanalysisfactor.com/employment/
https://www.theanalysisfactor.com/the-analysis-factor-team/
https://www.theanalysisfactor.com/resources/by-topic/mixed-multilevel-models/
http://www.theanalysisfactor.com/customer-login/
https://www.theanalysisfactor.com/resources/by-topic/missing-data/
https://www.theanalysisfactor.com/page/2/
https://www.theanalysisfactor.com/the-problem-with-tests-for-statistical-assumptions/
https://www.theanalysisfactor.com/august-2018-power-analysis-and-sample-size-determination-using-simulation/
https://www.theanalysisfactor.com/what-is-survival-analysis-and-when-can-it-be-used/
https://www.theanalysisfactor.com/resources/by-topic/stata/
https://www.theanalysisfactor.com/resources/by-topic/r/
https://www.theanalysisfactor.com/contact-us/
https://www.theanalysisfactor.com/june-2018-fundamentals-sample-size-calculations/
https://www.theanalysisfactor.com/resources/by-topic/effect-size-statistics-power-and-sample-size-calculations/
https://www.theanalysisfactor.com/resources/by-topic/complex-surveys-sampling/

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Domain Name: THEANALYSISFACTOR.COM
Registry Domain ID: 1562543523_DOMAIN_COM-VRSN
Registrar WHOIS Server: whois.dreamhost.com
Registrar URL: http://www.DreamHost.com
Updated Date: 2018-06-08T15:10:43Z
Creation Date: 2009-07-16T10:54:46Z
Registry Expiry Date: 2020-07-16T10:54:46Z
Registrar: DreamHost, LLC
Registrar IANA ID: 431
Registrar Abuse Contact Email:
Registrar Abuse Contact Phone:
Domain Status: ok https://icann.org/epp#ok
Name Server: NS1.DREAMHOST.COM
Name Server: NS2.DREAMHOST.COM
Name Server: NS3.DREAMHOST.COM
DNSSEC: unsigned
URL of the ICANN Whois Inaccuracy Complaint Form: https://www.icann.org/wicf/
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