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DTSTART;VALUE=DATE:20191014
DTEND;VALUE=DATE:20191019
DTSTAMP:20191017T085944
CREATED:20180515T155943Z
LAST-MODIFIED:20190904T133307Z
UID:3115-1571011200-1571443199@www.psstatistics.com
SUMMARY:Statistical modelling of time-to-event data using survival analysis: an introduction for animal behaviourists\, ecologists and evolutionary biologists (TTED02)
DESCRIPTION:\nCourse Overview:\nSurvival analysis is a set of statistical methods initially designed to analyse data giving the times at which individuals die\, and assess the effect that different predictor variables have on the rate of death. However\, its applications are much broader than this: it can be used to analyse any time-to-event data. Ecologists and evolutionary biologists often encounter data of this kind. Often factors influencing survival itself will be of interest. But there are many other cases\, e.g. what factors influence the time of first breeding? Or the time taken to reach maturity? Animal behaviourists too will encounter this type of data frequently\, e.g. what factors influence the time it takes to learn a novel behaviour pattern? Or the time to respond to a stimulus? etc. And yet the techniques of survival analysis are not generally well known by researchers in these disciplines. \nIn this course\, you will learn how to apply survival analysis models to quantify the effect that predictor variables (continuous or discrete) have on the rate at which events occur\, and how to test hypotheses about these effects. We will focus on a flexible modelling technique called the Cox proportional hazards model\, which makes minimal assumptions about the underlying probability distributions. You will learn how to fit and interpret these models\, how to evaluate its assumptions\, and how to extend it to model time dependent variables\, random effects\, multistate models and competing risks models. \n\n\n\nIntended Audience\nThis course would be suitable for participants who have a good understanding of the basic theory\nunderlying multiple regression/linear models and know how to apply them in R. No previous experience or knowledge of survival analysis is necessary. \nVenue – PS statistics head office\, 53 Morrison Street\, Glasgow\, G5 8LB – Google map\nAvailability – 20 places\nDuration – 5 days\nContact hours – Approx. 28 hours\nECT’s – Equal to 3 ECT’s\nLanguage – English \nPackages\nWe offer COURSE WITH FREE ACCOMMODATION\, COURSE ONLY and ‘ACCOMMODATION PACKAGES;\n• COURSE WITH FREE ACCOMMODATION – includes everything listed below (only eligible for persons in academia)\n• COURSE ONLY – Includes lunch and refreshments.\n• ACCOMMODATION PACKAGE (to be purchased in addition to the course only option) – Includes breakfast\, lunch\, refreshments and welcome dinner Monday evening. Self-catering facilities are available in the accommodation. Accommodation is approximately a 6-minute walk from the PS statistics head office. Accommodation is multiple occupancy (max 3-4 people) single sex en-suite rooms. Arrival Sunday 13th October (between 17:00-21:00) and departure Friday 18th October(accommodation must be vacated by 9am). An additional nights accommodation can be purchased\, departure 9am Friday morning email for details. \nOther payment options are available please email oliverhooker@psstatistics.com \nPLEASE READ – CANCELLATION POLICY: Cancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered\, contact oliverhooker@psstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees (and accommodation fees if booked through PS statistics) will be credited. However\, PS statistics will not be held responsible/liable for any travel fees\, accommodation costs or other expenses incurred to you as a result of the cancellation. Because of this PS statistics strongly recommends any travel and accommodation that is booked by you or your institute is refundable/flexible and to delay booking your travel and accommodation as close the course start date as economical viable. \n\n\n\n \nDr Will Hoppitt\n\n\n \n\n\n\n\n\n— \n\n\nTeaching Format\n\nIntroductory lectures on the concepts and refreshers on R usage. Intermediate-level lectures interspersed with hands-on mini practicals and longer projects. Round-table discussions about the analysis requirements of attendees (option for them to bring their own data). Data sets for computer practicals will be provided by the instructors\, but participants are welcome to bring their own data. \nAssumed quantitative knowledge\nA good understanding of statistical concepts\, statistical significance and hypothesis testing. \nAssumed computer background\nR experience is desirable but not essential. Attendees ideally should be able to import/export data\, understand basic R syntax and write simple functions and loops. \nEquipment and software requirements\nA laptop/personal computer with a working version of R or RStudio. R and RStudio are supported by both PC and MAC and can be downloaded for free by following these links. \nhttps://cran.r-project.org/ \nDownload RStudio \nIt is essential that you come with all necessary software and packages already installed (you will be sent a list of packages prior to the course) as internet access may not always be available. \nUNSURE ABOUT SUITABLILITY THEN PLEASE ASK oliverhooker@psstatistics.com \n\n\n\nCourse Programme\n\nSunday 13th\nMeet at 43 Cook Street\, Glasgow G5 8JN at approx. 17:00 onwards \nMonday 14th – Classes from 09:30 to 17:30 \nModule 1: Statistical modelling of rates and times \nModule 2: Parametric survival models and the Cox model \nTuesday 15th – Classes from 09:30 to 17:30 \nModule 3: Fitting Cox models \nModule 4: Interpreting Cox Models \nWednesday 16th – Classes from 09:30 to 17:30 \nModule 5: Evaluating the proportional hazard assumption \nModule 6: Stratified Cox models \nThursday 17th – Classes from 09:30 to 17:30 \nModule 7: Time dependent variables \nModule 8: Frailty Models and Multistate models \nFriday 18th – Classes from 09:30 to 17:30 \nModule 9: Competing risks models \nModule 10: Open session \n\n\n\n
URL:https://www.psstatistics.com/course/statistical-modelling-of-time-to-event-data-using-survival-analysis-tted02/
LOCATION:53 Morrison Street\, Glasgow\, Scotland\,\, G5 8LB\, United Kingdom
GEO:55.8535874;-4.267977
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BEGIN:VEVENT
DTSTART;VALUE=DATE:20191104
DTEND;VALUE=DATE:20191109
DTSTAMP:20191017T085944
CREATED:20170526T082542Z
LAST-MODIFIED:20190904T132921Z
UID:2830-1572825600-1573257599@www.psstatistics.com
SUMMARY:Behavioural data analysis using maximum likelihood in R (BDML02)
DESCRIPTION:\nCourse Overview:\nThis 5-day course will involve a combination of lectures and practical sessions. Students will learn to build and fit custom models for analysing behavioural data using maximum likelihood techniques in R. This flexible approach allows a researcher to a) use a statistical model that directly represents their hypothesis\, in cases where standard models are not appropriate and b) better understand how standard statistical models (e.g. GLMs) are fitted\, many of which are fitted by maximum likelihood. Students will learn how to deal with binary\, count and continuous data\, including time-to-event data which is commonly encountered in behavioural analysis. \nAfter successfully completing this course students should be able to: \n\nfit a multi-parameter maximum likelihood model in R\nderive likelihood functions for binary\, count and continuous data\ndeal with time-to-event data\nbuild custom models to test specific behavioural hypotheses\nconduct hypothesis tests and construct confidence intervals\nuse Akaike’s information criterion (AIC) and model averaging\nunderstand how maximum likelihood relates to Bayesian techniques\n\n\n\nIntended Audience\nAny researchers (from postgraduate students to senior investigators) interested in analysing behavioural data. Examples will be primarily from non-human animal behaviour studies\, but the methods will also be applicable to many researchers studying human behaviour. The course is intended for those wishing to construct custom statistical models and for those wishing to better understand the workings of standard statistical techniques that use maximum likelihood methods (e.g. GLMs). \nVenue – PS statistics head office\, 53 Cook Street\, Glasgow\, G5 8LB – Google map \nAvailability – 30 places \nDuration – 5 days \nContact hours – Approx. 35 hours \nECT’s – Equal to 3 ECT’s \nLanguage – English \nPackages\nWe offer COURSE WITH FREE ACCOMMODATION\, COURSE ONLY and ‘ACCOMMODATION PACKAGES;\n• COURSE WITH FREE ACCOMMODATION – includes everything listed below (only eligible for persons in academia)\n• COURSE ONLY – Includes lunch and refreshments.\n• ACCOMMODATION PACKAGE (to be purchased in addition to the course only option) – Includes breakfast\, lunch\, refreshments and welcome dinner Monday evening. Self-catering facilities are available in the accommodation. Accommodation is approximately a 6-minute walk from the PS statistics head office. Accommodation is multiple occupancy (max 3-4 people) single sex en-suite rooms. Arrival Sunday 3rd November (after 5pm) and departure Friday 8th November (accommodation must be vacated by 9am). \nOther payment options are available please email oliverhooker@psstatistics.com \nPLEASE READ – CANCELLATION POLICY: Cancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered\, contact oliverhooker@psstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees (and accommodation fees if booked through PR statistics) will be credited. However\, PS statistics will not be held responsible/liable for any travel fees\, accommodation costs or other expenses incurred to you as a result of the cancellation. Because of this PS statistics strongly recommends any travel and accommodation that is booked by you or your institute is refundable/flexible and to delay booking your travel and accommodation as close the course start date as economical viable. \n\n\n\n \nDr. William Hoppitt\n\n\n \n\n\n\n\n\n\nTeaching Format\n\nThere will be a combination of lectures and practicals. Practicals will be based on the topics covered in the preceding lectures. Data sets for computer practicals will be provided by the instructors \nAssumed quantitative knowledge \nA basic understanding of statistical concepts (mean\, variance\, correlation\, regression\, ANOVA etc.) and probability. \nAssumed computer background \nSome familiarity with R. Ability to import/export and manipulate data\, fit basic statistical models & generate simple exploratory and diagnostic plots. \nEquipment and software requirements \nA laptop/personal computer with a working version of R or RStudio. R and RStudio are supported by both PC and MAC and can be downloaded for free by following these links. \nhttps://cran.r-project.org/\nDownload RStudio \nIt is essential that you come with all necessary software and packages already installed (you will be sent a list of packages prior to the course) as internet access may not always be available. \nUNSURE ABOUT SUITABLILITY THEN PLEASE ASK oliverhooker@psstatistics.com \n\n\n\nCourse Programme\n\nSunday 3rd\nMeet at 43 Cook Street\, Glasgow G5 8JN at approx. 17:00 onwards \nMonday 4th – Classes from 09:30 to 17:30 \nModule 1: The process of statistical inference and the role of statistical models. Why learn likelihood techniques? Course outline\nModule 2: Maximum likelihood estimation: single parameter models for binary data \nTuesday 5th – Classes from 09:30 to 17:30 \nModule 3: Models with several parameters for binary data\, optimization algorithms\nModule 4: Testing hypotheses and constructing confidence intervals \nWednesday 6th – Classes from 09:30 to 17:30 \nModule 5: Modelling count data and the Poisson distribution\nModule 6: Modelling continuous data\, the normal distribution and the relationship of maximum likelihood to least squares \nThursday 7th – Classes from 09:30 to 17:30 \nModule 7: Modelling time to event data and the exponential distribution\nModule 8: Akaike’s information criterion (AIC) and model averaging \nFriday 8th – Classes from 09:30 to 16:00 \nModule 9: A brief introduction to Bayesian analysis\, the practical advantages\, and its relationship to maximum likelihood \nAfternoon: Trouble shooting and final summary \n\n\n\n
URL:https://www.psstatistics.com/course/behavioural-data-analysis-using-maximum-likelihood-bdml02/
LOCATION:53 Morrison Street\, Glasgow\, Scotland\,\, G5 8LB\, United Kingdom
GEO:55.8535874;-4.267977
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END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20191104
DTEND;VALUE=DATE:20191109
DTSTAMP:20191017T085944
CREATED:20190424T192351Z
LAST-MODIFIED:20190904T132410Z
UID:3439-1572825600-1573257599@www.psstatistics.com
SUMMARY:Introduction to Bayesian data analysis for social and behavioural sciences using R and Stan (BDRS02)
DESCRIPTION:\nCourse Overview:\nThis course provides a general introduction to Bayesian data analysis using R and the Bayesian probabilistic programming language Stan. We begin with a gentle introduction to all the fundamental principles and concepts of Bayesian data analysis: the likelihood function\, prior distributions\, posterior distributions\, high posterior density intervals\, posterior predictive distributions\, marginal likelihoods\, Bayes factors\, etc. We will do this using some simple probabilistic models that are easy to understand and easy to work with. We then proceed to more practically useful Bayesian analyses\, starting with general linear models\, followed by generalized linear models\, including logistic regression and Poisson regression\, followed by multilevel general and generalized linear models. For these analyses\, we will use real world data sets\, and carry out the analysis with Stan using the brms interface to Stan in R. With each example\, we will explore general concepts such as model checking and improvement using posterior predictive checks\, and model evaluation using cross-validation\, WAIC\, and Bayes factors. In the final part of the course\, we will delve into some more advanced topics: understanding Markov Chain Monte Carlo in depth\, Gaussian process regression\, probabilistic mixture models. \n\n\n\nIntended Audience\nThis course is aimed at anyone who is interested to learn and apply Bayesian data analysis in any area of science\, including the social sciences\, life sciences\, physical sciences. No prior experience or familiarity with Bayesian statistics is required. \nVenue – PS statistics head office\, 53 Morrison Street\, Glasgow\, G5 8LB – Google map \nAvailability – 20 places \nDuration – 5 days \nContact hours – Approx. 28 hours \nECT’s – Equal to 3 ECT’s \nLanguage – English \nPackages\nWe offer COURSE WITH FREE ACCOMMODATION\, COURSE ONLY and ‘ACCOMMODATION PACKAGES;\n• COURSE WITH FREE ACCOMMODATION – includes everything listed below (only eligible for persons in academia)\n• COURSE ONLY – Includes lunch and refreshments.\n• ACCOMMODATION PACKAGE (to be purchased in addition to the course only option) – Includes breakfast\, lunch\, welcome dinner Monday evening\, farewell dinner Friday evening\, refreshments and accommodation. Self-catering facilities are available in the accommodation. Accommodation is approximately a 6-minute walk from the PR statistics head office. Accommodation is multiple occupancy (max 3-4 people) single sex en-suite rooms. Arrival Sunday 2nd December (between 17:00 – 19:00) and departure Friday 7th December (accommodation must be vacated by 09:15). \nOther payment options are available please email oliverhooker@psstatistics.com \nCancellation policy: Cancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered\, contact oliverhooker@psstatistics.com Failure to attend will result in the full PLEASE READ – CANCELLATION POLICY: Cancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered\, contact oliverhooker@psstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees (and accommodation fees if booked through PR informatics) will be credited. However\, PS statistics will not be held responsible/liable for any travel fees\, accommodation costs or other expenses incurred to you as a result of the cancellation. Because of thisPS statistics strongly recommends any travel and accommodation that is booked by you or your institute is refundable/flexible and to delay booking your travel and accommodation as close the course start date as economical viable. \n\n\n\n \nDr. Mark Andrews\n\n\n \n\n\n\n\n\n\nTeaching Format\n\nThis course will be hands-on and workshop based. Throughout each day\, there will be some lecture style presentation\, i.e.\, using slides\, introducing and explaining key concepts. However\, even in these cases\, the topics being covered will include practical worked examples that will work through together. \nAssumed quantitative knowledge \nWe assume familiarity with inferential statistics concepts like hypothesis testing and statistical significance\, and some practical experience with commonly used methods like linear regression\, correlation\, or t-tests. Most or all of these concepts and methods are covered in a typical undergraduate statistics courses in any of the sciences and related fields. \nAssumed computer background \nR experience is desirable but not essential. Although we will be using R extensively\, all the code that we use will be made available\, and so attendees will just need to copy and paste and add minor modifications to this code. Attendees should install R and RStudio on their own computers before the workshops\, and have some minimal familiarity with the R environment. If some additional familiarity with R is required\, countless short video introductions to R and RStudio are available online (e.g.\, https://youtu.be/lVKMsaWju8w). \nEquipment and software requirements \nA laptop computer with a working version of R or RStudio is required. R and RStudio are both available as free and open source software for PCs\, Macs\, and Linux computers. R may be downloaded by following the links here https://www.r-project.org/. RStudio may be downloaded by following the links here: https://www.rstudio.com/. In addition to R and RStudio\, Stan for R should also be installed. Stan is also free and open source software and is available for PCs\, Macs\, and Linux computers. More information about Stan is available here http://mc-stan.org/\, and Stan for R (i.e.\, RStan) can be installed from here https://github.com/stan-dev/rstan/wiki/RStan-Getting-Started. Many supplementary R packages will be required. The list of necessary packages will be made available to all attendees prior to the course. These can all be installed from within RStudio will one click. It is highly recommended that all attendees come with all the necessary software and packages installed in advance. This will minimize troubleshooting during the workshop that might delay our progress. \nhttps://cran.r-project.org/ \nDownload RStudio \nUNSURE ABOUT SUITABLILITY THEN PLEASE ASK oliverhooker@psstatistics.com \n\n\n\nCourse Programme\n\nSunday 3rd\nMeet at 43 Cook Street\, Glasgow G5 8JN at approx. 17:00 onwards \nMonday 4th – Classes from 09:30 to 17:30 \nClass 1: We will begin with a overview of what Bayesian data analysis is in essence and how it fits into statistics as it practiced generally. Our main point here will be that Bayesian data analysis is effectively an alternative school of statistics to the traditional approach\, which is referred to variously as the classical\, or sampling theory based\, or frequentist based approach\, rather than being a specialized or advanced statistics topic. However\, there is no real necessity to see these two general approaches as being mutually exclusive and in direct competition\, and a pragmatic blend of both approaches is entirely possible. \nClass 2: Introducing Bayes’ rule. Bayes’ rule can be described as a means to calculate the probability of causes from some known effects. As such\, it can be used as a means for performing statistical inference. In this section of the course\, we will work through some simple and intuitive calculations using Bayes’ rule. Ultimately\, all of Bayesian data analysis is based on an application of these methods to more complex statistical models\, and so understanding these simple cases of the application of Bayes’ rule can help provide a foundation for the more complex cases. \nClass 3: Bayesian inference in a simple statistical model. In this section\, we will work through a classic statistical inference problem\, namely inferring the number of red marbles in an urn of red and black marbles. This problem is easy to analyse completely with just the use of R\, but yet allows us to delve into all the key concepts of all Bayesian statistics including the likelihood function\, prior distributions\, posterior distributions\, maximum a posteriori estimation\, high posterior density intervals\, posterior predictive intervals\, marginal likelihoods\, Bayes factors\, model evaluation of out-of-sample generalization. \nTuesday 5th – Classes from 09:30 to 17:30 \nClass 4: Bayesian analysis of linear and normal models. Statistical models based on linear relationships and normal distribution are a mainstay of statistical analyses in general. They encompass models such as linear regression\, Pearson’s correlation\, t-tests\, ANOVA\, ANCOVA\, and so on. In this section\, we will describe how to do Bayesian analysis of linear and normal models\, paying particular attention to Bayesian linear regression. One of the aims of this section is to identify some important and interesting parallels between Bayesian and classical or frequentist analyses. This shows how Bayesian and classical analyses can be seen as ultimately providing two different perspectives on the same problem. \nClass 5: The previous section provides a so-called analytical approach to linear and normal models. This is where we can calculate desired quantities and distributions by way of simple formulae. However\, analytical approaches to Bayesian analyses are only possible in a relatively restricted set of cases. However\, numerical methods\, specifically Markov Chain Monte Carlo (MCMC) methods can be applied to virtually any Bayesian model. In this section\, we will re-perform the analysis presented in the previous section but using MCMC methods. For this\, we will use the brms package in R that provides an exceptionally easy to use interface to Stan. \nClass 6: This section continues the previous one\, but explores a wider range of linear and normal models\, namely the general linear models. These include models with multiple predictors\, some or all of which may be categorical\, and interactions between these predictors. We will use brms for all of these analyses. For all the examples covered here\, we will use real world data-sets taken from a variety of different fields. \nWednesday 6th – Classes from 09:30 to 17:30 \nClass 7: Bayesian generalized linear models. Generalized linear models include models such as logistic regression\, including multinomial and ordinal logistic regression\, Poisson regression\, negative binomial regression\, and other models. Again\, for these analyses we will use the brms package and explore this wide range of models using real world data-sets. \nClass 8: Model evaluation and checking. A general topic in any analysis is to evaluate the suitability of the chosen or assumed statistical models in the analysis. This general topic incorporates hypothesis testing. In this section\, we will discuss this topic in depth\, paying particular attention to posterior predictive checks\, cross-validation\, information criteria\, and Bayes factors. We will revisit many of the examples covered so far\, and perform model checking and evaluation and hypothesis testing with the models that we used. \nThursday 7th – Classes from 09:30 to 17:30 \nClass 8: Multilevel general and generalized linear models. In this section\, we will cover the multilevel variants of the regression models\, i.e. linear\, logistic\, Poisson etc\, that we have covered so far. The topic of multilevel (or hierarchical) models is a major one\, and multilevel models are widely used throughout the sciences. In general\, multilevel models arise whenever data are correlated due to membership of a group (or group of groups\, and so on). For example\, if we have data concerning how socioeconomic status relates to educational achievement\, the data might come from individual children. But these children are in separate schools\, the schools are in separate cities\, and the cities are in separate countries. Thus\, the entire data-sets comprises groups (of groups etc) of data subsets\, and there may be important variation across these subsets. The entire day is devoted to multilevel regression models. We will\, as before\, use a wide range of real-world data-sets\, and move between linear\, logistic\, etc.\, models are we explore these analyses. We will pay particular attention to considering when and how to use varying slope and varying intercept models\, and how to choose between maximal and minimal models. Here\, we will cover model checking and evaluation in the same depth as with the previous models. \nFriday 8th – Classes from 09:30 to 16:00 \nClass 9: MCMC in depth. Although we will used MCMC methods extensively thus far\, we will have hidden some of their technical details. As one approaches more advanced Bayesian topics\, a deeper understanding of MCMC methods is required. In this section\, we will begin by discussing simple Monte Carlo (MC) approaches like rejection sampling and importance sampling\, and then proceed to Markov Chain Monte Carlo (MCMC) such as Gibbs sampling\, Metropolis Hastings sampling\, slice sampling\, and Hamiltonian Monte Carlo. \nClass 10: Customized and bespoke statistical models. Thus far\, we have use the brms package for almost all of our analyses. While brms is an excellent tool\, in some cases\, especially in more advanced analyses\, it is not possible to use a pre-defined statistical model\, e.g. a linear or logistic regression model\, and it is necessary to develop customized and bespoke probabilistic models directly in the Stan language itself. In this final section of the course\, we will delve into how to write Stan code directly. We’ll first explore the Stan code that brms creates\, and we’ll learn how to modify this code. We will then write customized models that perform nonlinear regression using Gaussian processes and radial basis functions\, and also finite mixture models. Through these examples\, we will learn how to write and analyse any type of custom statistical model and thus produce models that are well suited to whatever specialized problem we are working on. \n\n\n\n
URL:https://www.psstatistics.com/course/introduction-to-bayesian-data-analysis-for-social-and-behavioural-sciences-using-r-and-stan-bdrs02/
LOCATION:53 Morrison Street\, Glasgow\, Scotland\,\, G5 8LB\, United Kingdom
GEO:55.8535874;-4.267977
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BEGIN:VEVENT
DTSTART;VALUE=DATE:20200330
DTEND;VALUE=DATE:20200404
DTSTAMP:20191017T085944
CREATED:20180703T090336Z
LAST-MODIFIED:20190904T132511Z
UID:3253-1585526400-1585958399@www.psstatistics.com
SUMMARY:Introduction to statistical modelling for psychologists in R (IPSY03)
DESCRIPTION:\nCourse Overview:\nThis course will provide an introduction to working with real-life data typical of those encountered in the field of psychology. The course will be delivered by Dr. Dale Barr and Dr. Luc Bussière\, who are practicing academics in the fields of psychology and evolutionary biology respectively\, with many years of expertise with R and statistical modelling as both scientists and instructors. This five-day course will consist of series of modules (each lasting roughly half a day) covering topics including the basic ‘canon’ of psychological statistics (t-test\, correlation/regression\, ANOVA) presented within the framework of general linear models\, and building up to logistic regression and linear mixed-effects modelling. Along the way you will gain in-depth experience in data wrangling using the R ‘tidyverse’\, data and model visualisation and plotting\, as well as exploring and understanding model diagnostics. Classes will consist of a mixture of lectures and practical exercises designed to either build required skills for future modules or to perform a family of analyses that is frequently encountered in the psychological literature. \n\n\nIntended Audience\nAny researchers (from postgraduate students to senior investigators) wanting to learn how to correctly analyse data typical to the field of psychology using the R programming language. \nVenue – PS statistics head office\, 53 Cook Street\, Glasgow\, G5 8LB – Google map\nAvailability – 30 places\nDuration – 5 days\nContact hours – Approx. 35 hours\nECT’s – Equal to 3 ECT’s\nLanguage – English \nPackages\nWe offer COURSE WITH FREE ACCOMMODATION\, COURSE ONLY and ‘ACCOMMODATION PACKAGES;\n• COURSE WITH FREE ACCOMMODATION – includes everything listed below (only eligible for persons in academia)\n• COURSE ONLY – Includes lunch and refreshments.\n• ACCOMMODATION PACKAGE (to be purchased in addition to the course only option) – Includes breakfast\, lunch\, refreshments and welcome dinner Monday evening. Self-catering facilities are available in the accommodation. Accommodation is multiple occupancy (max 3- 4 people) single sex en-suite rooms. Arrival Sunday 29th March (between 17:00-21:00) and departure Friday 3rd April (accommodation must be vacated by 09:15). \nTo book ‘COURSE ONLY’ with the option to add the additional ‘ACCOMMODATION PACKAGE’ please scroll to the bottom of this page. \nOther payment options are available please email oliverhooker@psstatistics.com \nPLEASE READ – CANCELLATION POLICY: Cancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered\, contact oliverhooker@psstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees (and accommodation fees if booked through PS statistics) will be credited. However\, PS statistics will not be held responsible/liable for any travel fees\, accommodation costs or other expenses incurred to you as a result of the cancellation. Because of this PS statistics strongly recommends any travel and accommodation that is booked by you or your institute is refundable/flexible and to delay booking your travel and accommodation as close the course start date as economically viable. \n\n\n\n \nDr. Dale Barr\n\n\n \nDr. Luc Bussiere\n\n\n\n\n— \n\n\nTeaching Format\n\nThere will be a combination of lectures and practicals. Practicals will be based on the topics covered in the preceding lectures. Data sets for computer practicals will be provided by the instructors \nAssumed quantitative knowledge \nA basic understanding of statistical concepts\, including statistical significance and hypothesis testing \nAssumed computer background \nFamiliarity with R. Ability to import/export data\, manipulate data frames\, fit basic statistical models & generate simple exploratory and diagnostic plots. Relative newcomers to programming in R will be provided (by the instructors) with some introductory exercises to complete prior to the course. This will introduce some of the core features of R and RStudio before the course starts. \nEquipment and software requirements \nA laptop/personal computer with a working version of R or RStudio. R and RStudio are supported by both PC and MAC and can be downloaded for free by following these links. \nhttps://cran.r-project.org/ \nDownload RStudio \nIt is essential that you come with all necessary software and packages already installed (you will be sent a list of packages prior to the course) as internet access may not always be available. \nUNSURE ABOUT SUITABLILITY THEN PLEASE ASK oliverhooker@psstatistics.com \n\n\n\nCourse Programme\n\nSunday 29th\nMeet at 43 Cook Street\, Glasgow G5 8JN at approx. 17:00 onwards \nMonday 30th – Classes from 09:30 to 17:00\nIntroduction to R/RStudio\n• interacting with the RStudio IDE\n• installing add-on packages\n• R scripts and R notebooks\n• coding style guidelines\n• session management and project organization \nData wrangling and reproducible workflows with the tidyverse\n• loading datasets (csv\, excel\, SPSS\, google drive)\n• filtering\, sorting\, and reshaping data\n• grouping and summarizing data\n• combining datasets using joins\n• chaining commands together using ‘pipes’ \nTuesday 31st – Classes from 09:30 to 17:00\nData visualization with ggplot2\n• the ‘grammar of graphics’ philosophy\n• univariate plots: histograms\, density plots\, boxplots\, bar graphs\, violin and pirate plots\n• bivariate plots: scatterplots\, line graphs\, interaction plots\n• enhancing plots using labels and themes\n• creating subplots with faceting \nThe psychology stats ‘canon’ and the General Linear Model\n• t-tests\, confidence intervals\, effect size\, and power\n• correlation matrices and simple linear regression\n• contingency tables; chi-square tests\n• correlation and simple regression \nWednesday 1st – Classes from 09:30 to 17:00\nMultiple Regression\n• coding categorical predictors\n• detecting and dealing with multicollinearity\n• polynomial models for time-series data\n• model comparison and information criteria\n• model checking/validation\, plotting predictions \nThursday 2nd – Classes from 09:30 to 17:00\nAnalysis of Variance in the GLM framework\n• one-factor designs\n• multifactor designs: main effects and interactions\n• within-subject and mixed designs\n• checking assumptions (sphericity\, normality\, homogeneity of variance)\n• plotting and interpreting interactions\n• follow-up tests and contrasts \nGeneralized Linear Models\n• binary data (logistic regression)\n• count data (Poisson regression)\n• generating and plotting model predictions \nFriday 3rd – Classes from 09:30 to 16:00\nIntroduction to Linear Mixed-Effects Models\n• crossed random effects of participant and item\n• understanding variance components through data simulation\n• specifying the random effects structure\n• translating study design into lmer model syntax \n\n\n\n
URL:https://www.psstatistics.com/course/introduction-to-statistics-using-r-for-psychologists-ipsy03/
LOCATION:53 Morrison Street\, Glasgow\, Scotland\,\, G5 8LB\, United Kingdom
GEO:55.8535874;-4.267977
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