Masterspecialisatie, Psychologische Methodenleer

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1 Masterspecialisatie, Psychologische Methodenleer 4. Psychologische Methodenleer 4.1 Doelgroep en toelatingseisen De master Psychologie met specialisatie Psychologische Methodenleer is toegankelijk voor studenten die de bachelorspecialisatie Methodenleer hebben afgerond of een bachelor Psychologie hebben met vergelijkbare kennis en vaardigheden. Voor meer informatie kan men contact opnemen met de studieadviseur Psychologische Methodenleer, dr. P. Koele. 4.2 Doelstelling van het onderwijs van de specialisatie PML Zowel psychologisch onderzoek als de toepassing van wetenschappelijke kennis in de psychologische praktijk berusten op methodologische overwegingen en overtuigingen. Het is dan ook onmogelijk om wetenschappelijk onderzoek op waarde te kunnen schatten zonder een gedegen methodologische kennis. De algemene doelstelling van het onderwijs is het aanleren van een kritische en analytische houding ten opzichte van psychologisch onderzoek. Dit komt op verschillende manieren tot uiting. Door het blootleggen van veel gemaakte fouten in empirisch onderzoek en het aantonen van inconsistenties in theoretische redeneringen, proberen methodologen onderzoekers attent te maken op de vele valkuilen van psychologisch onderzoek. Deze houding is gericht op de verbetering van het psychologisch onderzoek en de praktijkvoering. In de tweede plaats ontwikkelen methodologen nieuwe methoden, technieken en bewijsvoeringen. Zo worden bijvoorbeeld modellen ontwikkeld voor de simultane analyse van responsen en reactietijden en modellen voor de analyse van strategieën die kinderen gebruiken op rekentaken. Ook wordt onderzocht aan welke criteria psychologische tests moeten voldoen om gemiddelden tussen groepen te kunnen vergelijken (denk bijvoorbeeld aan verschillen tussen etnische groepen op IQ-tests). Een andere nieuwe techniek waaraan aandacht wordt besteed is testafname via de computer, waarbij de inhoud van de test adaptief is en wordt afgestemd op het antwoordpatroon van degene die de test maakt. In de derde plaats zijn er theoretische problemen die zo algemeen zijn dat zij in andere onderdelen van de psychologie nauwelijks aan de orde komen, maar wel door methodologen worden bestudeerd. Bijvoorbeeld: wat is de status van psychologische begrippen zoals algemene intelligentie, extraversie of attitudes? Bestaan die eigenschappen echt of moeten ze beschouwd worden als theoretische constructies, die weliswaar nuttig zijn voor het voorspellen van studiesucces en het selecteren van sollicitanten, maar verder geen enkele aanspraak kunnen maken op de werkelijkheid? Zulke algemene vragen bevinden zich op het snijvlak van psychologie en filosofie en worden in de empirisch georiënteerde psychologie enigszins verwaarloosd. In de methodologie worden zij diepgaand behandeld. Studenten met een bachelor PML hebben twee mogelijkheden voor verdere specialisatie in de Methodenleer: óf men volgt de eenjarige aansluitmaster, óf men kiest voor de tweejarige Research Master. Naar een plek in de Research Master moet worden gesolliciteerd, waarbij studieprestatie, motivatie en interesse in onderzoek belangrijke selectiecriteria zijn. Meer informatie over de Research Master Psychology is te vinden in hoofdstuk 9. De specialisatie PML in de aansluitmaster is vooral een opleiding in de toegepaste methodenleer; de Research Master is meer theoretisch van aard en is vooral bedoeld voor studenten die zich verder willen ontwikkelen in de psychologie als empirische wetenschap door na de Master een traject te volgen dat leidt naar promotie tot doctor in één van de wetenschappelijke disciplines. Meer informatie is te vinden op nl/ma-psychology. 293

2 Masterspecialisatie, Psychologische Methodenleer 4.3 Beroepsmogelijkheden In het algemeen is de afgelopen decennia gebleken dat afgestudeerden in de Methodenleer zeer goede mogelijkheden hebben om een baan te vinden. Dit perspectief zal in de toekomst niet wezenlijk veranderen. Met de aansluitmaster Methodenleer heeft men uitgebreide kennis van de belangrijkste onderzoeksmethoden en data-analysetechnieken en praktische ervaring met het doen van onderzoek en het publiceren erover. Deze afgestudeerden komen voornamelijk terecht in onderzoeksfuncties. Deze functies zijn niet beperkt tot de methodenleer, maar zijn verspreid over de gehele psychologie en diverse andere wetenschapsgebieden. Enkele voorbeelden van functies die door methodologen worden vervuld zijn: - Methodologisch adviseur, bijv. op het gebied van onderwijsresearch, psychodiagnostiek, research management. - Praktijkgericht onderzoeker in het bedrijfsleven, o.a. bij testuitgevers, de researchafdeling van banken, bureaus voor marktonderzoek en opiniepeiling. - Beleidsmedewerker bij overheid en subsidiegevers. - Zelfstandig gevestigd onderzoeker. Met een afgeronde Research Master heeft men ruime kennis en ervaring met onderzoek en ligt een promotietraject voor de hand. Na de promotie zijn de volgende beroepsmogelijkheden van toepassing. - Wetenschappelijk onderzoeker o.a. op het gebied van de psychometrie, methodenleer, onderwijsresearch, verkeerskunde, gezondheidspsychologie, sociale tandheelkunde, psychodiagnostiek en ontwikkelingspsychologie. - Universitair docent, o.a. op het gebied van de statistiek, methodenleer, psychometrie, theoretische psychologie. Let wel, deze beroepsmogelijkheden zijn slechts een indicatie. Methodologen worden soms ook uitgever, therapeut of minister. 4.4 Onderwijsprogramma master PML In de bachelorspecialisatie PML is een brede basis gelegd voor de opleiding tot psychologisch methodoloog. In de aansluitmaster wordt deze opleiding voortgezet door een verdergaande specialisatie in de methodenleer. Daarbij wordt het onderwijsprogramma sterk bepaald door keuzevrijheid die noodzakelijk is voor de eigen vormgeving van het toekomstige beroep. Het aanbod van masteronderdelen PML is schematisch als volgt weergegeven. Semester 1 Semester 2 8 wkn 8 wkn 4 wkn 8 wkn 8 wkn 4 wkn 12 ec 12 ec 6 ec 12 ec 12 ec 6 ec Expl. IRT (6) Meth. Advice (6) Stage/Masterthese (36) Pres. (6) Gen. Analysis (6) ACMA (6) Calc (3) Math. Stat. (6) AP (3) BGM(3) Psychometrics (6) LISREL (6) AdLI(3) MA (3) R (6) MTT(3) Noot: verplichte cursussen zijn grijsgekaderd en vetgedrukt Verplichte onderdelen - Stage (18 ec) en Masterthese (18 ec, inclusief colloquia), samen 36 ec - Methodological Advice (6 ec)* 294

3 Masterspecialisatie, Psychologische Methodenleer Keuzeonderdelen - ACMA (Applied Continuous Multivariate Analysis) (6 ec)* - Advanced LISREL (3 ec)* - Advanced Programming (3 ec)* - Calculus (3 ec)* - Explanatory IRT (6 ec)* - Genetic Analysis of Psychology (6 ec)* - LISREL (6 ec)* - Mathematical Statistics (6 ec)* - Meta-analysis (3 ec)* - Modern Test Theory (3 ec)* - A Practical Course in Bayesian Graphical Modeling (3 ec)* - Programming Skills: R (6 ec)* - Programming Skills: Stimulus Presentation (6 ec)* - Psychometrics (6 ec)* - Onderdelen uit andere studierichtingen (max. 6 ec) * Cursussen aangeduid met * worden in het Engels gegeven. Stage (18 ec) en masterthese (18 ec, inclusief colloquia) vormen in feite de hoofdmoot van het Masterjaar. In deze onderdelen doet de student onderzoekservaring op en kunnen de kennis en vaardigheden die in de eerdere fase van de studie zijn opgedaan, worden toegepast. De masterthese moet in principe kunnen leiden tot een publicatie die geschikt is voor plaatsing in een wetenschappelijk tijdschrift. Onderwerpen voor stage en masterthese staan vermeld in een brochure die verkrijgbaar is bij het secretariaat Methodenleer. 4.5 Contactgegevens secretariaat en coördinatoren PML Secretariaat Mw. W.H.M. van Osch, kamer 524, tel , mlsecretariaat-fmg@uva.nl Onderwijscoördinator Dr. P. Koele, kamer 513, tel , P.Koele@uva.nl Websites Voorzitter van de programmagroep Prof. dr. H.L.J. van der Maas, kamer 520, tel , H.L.J.vanderMaas@uva.nl Voor contactgegevens van de docenten van PML en een overzicht van hun interessegebieden: zie Hoofdstuk 10. Op de volgende pagina s volgt een overzicht van de masteronderdelen die gevolgd kunnen worden binnen de specialisatie PML. 295

4 Masteronderdelen, Psychologische Methodenleer Masterthese Psychologische Methodenleer Punten 18 ec Tentamencode MWML 7204MWMLXY Toelatingsvoorwaarden Bachelor Psychologie Docent Alle docenten van de programmagroep Inhoud In de masterthese verricht men een (relatief) zelfstandig onderzoek, waarin de zelfstandigheid tot uiting komt in de opzet, uitvoering, analyse en rapportage. De eerste fase van de masterthese bestaat uit het schrijven van het MT-onderzoeksvoorstel. Het voorstel wordt, na goedkeuring door de begeleider, ingediend via een standaardformulier voor goedkeuring door de masterthesecoördinator. Als het MT-onderzoeksvoorstel is goedgekeurd kan het eigenlijke onderzoek beginnen. Onderwijsvorm Globaal houdt een masterthese voor een student de volgende werkzaamheden in: (a) het bedenken van een vraagstelling of het uitwerken van een gegeven vraag- of probleemstelling; (b) verdieping in de literatuur om op de hoogte te raken van het onderwerp; (c) het bedenken van een onderzoeksopzet; d) het schrijven van een MT-onderzoeksvoorstel; (e) het verzamelen en analyseren van de data; (f) de verslaglegging; en (g) de openbare presentatie. Daarnaast dienen 10 colloquia te worden bijgewoond. Zie hoofdstuk 6 voor meer informatie. Beoordeling Zie voor de beoordeling de Studentenhandleiding op Blackboard. Bijzonderheden Voor een masterthese krijgt men standaard 18 ec. Minder is niet mogelijk. In uitzonderlijke gevallen, en met een daartoe strekkende overtuigende motivatie, te beoordelen door de begeleider en de MT-coördinator van de specialisatie, kan de masterthese een omvang van 24 ec hebben. De masterthese is formeel opgesplitst in MT-onderzoeksvoorstel (4 ec) en MT-onderzoek (14 ec). Het MT-onderzoeksvoorstel moet gereed zijn binnen maximaal 5 weken na aanvang. De masterthese (inclusief schrijven MT-onderzoeksvoorstel) wordt uitgevoerd in een tijdsbestek van minimaal 12 weken (fulltime) en maximaal 24 weken (halftime). Het verloop van de masterthese laat zich doorgaans niet heel nauwkeurig plannen; het is verstandig rekening te houden met enkele weken uitloop voor geval van nood. Over omvang en planning worden vooraf duidelijke afspraken gemaakt met de begeleider. Deze afspraken worden vastgelegd in een MT-contract, waarin student en begeleider zich committeren aan een bepaalde planning en inspanning (het MT-contract bevat o.m. de aanvangsdatum, geplande einddatum, aantal ec, en door wie en op welke wijze de begeleiding verzorgd wordt). De inhoud van het voorgenomen onderzoek, en een nader uitgewerkte planning (waaruit de haalbaarheid moet blijken), maken deel uit van het MT-onderzoeksvoorstel. Meer informatie over de masterthese is te vinden in hoofdstuk 6 van deze studiegids en in de brochures/handleidingen die elke specialisatie biedt over de afstudeeropdracht. Daarnaast is een handleiding voor de masterthese te vinden op Blackboard. 296

5 Masteronderdelen, Psychologische Methodenleer Stage Psychologische Methodenleer Punten: 18 ec Tentamencode: MSML 7204MSMLXY Plaats in het rooster: gedurende het hele jaar Toelatingsvoorwaarden: Bachelor afgerond, met specialisatie PML Inlichtingen: Dr. Pieter Koele Inhoud Een student dient de kennis die hij/zij op de universiteit heeft opgedaan in de praktijk te brengen. De stagewerkzaamheden dienen van academisch niveau en methodologische aard te zijn. Na een inwerkperiode moet de student in staat zijn om de werkzaamheden met een redelijke mate van zelfstandigheid uit te voeren. Onderwijsvorm Begeleiding Bij interne stages heeft men met één begeleider te maken; bij externe stages moet er ook een externe begeleider zijn op de stageplek. Deze moet bekend zijn met het werk van PML-psychologen, en is bij voorkeur zelf een (PML-)psycholoog. Voor aanvang van de stage dienen de student en begeleider(s) afspraken te maken over de begeleiding, inhoud, werkwijze en duur van de stage. Stageverslag De werkzaamheden tijdens een stage worden na afloop van de stage gerapporteerd door middel van een stageverslag. Verdere informatie betreffende het stageverslag is te vinden in de stagehandleiding van PML op Blackboard. Beoordeling Zie voor de beoordeling de stagehandleiding op Blackboard. Bijzonderheden Combinatie Combinatie van masterthese en stage is mogelijk; overleg hiervoor met de stage/masterthesecoördinator. Omvang Een stage duurt 13 weken voltijd of langer indien de stage parttime plaatsvindt. De stage wordt gehonoreerd met 18 ec. 297

6 ACMA Applied Continuous Multivariate Analysis 6 ec RM RM01XY Semester Semester 1, period 2 Number of participants Max. 45 Prerequisites Bachelor of Psychology s Dr. L.J. Waldorp & dr. C.V. Dolan Information L.J.Waldorp@uva.nl With multivariate statistics, you simultaneously analyze multiple dependent and possibly multiple independent variables. When analyzing multiple dependent variables, techniques like the t-test or ANOVA are no longer sufficient to capture all relevant information in the data. Techniques like MANOVA are then required. Several possibilities of analyses will be discussed theoretically, and by analyzing examples by hand and with the aid of computer programs. Emphasis will be on applications: when to apply it (and when not), how to apply the different techniques, checking of assumptions, and limitations of multivariate analysis. First, we will review some univariate techniques. Then we move on to multivariate analyses using a bit of matrix algebra. Multivariate analyses begin with a brief introduction to regression analysis. Then we will discuss and practice several techniques: multiple regression, MANOVA, analysis of covariance and profile analysis (repeated measures). Teaching format Each week there will be one lecture and one practical session to apply techniques from the lecture. Assignments (30%), presentations (5%), and final exam (65%). To be announced. Tue through hrs. room A306 Thu through hrs. room A106 + A107 Examination Dates Fri hrs. room A Re-examination by mutual agreement. Special Conditions Also suitable as a course for PhD students to (re)inform themselves on relevant methods of analysis. PhD s can apply by contacting Cok Koeleman, C.H.Koeleman@uva. nl. 298

7 Advanced LISREL 3 ec RM RM23XY Semester Semester 2, period 2 Number of participants Max. 24 Prerequisites Bachelor of Psychology, LISREL (RM07) Dr. C.V. Dolan Information C.V.Dolan@uva.nl This follow-up course to LISREL (RM07) concerns advanced subjects in structural equation modeling. The objectives of this course are 1) extension of the LISREL model as introduced in RM07 with structured means; 2) application of models with structured means; 3) introduction of estimators; 4) statistical power calculation; 5) SEM of discrete data. Teaching format Lectures and practicals. The practicals will involve the student version of the LISREL program ( Intermediate assignments (25%) and a takehome exam (75%). The course material consists of lecture notes, which include exercises and solutions to exercises. These will be made available on the BlackBoard. Tue through hrs. room A410 Thu through hrs. room A

8 Advanced Programming 3 ec RM RM21XY Semester Semester 2, period 2 Number of participants Max. 20 Prerequisites Bachelor of Psychology, Programming in R (RM05), or Stimulus Presentation (RM24) s Prof dr. H.L.J. van der Maas, dr. R.P.P.P. Grasman, and others Information H.L.J.vanderMaas@uva.nl By working on one large computer program students will refresh and improve their programming skills. After this course students should be able to program large-scale simulations, to implement new statistical methods or develop experiments for their internship or master project. Students will choose one of a set of assignments depending on their choice of programming language. In four weeks they will write a computer program. The first week handles the requirements and design of the program. The second and third week is used for the actual implementation and testing. The third week focuses at testing, improvements, reporting and presentation of the program. Teaching format Weekly meetings, programming. Grading takes place on the basis of the program (2/3), report (1/6) and presentation (1/6). Tue through hrs. room A

9 Calculus 3 ec RM RM08XY Semester Semester 2, period 1 Number of participants Min. 6, max. 20 Prerequisites Bachelor of Psychology Dr. R.P.P.P. Grasman Information R.P.P.P.Grasman@uva.nl The purpose of this course is to teach you the basic mathematical skills that are assumed in formalized areas such as mathematical psychology, theoretical/computational neuroscience, mathematical statistics, and psychometrics. In this course you train these analytical skills which are absolutely essential for full understanding of the literature in these fields. This course covers the requirements for the courses Mathematical Statistics and Mathematical Psychology. Based on the experience of students in previous years, those of you with little high school mathematics are advised to refresh your algebra skills in the month before the course starts. Contact the lecturer for self study material. To assess your algebra level a test will be available through the course website. Uni- and multivariable differential- and integral-calculus, special functions, optimization and conditions for extrema, expansions, sequences, power- and Taylor-series, elementary differential equations, applications. Teaching Format Lectures, assignments and exercises. Assignments and exercises have to be handed in time to count for credit. Assignments and exercises (30%), and written exam with problems (70%). One cannot pass with a failed exam. The textbook is announced on the course website. Course notes (including exercises), will also be provided through the course website. Assignments will involve the use of computer software (a spreadsheet program, or R). Check the course website at ly/psyuvacalculus for more information and anouncements. Mon through hrs. room A410 Thu through hrs. room A410 Examination Dates Thu hrs. room A410 Thu hrs (resit) 301

10 Computational Modeling in Cognition Principles and Practice 3 ec RM RM26XY Semester Semester 2, period 2 Number of participants Max. 24 Prerequisites Bachelor of Psychology Prof. dr. Lewandowsky This course explains the principles of computational modeling. Concrete computer exercises are used to put the theory into practice. This course makes it easier for you to critically evaluate research articles that feature computation models, and it also helps you to develop computational models yourself. More generally, this course will change your perspective on psychological science. The course will first underscore the need for computational modeling by examining some pertinent recent examples from the field. In particular, it is shown how verbal models alone cannot be relied upon to provide testable predictions, and that a computational instantiation is often necessary to discover the true predictions of a model. This is followed by an introduction of the basics of parameter estimation, model comparison and selection, and interpretation of models. Specifically, the course will cover basic SIMPLEX methods and other minimization routines, and discuss the associated pitfalls (e.g., local minima, variability of predictions). The course also covers advantages of maximum likelihood estimation over least-squares approaches, and considerable time will be spent on various model selection techniques. All material will be presented with hands-on examples (using MATLAB). Any special purpose functions or other software that is required for the course will be made available for download ahead of time. The course will focus on several popular models of memory to introduce the relevant concepts; viz. SIMPLE (Brown et al., 2007); SOB (Lewandowsky & Farrell, 2008); and an instantiation of the TBRS (Oberauer & Lewandowsky, 2010). Teaching format Lectures and hands-on computer exercises. will be based on an assignment that puts the learned material into practice. The assessment also involves a written component. Lewandowsky, S., & Farrell, S. (2011). Computational modeling in cognition: Principles and practice. Thousand Oaks, CA: Sage. ISBN , ± 40,- Tue through hrs. room A410 Thu hrs. room A106 Wed hrs. room A106 Thu through hrs. room A

11 Examination Dates The due date for the assignment has not yet been determined, but it is likely to be one or two weeks after the class finishes. 303

12 Explanatory IRT 6 ec RM RM19XY Semester Semester 1, period 1 Prerequisites Bachelor of Psychology Prof. dr. P. de Boeck Information P.A.L.deBoeck@uva.nl To make the student acquainted with recent, advanced developments in item response theory, and with a general purpose software package in R to estimate these and other models. This course gives a new and integrated introduction to item response models (predominantly used in measurement applications in psychology, education, and other social science areas) from the viewpoint of the statistical theory of generalized linear and nonlinear mixed models. The new framework allows the domain of item response models to be co-ordinated and broadened to emphasize their explanatory use beyond their standard descriptive uses. For the estimation of the models, the lme4 package in R will be used, and more specifically its lmer function. The software can be used for IRT models and more generally for all kinds of generalized linear mixed models, including also the simple linear case as in ANOVA, multilevel modeling, and many other types of more specific models. Teaching Format Lectures based on exercises in class. Advanced knowledge of R is not required. Assignments. De Boeck, P., & Wilson, M. (Eds.). (2004). Explanatory Item Response Models. New York: Springer Verlag. ISBN ,- De Boeck, P. et al. (2011). The estimation of item response models with the lmer function from the lme4 package in R. Journal of Statistical Software, 39. See: Tue through hrs. room A106 Thu through hrs. room A106 Examination Dates Thu hrs. room A

13 Genetic Analysis in Psychology 6 ec RM RM25XY Semester Semester 1, period 1 Number of participants Max. 24 Prerequisites Bachelor of Psychology Dr. C.V. Dolan Information C.V.Dolan@uva.nl The aim of this course is to provide an introduction to genetic modeling and analyses, as one may encounter it in psychology. The subjects in this course will include twin and family studies, linkage analysis, and (genome wide and candidate gene) association analysis. The course material consists of lecture notes and articles. The course will include computer exercises. Prior knowledge of statistics should include a good understanding of regression analysis. Teaching format Lectures and computer practicals. Assignments (50%) and exam (50%). To be determined. Mon through hrs. room A409 Thu through hrs. room A312 Examination Dates Thu hrs. room A

14 LISREL 6 ec RM RM07XY Semester Semester 2, period 1 Number of participants Max. 45 Prerequisites Bachelor of Psychology, ACMA (RM01) Dr. C.V. Dolan Information C.V.Dolan@uva.nl, The aim of this course is to introduce the student to the theory and practice of LISREL (Linear Structural Relations) modeling, and to instruct students in actually carrying out LISREL modeling using the LISREL program. The LISREL model comprises a large number of specific statistical models, such as multiple and multivariate regression, pathmodels, factor models, longitudinal models, and certain random effects models. The LISREL program can be used to test specific hypotheses concerning the linear relationships between variables. These relationships may be specified among observed variables (e.g., in linear regression, or path models) and/or latent variables (latent regression). In addition such linear relationships are used to link observed variables to latent variables. The possibility to investigate hypotheses concerning the relationship among latent variables is very useful, as most psychological theory is formulated in terms of latent variables, i.e., hypothetical constructs. Multi-group analyses and LISREL models including structured means will also be discussed. During this course the specification of a variety of linear statistical models as LISREL models will be discussed. The emphasis is on hands-on experience in model specification, and on the interpretation of results. Technicalities are limited to mainly elementary matrix algebra and the decomposition of variance, as is carried out in simple regression analysis. In fact, all models can be interpreted in terms of simple regression analysis. The student will practice model specification and the interpretation of results in weekly assignments. Teaching format Lectures and practical sessions. Weekly assignments (50%) and a final assignment (50%). To be announced on Blackboard. Mon through hrs. room A108 Thu through hrs. room A106 + A107 Special Conditions The practicals consist mainly of specifying and fitting models in the student version of the LISREL program (free to download 306

15 Mathematical Statistics 6 ec RM RM09XY Semester Semester 2, period 1 Number of participants Min. 6, max. 20 Prerequisites Bachelor of Psychology, Calculus (RM08) or equivalent Dr. R.P.P.P. Grasman Information R.P.P.P.Grasman@uva.nl Nearly all modeling and analysis in neuroscience/cognitive psychology and psychometrics requires probabilistic and statistical reasoning. Students are assumed to have a working knowledge in applied statistical techniques (hypothesis testing, correlation, linear regression, t-test, ANOVA, chi-square test). In this course you will acquire insight in, and the skills of, probability theory and statistics. You will master the fundamental building blocks for mathematical models in psychometrics, mathematical psychology and cognitive psychology/neuroscience. With these skills you will be able to delve into the analytical details of stochastic models and statistical methods used within these fields. Although this course is experienced as challenging by most students, with enough time available to spend on mastering the material this should not discourage your participation. Axiomatic probability, calculus of probabilities, random variables, distributions and densities, joint- and conditional distributions, expectation and moments, Chebyshev s inequality, moment generating functions, discrete and continuous probability models, sampling distributions, law of large numbers, central limit theorem, transformations of stochastic variables, theory and methods of estimation (Cramér-Rao inequality, method of moments, maximum likelihood) and hypothesis testing (Neyman-Pearson, likelihood ratio tests). Teaching Format Lectures, labs, and assignments. Presence at labs is required. Assignments and written exam. To be announced on Blackboard. Check the course website at for more information and announcements on the textbook. Mon through hrs. room A410 Thu through hrs. room A410 Examination Dates Thu hrs. room A410 Thu hrs. (resit) 307

16 Meta-analysis 3 ec RM RM13XY Semester Semester 2, period 2 Number of participants Max. 20 Prerequisites Bachelor of Psychology, Programming Skills: R (RM05) Dr. J.M. Wicherts Information J.M.Wicherts@uva.nl To gain insight into the basics and statistical background of meta-analysis. To learn to evaluate and to conduct a meta-analysis with moderators. This course is concerned with the methodological and statistical technique of metaanalysis. The goals are as follows: (a) understanding the basic statistical foundations of meta-analyses of experimental and correlational data, (b) reading and analyzing metaanalytic reviews, and (c) designing and conducting meta-analyses of relevant literatures. After completion of the course, the student will have basic knowledge of meta-analytical techniques and will be able to conduct a straightforward meta-analysis by him/herself. Teaching Format Lectures and assignments. Assignments (50%). The final assignment (50%) includes a meta-analysis and a presentation of the results. Articles to be announced on Blackboard. Wed through hrs. room A410 Mon through hrs. room A

17 Methodological Advice 6 ec RM RM11XY Semester Semester 1, period 2 Number of participants Max. 15 Prerequisites Bachelor of Psychology s Prof. dr. G.J. Mellenbergh & dr. H.J. Adèr Information G.J.Mellenbergh@uva.nl The objectives of the course are (1) to acquire methodological knowledge that is needed for advising researchers in the behavioral and social sciences on their research problems, and (2) to get experience with methodological consultancy. The course covers methodological consultancy on the following topics: research designs, construction and use of instruments, and statistical data analysis. A mini-traineeship at the Methodology Shop ( Methodologiewinkel ) is included for students who have not yet worked at this shop. Teaching Format Lectures, consultancy interviews, and discussion of consultancy problems. Assignments, oral and written reports. Adèr, H.J., & Mellenbergh, G.J. (with contributions by D.J. Hand) (2008). Advising on research methods: A consultant s companion. Huizen, the Netherlands: Johannes van Kessel Publishing. The book can be ordered via for the reduced student price of 57,50. Mon through hrs. room A413 Thu through hrs. room A

18 Modern Test Theory 3 ec RM RM17XY Semester Semester 2, period 2 Number of participants Max. 30 Prerequisites Bachelor of Psychology, Programming skills: R recommended Prof. dr. P. de Boeck Information P.A.L.deBoeck@uva.nl To provide insight into, and practical experience with, models for item responses. Tests and test scores play a very important role in psychology, both in research and in applied work. Traditionally, test scores are analyzed using classical test theory. This theory is still very influential, yet it has serious limitations. For example, the classical approach to reliability leads to incorrect conclusions. Modern test theory allows us to develop and test advanced models of item responses. With these models we can test for item bias, solve equating problems and develop computer adaptive tests. In computer adaptive testing items are administered depending on the success on earlier items in the test, which makes test much shorter and can guarantee that all respondents are measured in an equally reliable way and with the desired level of reliability. In this course students will be introduced to modern test theory and learn to work with software for practical data-analyses for item response models. Special attention will be given to measurement issues in the research domains the students are interested in, and it will be discussed how IRT can possibly help. Teaching Format Lectures and practical sessions. Weekly assignments (1/3) and final assignment (2/3). Hambleton, R.K., Swaminathan, H., & Rogers, H.J. (2000). Fundamentals of item response theory. Newbury Park: Sage. ISBN ,- Tue through hrs. room A107 Thu through hrs. room A107 Special Conditions This course is not appropriate for students with a bachelor in Psychological Methods. The course overlaps with the course Latente Variabele Modellen. 310

19 A Practical Course in Bayesian Graphical Modeling 3 ec RM RM16XY Semester Semester 2, period 3 Prerequisites Bachelor of Psychology Basic familiarity with R, Matlab, or C is highly recommended Dr. E.-J. Wagenmakers Information EJ.Wagenmakers@gmail.com First, to get hands-on experience with an easy-to-use computer program that allows you to implement all kinds of Bayesian models. Second, to understand why Bayesian statistics is right, and everything else is wrong. In this course, plenary lectures will provide the theoretical background of Bayesian statistics, whereas practical computer exercises will teach you how to use the WinBUGS program and apply it to a wide range of different statistical models. After completing this course, you will have gained not only a new understanding of statistics (yes, it can make sense), but also the technical skills to implement statistical models that professional researchers in the field of psychology dare only dream about. Teaching Format Plenary lectures, individual and group exercises. To be announced on Blackboard. Online materials based on a book that is currently in preparation (Lee & Wagenmakers). The location of the materials will be announced on Blackboard. Mon through hrs. room A107 Wed through hrs. room A

20 Programming Skills: R 6 ec RM RM05XY Semester Semester 1, period 3 Number of participants Max. 30 Prerequisites Bachelor of Psychology, Computer literate Prof. dr. H.L.J. van der Maas Information H.L.J.vanderMaas@uva.nl In this course students learn general programming concepts, to work with the programming language R, and to write programs for advanced data-analysis, for visualization of data and for computer simulation. R will be used in several other courses. Programming is an important research skill. Being able to program allows one, for instance, to investigate psychological models by simulations, to develop and implement new statistical techniques, to manipulate and analyze data in various ways, and to make computerized experiments. Psychological researchers use all kinds of programming languages. R and Matlab are high-level technical computing languages for statistical computing, graphics, and computer simulation. In this course students will work with numerous libraries available for these languages. The course will mainly focus on R, but also a short introduction to Matlab will be given. R is easy to learn, free and platform independent ( Teaching Format Lectures and practical sessions. Weekly programming assignments (1/3) and exam (2/3). Appleman, D. (2000). How computer programming works (2nd ed.). Berkeley: APress. ISBN , 27,90 (only when you have no experience with programming). Manuals, to be announced later (available on the internet). Lectures Mon through hrs. room A306 Wed through hrs rooma306 Practical sessions Mon through hrs. room A106 + A107 Wed through hrs. room A106 + A107 Fri through hrs. room A106 + A107 Examination Dates Fri hrs. room A106 + A107 Special conditions The course covers 4 weeks of full-time study. 312

21 Programming Skills: Stimulus Presentation 6 ec RM RM24XY Semester Semester 1, period 3 Number of participants Max. 25 Prerequisites Bachelor of Psychology, Computer literate s Dr. J.G. Wijnen, drs. T. Pronk, & drs. I.K. Schneider Information J.G.Wijnen@uva.nl, T.Pronk@uva.nl In this course students learn to work with two programming languages, both dedicated towards computerized psychological experimentation. At the end of the course students will be able to program their own experiments and get their data ready for statistical analyses. The course aims to teach general programming skills as well as skills more specific to the software covered. The course is especially suited for students that expect to carry out behavioral or psychophysiological lab research in projects in the near future or in their further academic careers. Presentation is a high-level technical computing language that is very suitable to program psychological stimulus-response tasks in a lab environment. It is specially developed for tasks with critical timing requirements. In addition, it has many features that facilitate communication with other equipment (EEG, eye-tracker, functional MRI, TMS, etc.). Inquisit is general purpose psychological measurement software that emphasizes ease of use and is suitable for most cognitive, social and neuropsychological experimental paradigms. Teaching Format Lectures and practical sessions. Assignments and exams for the programming languages covered. All materials will be made available via a course-dedicated website and/or blackboard. Lectures Mon hrs. room A102 Practical sessions Mon through hrs. room A106 Tue through hrs. room A106 Wed through hrs. room A106 Fri through hrs. room A106 Thu hrs room A106 Examination Dates Fri hrs. room A106 Special conditions: The course covers 4 weeks of full-time study. 313

22 Psychometrics 6 ec RM RM12XY Semester Semester 1, period 2 Prerequisites Bachelor of Psychology, Latente Variabele Modellen (BM30), Modern Test Theory (RM17) or Mathematical Statistics (RM09) Prof. dr. G.K.J. Maris Information G.K.J.Maris@uva.nl The course provides an introduction to the concepts, theory, and practice of statistical inference for psychometric measurement models. Psychometric measurement models are used to - take decisions about individual students; - evaluate the relationship between ability and student characteristics in survey type research; and - monitor student progress. Using the Rasch model as simple example different estimation and model evaluation methods will be introduced, suitable for the different measurement contexts, and implemented in a computer program. The focus will be on tests consisting of (at most) two items. This unrealistically simple case (the Rasch model and a two item test) serves to illustrate the essential features and can readily be generalized to more complex and realistic contexts as well as to more complex measurement models. Teaching Format Lectures and practicals. Take home exam (75% of final score) and computer program (25% of final score). To be announced on Blackboard. Fri through hrs. room JK B88 Fri through hrs. room A

23 Scientific Writing and Presenting 6 ec RM15 Semester Semester 1, period 1 Prerequisites Bachelor of Psychology Dr. E.-J. Wagenmakers Information EJ.Wagenmakers@gmail.com To improve your scientific communication skills. When you are not able to write a compelling article or give an exciting talk, your academic career will be a constant uphill battle -- no matter how excellent your ideas and research might be. As a scientist, you need to be able to explain your work to other scientists, and do this clearly, effectively, and convincingly. Despite the fact that good communication skills are of crucial importance (both inside and outside of academia), very few courses in the Dutch educational system teach proper scientific communication. As a result, students are left to improve their communication skills largely by themselves, through trial and error. This is unfortunate, because anybody s writing skills can be considerably improved with only a little instruction. In this course, we will cover the basics of scientific communication by means of weekly plenary sessions, exercises and small-scale feedback sessions. All teaching materials are tailored to the needs of a psychological researcher in action. After completing this course, you will be on your way to becoming a better writer, a more confident presenter, and, perhaps, a better scientist as well. Teaching Format Plenary lectures, individual and group exercises, self-study, feedback sessions. To be announced on Blackboard. Williams, J.M. (2010). Style: Lessons in clarity and grace (10th ed.). New York, NY: Pearson Longman. ISBN ,95. Wed hrs. room A306 Wed through hrs. room A312 Wed through hrs. room A306 Examination Dates To be announced on Blackboard. 315