Full Schedule
Please note that this schedule is current as of 2018-07-16. Further changes may occur.
Tuesday, July 24
09:00
Herbert Hoijtink
09:00 - 13:00
IAAC SR1
Yves Rosseel
09:00 - 13:00
IAAC SR2
Noémi Schuurman
09:00 - 13:00
IAAC SR3
14:00
Herbert Hoijtink
14:00 - 18:00
IAAC SR1
Yves Rosseel
14:00 - 18:00
IAAC SR2
-
Rolf Steyer
14:00 - 18:00
void NA
NA
18:00 - NA
19:00
event Welcome Reception
Cafeteria Zur Rosen
NA
19:00 - 22:00
Wednesday, July 25
09:00
opening Opening Ceremony
09:00 - 10:00
Steigenberger Saal Friedrich Schiller
10:15
Replication is a fundamental aspect of the scientific method and is central to the rhetoric of science. Yet recent empirical research has called into question the replicability of experimental research in fields as diverse as economics, medicine, and psychology. This work undermines the credibility of science and the evidence science provides. Surprisingly, there has been little research on the methodology of replication itself, including the design of replication studies and appropriate statistical analyses to determine whether a set of studies replicate one another. Perhaps as a result, some recent programs of research on replication have used multiple, and sometimes mutually contradictory, methods to study replication. This talk will draw on a meta-analytic perspective to formalize ideas about the definition of replication and the analysis of replication studies. I will focus on three problems that seem straightforward, but will argue that each of them is more complex than it first appears. One is the precise definition of replication: What exactly does it mean to say that the results of a set of studies replicate one another? The second is the statistical analysis of replications: Given a definition of replication, what statistical analysis is appropriate? The third is the design of replication studies: What kind of ensemble of two or more studies should we assemble to evaluate whether results replicate?
Larry Hedges
Heinz Holling
10:15 - 11:00
Steigenberger Saal Friedrich Schiller
11:00
coffee Coffee Break
11:00 - 11:30
11:30
session Causal Inference in Dynamic Models
Christian Gische
11:30 - 12:30
Steigenberger Saal Friedrich Schiller
Issues in Causality in Discrete-Time and Continuous-Time Stochastic Process Models
Julia Gantner
Rolf Steyer
An Interventionist Approach to Causal Inference Based on Panel Data
Christian Gische
Manuel Voelkle
A General Nonlinear Model for the Identification of Mediators Without the No Confounder Assumption
Holger Brandt
session Item Response Theory
Frans Kamphuis
11:30 - 12:50
Steigenberger Salon Schlegel
A State-Space Approach for Student Growth Percentile Estimation
Frans Kamphuis
Ron Engelen
Psychometric Evaluation of the d2 Test of Sustained Attention With the Rasch Poisson Counts Model
Purya Baghaei
Mahsa Nadri
Forecasting Clinical Outcomes by Combining Measurement and Prediction Models for Health Evaluations
Niels Smits
Exchanging Selection Rules from Cognitive Diagnosis Modeling to Traditional Item Response Theory
Miguel A. Sorrel
Juan Ramón Barrada
Jimmy de la Torre
session Applied Statistics
Patricia Martinkova
11:30 - 12:30
Steigenberger Salon Hölderlin
Model-Based Reliability to Check for Disparities in Ratings of Internal and External Applicants
Patricia Martinkova
Dan Goldhaber
Elena Erosheva
Questionable Research Practices in Student Theses - Prevalence, Antecedents and Implications
Anand Krishna
Sebastian Maximilian Peter
Impact of Formal Educational Upgrading on the Likelihood Leaving Unemployment
Sushant Pandkar
Johannes Jaenicke
session Replication Crisis
Jörg Blasius
11:30 - 12:10
Steigenberger Salon Novalis
Fabrication of Interview Data in PISA and PIACC
Jörg Blasius
Transparency and Replicability in Cross-National Survey Research
Elena Damian
Bart Meuleman
Wim Van Oorschot
13:00
lunch Lunch Break
13:00 - 14:00
14:00
14:00 - 15:30
15:30
Incidental data are data that people produce incidentally, as a byproduct of the normal course of operations of a platform, business, or government. Well-known examples include using Twitter, Facebook, Google search, smartphones, badges, etc. to study social phenomena, such as election behavior, attitudes, employment, or consumer confidence. It has been almost ten years since various high-impact papers and books have proclaimed the end of traditional social research and the beginning of a new era of exciting new possibilities for social science.
Daniel Oberski
Axel Mayer
15:30 - 16:00
Steigenberger Saal Friedrich Schiller
Tests represent the evaluation technology most widely used by psychologists in their professional practice. In recent years there have been great advances in psychological evaluation, which have affected both the tests themselves and their use. In this presentation, recent advances in the construction and use of tests are reviewed, and some future challenges discussed. This review is structured around six dimensions of change: the evolution of psychometric models, changes in the technology used, developments in the construction of items, estimation of reliability, conceptualization of validity, and use of tests in professional practice. Finally, some future perspectives are discussed, taking into account the great impact of new information technologies on the evaluation methods, tests included.
José Muñiz
Ana Hernandez
15:30 - 16:00
Steigenberger Salon Schlegel
16:00
coffee Coffee Break
16:00 - 16:30
16:30
session Latent Variable Analysis
Tobias Koch
16:30 - 17:50
Steigenberger Saal Friedrich Schiller
Analyzing Different Types of Moderated Method Effects in Confirmatory Factor Models for Structurally
Tobias Koch
Augustin Kelava
Michael Eid
Repeated Measures ANOVA with Latent Variables Using the Latent Growth Component Approach
Benedikt Langenberg
Axel Mayer
DIF of Self-Assessment Items Across Different Levels of a Latent Variable: Positive Affect
Ana Hernández
Vicente González-Romá
Inés Tomás
Parameter Associations in Bivariate Dual Change Score Models: Implications for Simulation Studies
Holly O'Rourke
Kevin Grimm
session Multilevel Analysis
Johannes Hartig
16:30 - 17:50
Steigenberger Salon Schlegel
Estimation of Random Group DIF Using Two- and Three-Level GLMMs
Johannes Hartig
Carmen Köhler
Alexander Naumann
Multilevel Models for Evaluating the Effectiveness of Instruction: ANCOVA vs. Change-Score Approach
Carmen Köhler
Johannes Hartig
The Optimal Design of Cluster Randomized Trials With Outcomes at Individual and Cluster Level
Mirjam Moerbeek
Comparative Performance of Single Trial Multilevel Analyses of Event-Related Brain Potentials
Juan Carlos Oliver-Rodríguez
Mirjam Moerbeek
session Structural Equation Modeling
Keith Widaman
16:30 - 17:50
Steigenberger Salon Hölderlin
Unreliability Has Important Negative Effects: Correcting May Be Easier Than You Think
Keith Widaman
Multilevel SEM for Discrete Data With the Pairwise Likelihood Estimation Method
Mariska Barendse
Yves Rosseel
An Alternative Estimation Method for Multilevel SEM Based on Factor Scores
Ines Devlieger
Yves Rosseel
Omitted Cross-Loadings in Nonlinear SEM: A Monte Carlo Study
Karina Rdz-Navarro
Karin Schermelleh-Engel
symposium New Developments in Mokken Scale Analysis
Andries van der Ark
16:30 - 17:50
Steigenberger Salon Novalis
Introduction to Mokken Scale Analysis
Klaas Sijtsma
Checking Assumptions in Two-Level Mokken Scale Analysis
Letty Koopman
Two-Level Mokken Scale Analysis: The State of the Art
Andries van der Ark
Using Mokken Scaling Techniques to Evaluate Educational Assessments
Stefanie Wind
Thursday, July 26
08:30
Traditionally, the majority of studies investigating the effectiveness of interventions focused on the average effect, but there is much more to learn about the effects of a treatment or an intervention. Researchers are for example interested in heterogeneity of effects, in subgroup effects, or in conditional effects given values of one or multiple covariates. In addition, there is interest and need for visualizing the effects of interventions and in making the key findings from intervention studies usable for selecting the best treatment for a concrete person. In personalized medicine and related fields, attention is shifting towards estimation of interindividual differences in effects and there is a variety of statistical approaches that can be used for this purpose. However, the investigation of conditional effects and interindividual differences in the effects is particularly challenging in the social and behavioral sciences, because many constructs-of-interest such as depression or anxiety are latent variables. In addition, the selection of variables for the analysis is crucial, and modeling conditional effects oftentimes requires interactions and potentially non-linear relationships. In this keynote, I will bring together concepts from the causal inference literature and from structural equation modeling to allow researchers to gain a deeper understanding of the effectiveness of interventions based on latent variable models. I will use causality conditions and effect definitions from the causal inference literature and show how multigroup structural equation models with stochastic group sizes can be used to estimate the effects of interest in experimental and quasi-experimental studies. The new approach (and the accompanying open source R package) is termed EffectLiteR approach Several empirical examples from psychology and educational science are used to illustrate the proposed approach. Furthermore, I will show that many popular methods like ANOVA or moderation analysis are special cases of the general multigroup SEM approach for analyzing treatment effects. A SEM-based approach also has the advantage that many recent advancements that have been made in this area, like robust estimators and standard errors, modern fit statistics, and measurement models can be used for estimating causal effects. Finally, I will show some extensions of the proposed model, namely how to include propensity scores in the analysis, how it can be extended to multilevel SEM approaches and how Bayesian non-linear structural equation models can be used to deal with latent interactions and non-normally distributed latent variables.
Axel Mayer
Rolf Steyer
08:30 - 09:15
Steigenberger Saal Friedrich Schiller
09:30
session Large Scale Data
Steffi Pohl
09:30 - 10:30
Steigenberger Saal Friedrich Schiller
Item-Person Mismatch and Parameter Recovery Accuracy in Sparse Multi-Matrix Booklet Designs
Anta Akuro
Martin Brunner
Steffi Pohl
Measurement Invariance of the Academic Performance for Fifteen Countries With the Alignment Method
Pamela Woitschach
Bruno Zumbo
Ruben Fernandez-Alonso
José Muñiz-Fernández
Treatment of Measurement Error and Missing Data Using Nested and Non-Nested Multiple Imputation
Simon Grund
Oliver Lüdtke
Alexander Robitzsch
symposium Differential Item Functioning (DIF) in Educational Settings: Methods, Simulations and Applications
Rudolf Debelak, Martin Tomasik
09:30 - 10:50
Steigenberger Salon Schlegel
A Regularized Moderated Item Response Model for Assessing Differential Item Functioning
Alexander Robitzsch
Oliver Lüdtke
Calibration of a Criterion-Referenced Computerized Adaptive Test in Higher Education
Hanna Köhler
Andreas Frey
Sebastian Born
Aron Fink
Christian Spoden
Johannes Bauer
Differential Item Functioning in the Context of Multistage Testing
Sebastian Appelbaum
Thomas Ostermann
Martin J. Tomasik
A Flexible Method for the Detection of Differential Item Functioning
Rudolf Debelak
Lennart Schneider
Achim Zeileis
Carolin Strobl
session Factor Analysis
Florian Scharf
09:30 - 10:50
Steigenberger Salon Hölderlin
Orthogonal Versus Oblique Rotation in Temporal EFA for Event-Related Potentials
Florian Scharf
Steffen Nestler
Common Factor Analysis and Principal Component Analysis: Competing Indeterminacies
Keith Widaman
The Number of Factors in Exploratory Factor Analysis
Max Auerswald
Morten Moshagen
On the Influence of Processing Speed on Investigations of Structural Validity: A Simulation Study
Karl Schweizer
session Applied Statistics
Thomas Schäfer
09:30 - 10:50
Steigenberger Salon Novalis
Cohen Revised: Empirical Redefinition of the Conventions for Interpreting Effect Sizes in Psychology
Thomas Schäfer
Introducing Indigenous Methodology Into the Practice of the European Social Research
Justyna Pilarska
The Impact of Test-Review Models on Improving Tests and Testing: The Case of Spain
Hidalgo M. Dolores
Hernández Ana
Neets in French Labour Market: A Multidimensionnal and Fuzzy Approach
Claire Bonnard
Jean-François Giret
Yann Kossi
11:00
coffee Coffee Break
11:00 - 11:30
11:30
session Bayesian Statistics
Herbert Hoijtink
11:30 - 12:50
Steigenberger Saal Friedrich Schiller
Computing Bayes Factors From Data With Missing Values
Herbert Hoijtink
Xin Gu
Joris Mulder
Yves Rosseel
Handling Ordinal Predictors in Regression Models via Monotonic Effects
Paul Bürkner
Emanuel Charpentier
Bayesian Estimation for Cases of Empirical Underidentification
Jonathan Helm
Operationalizations of Inaccuracy of Prior Distributions in Simulation Studies
Milica Miocevic
session Causal Inference
Rolf Steyer
11:30 - 12:50
Steigenberger Salon Schlegel
Average Effects Based on Regressions with Log Link: A New Approach with Stochastic Covariates
Christoph Kiefer
Axel Mayer
Ignoring Ignorability: Towards a Realistic Public Policy Evaluation
Trinidad Gonzalez
Ernesto San Martin
How to Model Production in Psychology? A Bayesian Stochastic Frontier Structural Equation Model
Rüdiger Mutz
Bias in Estimating Treatment Effects of Latent Non-normal Distributed Outcome Variables With Binary Indicators
Jan Ploetner
Rolf Steyer
symposium Response Time Modeling in Psychometrics
Steffi Pohl
11:30 - 12:50
Steigenberger Salon Hölderlin
Disentangling Missingness Due to a Lack of Speed From Missingness Due To Quitting
Esther Ulitzsch
Steffi Pohl
Matthias von Davier
Response Time Models for Automated Test Assembly
Benjamin Becker
Sebastian Weirich
Dries Debeer
Response Times and Latent Response Style Classes in Noncognitive Measures
Artur Pokropek
Lale Khorramdel
Matthias von Davier
A Finite-State Machine Approach to Extract Item Response Times From Questionnaire Item Batteries
Ulf Kroehne
Janine Buchholz
Frank Goldhammer
session Experimental Design
Volker Kraft
11:30 - 12:40
Steigenberger Salon Novalis
Statistical Power in Pooled Time Series
Marcel Elipe Miravet
Patricia Flor Arasil
Francisco Herrero Machancoses
Jesús F. Rosel Remírez
Pilar Jara Jiménez
Understanding (the) Power in Designed Experiments
Volker Kraft
Degrees of Freedom Approximations in Multilevel Meta-Analysis of Standardized Single-Case Experiment
Laleh Jamshidi
John M. Ferron
Mariola Moeyaert
S. Natasha Beretvas
Wim Van den Noortgate1
13:00
lunch Lunch Break
13:00 - 14:00
14:00
14:00 - 15:30
meeting Executive Committee Meeting
NA
14:00 - 15:30
Steigenberger Salon Schlegel
15:30
To better understand psychological mechanisms, researchers are increasingly capitalizing on longitudinal studies. The lead-lag structure and the possibility to disentangle between-person and within-person sources of variance, are two major assets of longitudinal (panel) data. However, how to best exploit this information in data analysis and interpretation? In this presentation, I want to identify several common problems and show how to avoid them by paying closer attention to the role of time. I will begin with a short example that illustrates some of the typical problems and questions faced by applied researcher and practitioners. Second, I will distinguish between static versus dynamic and discrete versus continuous time modeling approaches and discuss their advantages and disadvantages in the study of psychological mechanisms. Third, I will review different approaches to dealing with between-person differences, highlighting their dual role as a potential source of confounding as well as a source of information to improve estimation and causal inference. I will outline a possible way to better integrate information on between person differences and within person changes in the search for causal mechanisms in future research and end with a discussion of current problems and limitations.
Manuel Völkle
Noémi Schuurman
15:30 - 16:00
Steigenberger Saal Friedrich Schiller
state-of-the-art Optimal Design
Optimal design allows for estimating parameters of statistical models according to important optimality criteria, e.g., minimizing standard errors of estimators. Thus, optimal designs may considerably reduce the number of experimental units, such as respondents or items in empirical studies. For a long time, optimal design has not received much attention within psychology, but meanwhile interest for this subject is rapidly increasing as such designs are needed in large scale assessment, e.g. PISA, or for adaptive testing. In this presentation, first, fundamental principles of optimal design are introduced using well-known linear models, e.g. analysis of variance or multiple regression. The rationale of adaptive, Bayesian, and minimax designs needed for nonlinear models will then be outlined. Such designs are presented for fixed and random effects models, e.g. IRT models or growth curve models. Finally, two R packages for deriving Bayesian and minimax designs based on recently developed algorithms will briefly be demonstrated.
Heinz Holling
Mirjam Moerbeek
15:30 - 16:00
Steigenberger Salon Schlegel
16:00
coffee Coffee Break
16:00 - 16:30
16:30
session Structural Equation Modeling
Jonathan Helm
16:30 - 17:50
Steigenberger Saal Friedrich Schiller
Dust Yourself Off and Try Anew: Reproducing ANOVA Using SEM
Jonathan Helm
Dealing With Hypotheses That Depend on the Scaling of Latent Variables
Juliane Wilcke
Metric Measurement Invariance of Latent Variables: Foundations, Testing, and Correct Interpretation
Stefan Klößner
Eric Klopp
The Interpretation of Parameter Estimates in Structural Equation Models
Stefan Klößner
Eric Klopp
session Latent Variable Analysis
Heidelinde Dehaene
16:30 - 17:30
Steigenberger Salon Schlegel
Semiparametric Regression Models for Indirectly Observed Outcomes
Heidelinde Dehaene
Jan De Neve
Yves Rosseel
Impact and Dimensionality: The Performance of Logistic Regression in Differential Item Functioning
Hui-Fang Chen
Kuan-Yu Jin
Assessing Individual Change Without Knowing the Test Properties: Item Bootstrapping
Juan Botella
Desirée Blázquez
Manuel Suero
James F. Juola
session Item Response Theory
Sebastian Born
16:30 - 17:30
Steigenberger Salon Hölderlin
Comparing Fixed-Precision Multidimensional Computerized Adaptive Tests for Various Assessment Areas
Sebastian Born
Muirne Paap
Johan Braeken
Concurrent Adaptive Tests for Formative Assessments in School Classes
Daniel Bengs
Ulf Brefeld
Ulf Kröhne
The Dual Side of Classical Test Theory: The Geometry of the Axiom of Common Cause
Ernesto San Martín
session Applied Statistics
Patricia Flor Arasil
16:30 - 17:30
Steigenberger Salon Novalis
Measuring Intercultural Competence: Methodological Issues and Challenges
Maggie Yue Zhao
Irritability and Its Memory: A Time Series Model
Patricia Flor Arasil
Marcel Elipe Miravet
Pilar Jara Jiménez
Francisco Herrero Machancoses
Jesús F. Rosel Remírez
Use of Low Cost Tools in Ergonomic Research of Mobile Restaurants in Western India
Prabir Mukhopadhyay
Vipul Vinzuda
Thridev Suvarnan
Nakul Lakhtar
void NA
NA
18:00 - NA
19:00
event Conference Dinner
Paradiescafé Jena
NA
19:00 - 00:00
Friday, July 27
09:00
In longitudinal modeling, maximum likelihood estimators of model parameters are consistent if missingness depends only on the covariates and/or observed outcomes. Such missingness processes are hence ignorable. When missingness depends on unobserved outcomes or on the random effects in mixed effects/multilevel models, it is said to be not missing at random (NMAR) and is no longer ignorable. For such NMAR missingness, joint modeling of the outcomes and missingness has been advocated, but these approaches rely on strong, unverifiable assumptions, such as parameteric specification of the missingness process. In this talk, I will show that minimal assumptions about missingness, such as whether it depends on random effects or contemporaneous observed/unobserved outcomes, often allows us to make the missingness ignorable. In other words, we can obtain consistent estimates of (some) model parameters using standard estimators, a concept also referred to as "protective" estimation. Perhaps surprisingly, one approach is to simply discard more data. Another approach is to change the estimator, for example, by switching from a random-effects model to a fixed-effects model (Skrondal \& Rabe-Hesketh, 2014, Biometrika 101, 175-188). This is joint work with Anders Skrondal.
Sophia Rabe-Hesketh
Mirjam Moerbeck
09:00 - 09:45
Steigenberger Saal Friedrich Schiller
10:00
session Item Response Theory
Christoph König
10:00 - 11:00
Steigenberger Saal Friedrich Schiller
Reducing Sample Size Requirements of the 2PL With a Bayesian Hierarchical Approach
Christoph König
Christian Spoden
Andreas Frey
Instructional Sensitivity of Polytomous Test and Questionnaire Items
Alexander Naumann
Johannes Hartig
Stephanie Musow
Jan Hochweber
Estimation of a Multidimensional Item Response Model Using Bayesian Nonparametrics
Felix Naumann
session Missing Data
Mario Lawes
10:00 - 11:00
Steigenberger Salon Schlegel
Planned Missing Data Designs: Investigating the Efficiency of a Three-Method Measurement Model
Mario Lawes
Martin Schultze
Michael Eid
Handling Missing Data in Single-Case Experiments
Tamal Kumar De
René Tanious
Bart Michiels
Patrick Onghena
Testing Missingness for Continuous and Categorical Data
Serguei Rouzinov
André Berchtold
symposium Applying Subjective Bayes to Real Life Data
Fayette Klaassen
10:00 - 11:00
Steigenberger Salon Hölderlin
Including and Assessing Expertise via Prior Probabilities.
Fayette Klaassen
Duco Veen
Bayesian Approximate Measurement Invariance: When You Have Too Little or Too Much Data.
Kimberley Lek
Sonja Winter
Expert-Weighted Prior Information: Applications in Psychology and Veterinary Medicine
Haifang Ni
Mariëlle Zondervan-Zwijnenburg
Duco Veen
11:00
coffee Coffee Break
11:00 - 11:30
11:30
session Longitudinal Data
Manuel Arnold
11:30 - 12:50
Steigenberger Saal Friedrich Schiller
Individual Parameter Contribution Regression for Longitudinal Data
Manuel Arnold
Generalized Continuous Time Models and the Continuous Time Rasch Model as an Example
Martin Hecht
Katinka Hardt
Charles C. Driver
Manuel C. Voelkle
The Association Between Depression and Education in UK Adolescents: A Cross-Lagged Panel Analysis
José López-López
Rebecca Pearson
Liz Washbrook
Mina Fazel
Kate Tilling
Latent Markov Factor Analysis for Exploring MM Changes in Time-Intensive Longitudinal Studies
Leonie V.D.E. Vogelsmeier
Jeroen K. Vermunt
Kim De Roover
session Latent Variable Analysis
Jose-Luis Padilla
11:30 - 12:50
Steigenberger Salon Schlegel
On the Effect of Observations and Parameters on the fit of SEM Models With Large Sample-Sizes
Piotr Tarka
Evaluating Model Quality in Exploratory Bi-factor Modelling
Eduardo Garcia-Garzon
Francisco J. Abad
Luis E. Garrido
Analyzing Approximate Invariance From a Mixed-Method Ecological Approach to Validation
Jose-Luis Padilla
Isabel Benitez
Luis Manuel Lozano
Pablo Doncel
Catalina Estrada
Carolina Barrios
Alejandra del Carmen Domínguez
A Correlated Covariate Amplifies the Bias of a Fallible Covariate in Causal Effect Estimates
Marie-Ann Sengewald
Steffi Pohl
session Multilevel Modeling
Wouter Talloen
11:30 - 12:50
Steigenberger Salon Hölderlin
Measurement Error and Unmeasured Confounding in Multilevel Mediation Models
Wouter Talloen
Tom Loeys
Beatrijs Moerkerke
Same Same but Different?! Measuring of Local Sex Ratios
Andreas Filser
Richard Preetz
Assessing Structures of Prejudice in Europe with Multilevel Latent Class Analysis
Alice Barth
Detecting Selection Bias in Meta-Analyses with Dependent Effect Sizes: A Simulation Study
Belén Fernández-Castilla
Wim Van Den Noortgate
session Bayesian Statistics
David Kaplan
11:30 - 11:30
Steigenberger Salon Novalis
An Approach to Addressing Multiple Imputation Model Uncertainty Using Bayesian Model Averaging
David Kaplan
Sinan Yavuz
Bayesian Meta-Analysis of Studies Using Cohen's d in R
Sheri Kim
Hypothesis-Testing Demands Trustworthy Data – A Simulation Approach to Inferential Statistics Based on the Research Program Strategy
Frank Zenker
Antonia Krefeld-Schwalb
Erich H. Witte
13:00
lunch Lunch Break
13:00 - 14:00
14:00
session Item Response Theory
Timo Bechger
14:00 - 15:20
Steigenberger Saal Friedrich Schiller
DIF Methods in Dexter
Ivailo Partchev
Timo Bechger
The Great Dexperiment: Psychometrics With Observed Variables
Timo Bechger
Bayesian Estimation of Item Response Models to Account for Learning During the Test
José H. Lozano
Javier Revuelta
A Probabilistic IRT Model for the Joint Assessment of Objects and Persons in Fully Crossed Designs
Georg Hosoya
symposium Challenges in Interdisciplinary Research Methodology: The Study of Complex Systems
Hilde Tobi
14:00 - 15:05
Steigenberger Salon Schlegel
Using Simulation Models to Measure Resilience
Guus ten Broeke
A Modest Step Toward Bringing Unity in Interdisciplinary Research
Jarl Kennard Kampen
Innovation Modelling in Engineering and Scholastic Philosophy
Julia P. A. von Thienen
Chiara Paladini
Christoph Meinel
Mapping Validity in Modelling for Interdisciplinary Research
Hilde Tobi
Guus ten Broeke
session Latent Class Models
Ana Gomes
14:00 - 14:40
Steigenberger Salon Hölderlin
Internet Use in the European Union: A Multilevel Latent Class Analysis
Ana Gomes
José G. Dias
Using Latent Variable Models to Evaluate Test Quality Criteria of Tests Measuring Nominal Constructs
Hendryk Böhme
session Applied Statistics
Alrik Thiem
14:00 - 15:00
Steigenberger Salon Novalis
Interpretation of Main Effects for Moderated Regression Models
Julie Lorah
Topic Modeling As a Type-Forming Process of Social-Ecological Education Research
Thomas Prescher
Small Act, Huge Effect: Algorithmic Sources of Publication Bias in Political Science Research
Alrik Thiem
Tim Haesebrouck
15:30
Although once considered by some researchers to be on the fringe of conventional methodology, systematic observation has been progressively incorporated into diverse areas of psychological research. Psychological science is increasingly focusing on the study of everyday behavior, and studies applying systematic observation methodologies can now be found in mainstream interdisciplinary psychology journals (e.g., Frontiers in Psychology and Psicothema) and methodology journals (e.g., Behavior Research Methods and Quality \& Quantity). In this state-of-the-art lecture, I will discuss core aspects of systematic observation as a scientific method, with a focus on the profile of this approach and the specific processes it involves. Observational methodology is characterized by high scientific rigor and flexibility throughout its different stages and allows the objective study of spontaneous behavior in natural settings. The study of spontaneous behavior is characterized by a richness of information that can only be captured by video or sound recordings, without elicitation. Furthermore, the tools now available to explore this richness, often hidden within the deeper layers of the data, have been greatly enhanced by recent technological advances. Quantification in observational methodology is particularly robust and observational studies applying this methodology deserve consideration as mixed methods research. One particularly interesting area is the use of indirect observation of everyday behavior in natural settings based on textual material, such as conversations, blog posts, tweets, etc. This approach involves ‘liquefying’ original or transcribed texts into a format in which the original qualitative data can be quantified and analyzed using techniques based on the order or sequence of events rather than on traditional frequency counts. I will conclude this lecture by providing an overview of the broad range of applications of scientific systematic observation and highlight the possibilities of systematically studying spontaneous behavior in natural settings.
M. Teresa Anguera
Daniel Oberski
15:30 - 16:00
Steigenberger Saal Friedrich Schiller
Randomized experiments provide the strongest warrant for causal inference. However, randomized experiments assume full treatment adherence for a proper estimate of the causal effect. One form of treatment non-adherence is binary in which participants in the treatment group do not accept treatment. Biases resulting from binary treatment non-adherence using several traditional analysis approaches (e.g., per protocol analysis) are briefly illustrated. Newer approaches that compare the effect for those participants in the treatment group who received treatment with those participants in the control group who would have accepted treatment if offered are presented (average treatment effect on the treated). Another form of non-adherence is partial adherence in which participants in the treatment group receive only a proportion of the intervention (e.g., 5 sessions of a 10-sesssion intervention program). Confounder adjustment and instrumental variable approaches that provide estimates of the treatment effect conditional on the proportion of the full treatment that was received are presented. We discuss the assumptions of these approaches and the conditions under which they may be most useful. The usefulness of binary and partial adherence approaches as supplements to intention to treat analyses as an estimate of the causal effect will be discussed.
Stephen West
Steffi Pohl
15:30 - 16:00
Steigenberger Salon Schlegel
16:00
coffee Coffee Break
16:00 - 16:30
16:30
meeting EAM Members Meeting
NA
16:30 - 18:00
Steigenberger Saal Friedrich Schiller
Department of Methodology and Evaluation Research

University of Jena

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