Abstract
In this lecture we will argue that the randomized versus quasi-experiment is a false dichotomy because each design falls on a continuum of control
for confounding that can lead to spurious observed relationships. To
define the continuum, we quantify the extent of statistical control in
terms of the Frank's (2000) impact of confounding variable. Then we
evaluate the degree of statistical control by comparing the impact of
covariates on estimates of interest against the expected impacts under
randomization. That is, we use theoretical randomization as a baseline
for evaluating the effective control of any study instead of using a
single randomized empirical study as a gold standard against which to
compare others. Ultimately, we will report that the quasi-experiment in
Hong and Raudenbush (2005) crosses the threshold for equivalence with a
theoretical randomized study. We then develop a general formula and
guidelines for equating quasi-experiments and randomized experiments
based on the degree of statistical control achieved. In the discussion
we emphasize the validity of causal inferences from quasi-experiments
and the false dichotomy between quasi- and randomized experiments.
References
Abstract
When the goal of inference is estimating causal effects, we usually have to face problems related to how data are observed. In observational studies the most relevant of such problems is the fact that assignment to treatment is not under the control of the investigator; in addition some studies, both observational and experimental, may be affected by different sorts of post-treatment selection of observations due to, e.g., non-response, truncation or censoring `due to death'. All such complications require to somehow control for them, but the use of the standard statistical conditioning is improper. A relatively recent approach to deal with post-treatment complications is Principal Stratification, as first defined
by Frangakis and Rubin (2002) within the framework of the Rubin
Causal model and applied mainly in experimental studies. The framework
appears to be a very general one, that can be applied in various
contexts. We consider a specific post-treatment complication that may arise in both randomized and observational studies, namely the problem of nonignorable nonresponse on an outcome variable. By exploiting Principal
Stratification, we analyze and propose identification strategies
with and without the availability of an instrumental variable for nonresponse.
As a motivating example we consider a simplified evaluation study in the field of financial aids to firms, where typically missingness on the outcome variables, such as variables related to
firms' performances, can rarely be assumed missing at random.
References
Abstract
Meta-analysis may be broadly defined as the quantitative review and synthesis of the results of related but independent studies. In a meta-analysis the interest does not always concern only one specific outcome measure. Sometimes the focus is on the combination of several outcome measures that are presented in the individual studies, for instance, when there are more treatment groups or more outcome variables. When the summary data per study are multi-dimensional, the data analysis usually is restricted to a number of separate univariate analyses, i.e., one analysis per outcome variable. However, such univariate analyses neglect the relationships between the multiple outcome measures. In a multivariate meta-analysis all outcome measures are analysed jointly, therefore also revealing information about the correlations between the multiple outcome variables.
In this presentation I will show some clinical applications of multivariate meta-analysis.
References
Abstract
Rank data typically arise in two different settings. Firstly, as ranking of a small number of items produced independently by a larger number of subjects. An example is patient preferences. In the second setting the continuous measurements on a group of subjects are converted into rank ordering before analyzing the data, for example in order to apply the Wilcoxon ranksum test. In both situations an explicit probability model for ranks would be desirable as a starting point for statistical inference. An important example of such a model is the proportional hazards regression model of Cox. It is much used for analysis of survival data but rarely as a general method for nonparametric regression. This is, at least partially, due to lack of invariance of the model under changing the sign of
all observations (reversing the ranking). In the context of the first setting the Cox model is also known as a forward-selection model.
In the presentation I will briefly review properties and relative merits of existing models for rank data and then introduce a new model and illustrate its use.
Abstract
Studying short-term dynamic processes and change mechanisms in interaction yields important knowledge that contributes to understanding long-term social development of children. In order to get a grip on this short-term dynamics of interaction processes, we made a simulation model of dyadic interaction of children during one play session, which is inspired by dynamic systems principles (Thelen & Smith, 1994; Van Geert, 1994). The central aim of this model is to generate patterns of interaction that correspond with observed interaction patterns in children of different sociometric statuses, which have been observed in our empirical study. In this study, three types of dyads were formed, comparable with the dyads distinguished in the model. The dyads consisted of two same-sex grade 1 pupils, of whom one child had either a rejected, a popular, or an average status, and in which the play partner always had an average status.
The theoretical components of the model comprise children’s goal-directedness of actions, concerns, emotional appraisals, social power, and social effectiveness. The model’s output consists of simulations of children’s emotional expressions and actions over every second of a play session, of three groups of dyads of different sociometric statuses. In this presentation, I will go into the empirical validation of the model and the methods needed for such validation. It focuses on the model’s predictions of averages and distributions of the major variables and on the model’s sensitivity. Overall, the model fits the empirical data well. An exception is the lesser fit of the ‘popular’ group of dyads, which is explained by the limited use of social effectiveness in the model. In the discussion, I will reflect - among others - on the implication of our findings for using this type of simulation models in the field of research on social development.
References
Abstract
When parametric and semiparametric methods fail to capture the shape of the conditional hazard rate, nonparametric methods are a useful alternative. This paper proposes a new nonparametric estimator for the conditional hazard rate, which is defined as the ratio of local linear estimators for the conditional density and survivor function. We show that the resulting hazard rate estimator is pointwise consistent and asymptotically normal distributed under appropriate conditions. Furthermore, we derive plug-in bandwidths based on normal and uniform reference distributions, which minimize the asymptotic mean squared error. The new estimator performs competitively in terms of mean squared error in comparison to existing estimators for the hazard rate. Moreover, its smoothing parameters are relatively robust to misspecification of the reference distributions, which facilitates bandwidth selection. Additionally, the new hazard rate estimator is conveniently calculated using standard software for local linear regression. We illustrate the use of the local linear hazard rate in an application to kidney transplant data.
Reference
Abstract
We study the nonparametric maximum likelihood estimator (MLE) for current status data with competing risks. Current status censoring occurs when the variable of interest is not observed directly, but only known to lie before or after a certain time. Current status data with competing risks arise naturally in cross-sectional survival studies with several failure causes, and generalizations arise in HIV vaccine clinical trials.
Until now, the large sample properties of the MLE have been mostly unknown. We resolve this issue by proving consistency, the rate of convergence, and the limiting distribution of the MLE. These asymptotic properties are nonstandard, due to the censoring. The rate of convergence is slower than usual, and the limiting distribution involves a new self-induced process. I will illustrate this process in an example.
References
Abstract
Inference from data to a population traditionally proceeds by null hypothesis testing. This tells whether the data support the hypothesis that in the population some quantity is zero (a difference between means, a correlation, an indicator of growth over time). However, this is seldom the focus of interest for the researcher who collected the data. Researchers tend to be more interested in the alternative situation in which the null hypothesis fails to hold. Usually the alternative hypothesis is uninformative, e.g. ‘the means at our four time points are not all equal’, although researchers often possess informative and competing hypotheses, e.g. ‘the means are increasing from time point 1 to 4’ or ‘the means at time points 1 and 4 are smaller than at time points 2 and 3’. This presentation addresses researchers who want to evaluate informative hypotheses.
Point of departure is that adequate statistical tools should be available to researchers who have informative hypotheses (prior knowledge) in the form of hypothesized order relations between statistical parameters. Such knowledge may come from theories, earlier research expertise, or difference of opinion with colleagues. Bayesian model selection applied to order-restricted alternatives has recently become feasible when enough computing power became available for the required algorithms.
Two examples will be used to illustrate the approach proposed: order restricted analysis of variance, and order restricted models for the analysis of contingency tables. A potential drawback of the Baysian approach is the sensitivity with respect to the prior distributions that have to be specified. Supported by theoretical derivations, both examples will be used to discuss the prior sensitivity of the Bayesian approach proposed.
References
Abstract
Relatively much money is spent on setting up and carrying out promotions. In current research, mainly the effects of promotions on sales of a brand or product have been examined. It has been flagrantly and frequently established that promotions have a strong positive effect on sales in the short term. Relatively little is known, however, about the mechanisms of promotion's effectiveness, or the lack of it. Especially little has been published about underlying matters, like the evaluation and knowledge of consumers on promotions.
So the key question is: Why are sales promotions effective? The focus of the present study is on sales promotion, i.e., any temporary offer that is not available for the normal product, in the normal quantity, for the normal price and/or in the normal distribution. More specifically, the main research question of this study is: Which variables explain why consumers participate in promotions?
To answer this question a multilevel model will be estimated on panel data from the Trendbox company. Because of the multilevel model being used, two secondary questions are considered:
Abstract
A standard assumption in statistical causal inference is that the response of an outcome variable Y to an unconfounded cause X should not depend on how X is set to a particular value x. Unfortunately, in many realistic cases this assumption fails. For example, since Total Cholesterol is the sum of low density lipoproteins (LDL) that are bad for you, and high density lipoproteins (HDL) that are protective, then a manipulation in which Total Cholesterol was raised solely by raising LDL levels would not have the same effect as a manipulation in which Total Cholesterol was raised solely by raising HDL levels. In some cases, this ambiguity in a "manipulation" is harmless, in some cases not. In this talk I discuss the problem this presents for causal discovery and causal inference, and a foundational approach for how we might deal with it.
References
Abstract
Multilevel multivariate data occur in many forms. Examples are scores on a number of items collected from inhabitants within different countries, or NMR spectra of urine samples collected at a number of measurement occasions from different monkeys. To explore the relationships between the variables in this kind of data, the general framework of Multilevel Component Analysis can be used. The method is a hybrid between analysis of variance and principal component analysis (PCA). That is, the observed data are split into orthogonal parts, and they are analyzed separately by PCA, or variants thereof. In the case of two-level multivariate data, the results of an MLCA thus offer insight into both the between and within variability. MLCA can easily be adapted for modelling data resulting from an experimental design. Examples from various fields will be shown to illustrate the usefulness of the approach. Furthermore, some relationships between MLCA and alternative models, mainly in the structural equation modelling field, will be discussed.
References
Abstract
The Hunt for the Last Respondent has been inspired by concerns about the possibly detrimental effect of nonresponse on the accuracy of survey outcomes, as response rates are generally considered to be the most important criterion of survey quality, and the Netherlands is notorious for its low response rates. The study addresses a number of general questions, such as: Why are high nonresponse rates a reason for concern?; Who are less likely to respond, either because they are more difficult to contact or because they are more reluctant to cooperate?; How can response rates be enhanced?
Analyses of nonresponse on two surveys in which the SCP is involved, namely the Dutch Amenities and Services Utilisation Survey 1999 and its follow-up survey among persistent refusers, and the European Social Survey 2002/2003, aim at answering additional questions, such as: How to study nonresponse?; Do enhanced response rates improve the accuracy of survey outcomes?; How to combat nonresponse error and allocate funds effectively? The study shows that specific groups in society may be hard to contact and less willing to cooperate in surveys. This can result in bias when the determining factors of survey participation are directly related to the subject of a survey. Nonresponse rates can be enhanced substantially, but enhancing response rates does not always improve the accuracy of survey outcomes. The study recommends to spend a part of the funds for data collection on obtaining information about final nonrespondents as this is more effective than raising the response rate by a few percent.
References
Abstract
In many applications, social networks are not static but evolve over time. This can be due to purely structural, network-endogenous mechanisms (like reciprocity or transitivity), but also due to individual characteristics of the actors in the network (what is a relevant choice of actor characteristics will depend on the type of relation – one may think of gender, age, habits, substance use and other health behaviors, political preferences, etc.). Changeable individual characteristics, in turn, are often mediated by social networks. Processes of social influence, contagion, or group differentiation, all depend on the social network as their ‘structural substrate’. Examples are smoking initiation among adolescents – and other substance use and abuse –, the formation of attitudes and norms, the dynamics of fads, collaboration in organisations, etc. This mutual interference between network dynamics and the dynamics of changeable actor characteristics, together with the already complex interdependence structure that characterizes social networks in general, poses a statistical challenge. In principle, the collection of longitudinal (panel) data on networks and individual characteristics allows for separating effects in both causal directions on empirical grounds.
Since recently, statistical methods have been developed to analyze the dynamics of social networks, and also the simultaneous and interrelated dynamics of social networks and the behavior of the actors in the network. These methods are based on stochastic microsimulation models representing the dynamics of a relational network in a set of actors. Associated to these models are procedures for parameter estimation and testing, using Markov chain Monte Carlo methods. These procedures are implemented in the program SIENA. Researchers who have been active in developing these statistical methods include, in addition to the author, Marijtje van Duijn, Mark Huisman, Johan Koskinen, Michael Schweinberger, and Christian Steglich. A review will be given of the work on this methodology and some applications.
References
Abstract
A simple psychometric measurement model without latent
variables. It requires that after possible rescaling, measures
for the same construct have the same associations to variables
in the nomological net around the construct and are measured on
the same scale. This exchangeability model is equivalent to
factor analysis and Rasch models lacking latent variables
assumptions. The model does not assume local independence and is
completely void of causal connotations.
References
Abstract
Science infers general statements and predictions from limited
bodies of empirical evidence, and it therefore faces the problem
of induction. Statistics plays an important role in how science
solves this problem. In my talk I first make precise what role
it plays, and then investigate the extent to which, in this
role, it can support the realist ambitions of science.
The first task involves a critical analysis of the logical
empiricist views of Carnap, and a reformulation of inductive
inferences as Bayesian logical arguments. The second involves a
reversed application of De Finetti's representation theorem, and
a rather delicate mix of his strict subjectivism with the
frequentist theory. However, these reform measures do not yet go
far enough. In the last part of the talk will argue that
scientists have good reasons for employing underdetermined
statistical models.
References
Abstract
The delay in language and arithmetic achievements of school children
in Frisian education can be ascribed in part to the lower
quality of primary schooling in Fryslân. An example is the
finding that Frisian schooling lags behind as far as pupil
counselling is concerned. Moreover, it has been shown that
Frisian teachers remain behind in didactical skills and they
devote less teaching time to the subject of arithmetic than in
the primary schools in Limburg (Van Ruijven, 2003; 2004).
These are the most prominent conclusions drawn in the study into
Frisian education. The study initially focusses on the
educational level of the school children in Frisian primary
education. In this respect, educational achievements of pupils
in grade 7 in Frisian primary education have been compared with
the national mean and with educational achievements in
comparable provinces. Next to these analyses, the explanatory
question has been put forward. In view of the explanation of the
lower educational achievements in Fryslân, the results of the
Frisian pupils have been analysed in relation to features at
individual and school level. Regarding these explanatory
analyses, the data of the pupils and schools in the comparable
provinces have been used as a reference point as well.
In this presentation the design of the
study and its analyses will be central points of interest.
Firstly, I will pay attention to the selection of the reference
provinces. Which criteria have been applied in selecting the
reference provinces and how has the correspondence between the
provinces been determined?
In the second part I will concentrate
on the design of the analyses concerning the explanatory
research question. With an eye to the nested structure of the
data, the multi-level technique has been applied. A special
characteristic of the design followed is that a second
technique, cluster-analysis, has been integrated into the
multi-level design. Which opportunities do both techniques offer
to educational research? And did it work to explain the
interprovincial achievement differences this way?
References
Abstract
A critical treatment of some fundamentals of a book by Jaynes
(2003) is given. By `inference' Jaynes simply means: deductive
reasoning whenever enough information is at hand to permit it;
inductive or plausable reasoning when, as is almost invariable
the case in real problems, the necessary information is not
available. But if a problem can be solved by deductive
reasoning, probability theory is not needed for it.
Jaynes'
topic is the optimal processing of incomplete information.
If degrees of plausability are represented by real numbers, then
there is a uniquely determined set of quantative rules for
conducting inference. That is, any other rules whose results
conflict with them will necessarily violate an elementary and
nearly inescapable desideratum of rationality or consistency.
This gives a new perspective to the
foundations of probability theory.
Reference
Abstract
In this lecture we present an exploratory model-based clustering
approach for the analysis of large data sets. Basically, a
model-based cluster analysis (another name that is often used is
latent class analysis) searches for homogeneous groups of
persons that is groups of persons that give similar responses to
a set of items.
The approach addresses a number of practical problems
that often arise in exploratory model based cluster analysis of
large data sets: