**Genre**:** **Paper collection

**Editors**:** **Herman J. Adèr and Gideon J. Mellenbergh

**First Edition**:** **2015

**Softcover ISBN** 97-890-79418-34-3

**Price**:** **€10,- ($ 11,-)

**eBook: ISBN **97-890-79418-38-31

**Price**:** **€9,- ($ 10,-)

**Website**:** **www.jvank.nl/ARMSelected2014

**Contents **

Advising on research methods: Selected topics 2014 results from a research master course Methodological Advice that was given at the University of Amsterdam at the end of 2014 by Gideon J. Mellenbergh and Herman J. Adèr.

The course had the same format as the course given in 2013.

The objectives of the course were: (a) to acquire methodological knowledge that is needed for advising researchers in the behavioral and social sciences, and (b) to get experience with methodological consultancy.

The main material for the course was the book:

*Advising on research methods: A consultant’s companion*

by Herman J. Adèr and Gideon J. Mellenbergh (with contributions by David J. Hand).

The students had to fulfill various assignments, one of which was to write a paper on a topic that may come up during methodological consultancy.

In the beginning of the course, paper topics were selected from a long list of relevant methodological issues.

The publication process of drafting, submitting, reviewing, adapting and correcting was the same as in the production of any other edited paper collection.

Eleven students participated in the course. They made the following contributions to the book:

**Eline J. S. S. Tan and Bianca Westhoff**

discuss the use of **unobtrusive measurements** in behavioral and social science research. Unobtrusive measurements are procedures where participants are unaware of being measured. Four types of unobtrusive measurements are distinguished: simple observations, contrived observations, physical traces, and archives. The authors describe the advantages, disadvantages, and ethical aspects of unobtrusive measurements. The advantage of unobtrusive measurements is that they are not vulnerable to respondent biases, such as, social desirable answering questions. Disadvantages are, for example, that the reliability and validity of unobtrusive measurements are often hard to assess, and that ethical constraints may occur. The authors recommend to supplement obtrusive measurements with unobtrusive ones if that is possible.

**Boris Stapel and Ruben J. J. van Beek **discuss the influence of a third variable on the relation between two other variables. The authors distinguish three types of third variables: **confounders, moderators, and mediators**. They describe the status of each of these three variables. Moreover, they discuss statistical methods to test moderator and mediator effects. Finally, they bring together questions that are often asked on confounders, moderators, and mediators, and give short answers to these questions.

**Yoram K. Kunkels and Pia Tio
**give an introduction to

**network analysis to visualize the structure of psychological concepts**. The article starts with an introduction to network analysis. Subsequently, network analysis is demonstrated by an example: An analysis of test takers’ responses to items that measure the Big Five personality traits. Moreover, the article mentions the association measures and software to apply network analysis in practice.

**Maria C. (Riëtte) Olthof
**addresses

**the analysis of clustered data**, for example, achievement test scores of students within school classes. Clustering of data violates the independence assumption that is often made in statistical methods. She describes the strong negative effects that clustering can have on the Type I errors of statistical tests. She discusses simple methods to correct for the effects of clustering. Moreover, she gives an impression of multilevel analysis for the analysis of clustered data. She compares methods to analyze independent data with simple and multilevel methods to analyze clustered data using language test scores of students nested within classes. The example shows large differences between conclusions drawn from the analysis of clustered data with and without taking into account the clustered nature of the data.

**Noor Seydel and Martina van Slooten
**address the topic of

**equivalence null hypothesis testing**. Equivalence null hypothesis testing applies to the situation where researchers want to ascertain that variable means of different groups of study participants are approximately the same. They discuss the construction of an equivalence interval, which is the interval where researchers consider the difference between two means negligible. Moreover, the authors describe how an equivalence null hypothesis is tested. The article ends with a tutorial that demonstrates how equivalence null hypothesis can be applied in behavioral research.

**Quentin F. Gronau and Tom H. Oreel
**introduce readers into

**Bayesian inference and the inclusion of prior information into Bayesian analysis**. The article starts with a short introduction into Bayesian concepts and methods, and the inclusion of prior information. The article shows the steps to update prior information, and methods to study hypotheses. The methods are demonstrated by data of studies on suicide attempts and deaths. The example shows that a Bayesian analysis can yield other conclusions than a conventional (non-Bayesian) analysis.

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