1. Introduction and Overview. 2. Detecting Influential Observations and Outliers. 3. Detecting and Assessing Collinearity. 4. Applications and Remedies. 5. Research Issues and Directions for Extensions. Bibliography. Author Index. Subject Index.

Regression Diagnostics: Identifying Influential Data and Sources of Collinearity

Trust: The Social Virtues and the Creation of Prosperity

Part I. Understanding Secularization: 1. The secularization debate 2. Measuring secularization 3. Comparing secularization worldwide Part II. Case Studies of Religion and Politics: 4. The puzzle of secularization in the United States and Western Europe 5. A religious revival in post-communist Europe? 6. Religion and politics in the Muslim world Part III. The Consequences of Secularization: 7. Religion, the Protestant ethic, and moral values 8. Religious organizations and social capital 9. Religious parties and electoral behavior Part IV. Conclusions: 10. Secularization and its consequences 11. Re-examining the theory of existential security 12. Re-examining evidence for the security thesis.

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Sacred and Secular: Religion and Politics Worldwide

The Political Consciousness of Post-Industrialism@@@The Silent Revolution: Changing Values and Political Styles among Western Publics.

Peter Berger (1929) is an American sociologist best known for his collaboration with Thomas Luckman in writing The Social Construction of Reality: A Treatise in the Sociology of Knowledge. That book argues that human reality, and knowledge of it, is a social construct, emerging from the individual or group’s interaction with larger social structures (institutions). Social structures, once widely adopted, lose their history as social constructions (objectivation), and come over time, by the people who live within them, to be deemed natural realities independent of human construction (reification). Berger predicted, in his later book, The Sacred Canopy, near-term all-encompassing secularization of religion, which prediction has proved false, especially in the third world (as Berger himself has acknowledged in his later work, Desecularization).

神圣的帷幕 : 宗教社会学理论之要素 = The sacred canopy elements of a sociological theory of religion

Welfare States and Dimensions of Social Capital

Tests of hypotheses explaining the variation in church attendance are dominated by the use of international comparative survey data covering many countries with only a limited number of samples within these countries. As a result, the main research focus is on between country effects and hardly on within country effects. The latter, however, comprises a more convincing test, because fewer assumptions about unobserved country-specific variables are required. Elaborating on various analytical models, we show that results from a between country research design may lead to inaccurate conclusions. To illustrate this point, we selected the Mannheim Eurobarometer Trend File, which includes as many European countries on as many points in time as possible. Step by step we disentangle the well-known strong negative overall between country correlation of social security with church attendance. We show that this correlation most likely is owing to unspecified country characteristics, as within countries, social security is sometimes positively related to church attendance and sometimes negatively, whereas on average there is no effect at all. Rather than increases in social security spending, rising gross domestic product seems to reduce church attendance. Our cautionary tale about the use of between country research designs applies to other fields of research as well.

The between and within Effects of Social Security on Church Attendance in Europe 1980–1998: The Danger of Testing Hypotheses Cross-Nationally

This article explores an important property of the intrinsic estimator that has received no attention in literature: the age, period, and cohort estimates of the intrinsic estimator are not unique but vary with the parameterization and reference categories chosen for these variables. We give a formal proof of the non-uniqueness property for effect coding and dummy variable coding. Using data on female mortality in the United States over the years 1960-1999, we show that the variation in the results obtained for different parameterizations and reference categories is substantial and leads to contradictory conclusions. We conclude that the non-uniqueness property is a new argument for not routinely applying the intrinsic estimator.

https://repository.ubn.ru.nl/bitstream/handle/2066/139799/139799-1.pdf

The non-uniqueness property of the intrinsic estimator in APC models

Weighted effect coding refers to a specific coding matrix to include factor variables in generalised linear regression models. With weighted effect coding, the effect for each category represents the deviation of that category from the weighted mean (which corresponds to the sample mean). This technique has particularly attractive properties when analysing observational data, that commonly are unbalanced. The wec package is introduced, that provides functions to apply weighted effect coding to factor variables, and to interactions between (a.) a factor variable and a continuous variable and between (b.) two factor variables.

/pdf/weighted-effect-coding-for-observational-data-with-wec-1bac1cxf0i.pdf

Weighted Effect Coding for Observational Data with wec

Sweeney and Ulveling (1972) introduced weighted effect coding, where the estimates for categories of nominal and ordinal variables are deviations from the arithmetic mean, typically from a sample This somewhat neglected parameterization is preferred over the well-known effect coding (ANOVA) if the data are unbalanced (ie, when categories hold different numbers of observations) and was recently revived in this journal (te Grotenhuis et al 2016) In this paper, we show that weighted effect coding can also be applied to regression models with interaction effects The weighted effect coded interactions represent the additional effects over and above the main effects obtained from the model without these interactions This is a useful alternative to effect coding when the data are unbalanced as in most observational data In this contribution, we describe this novel parameterization and provide syntax, data, and examples in SPSS, R, and Stata on http://wwwrunl/sociology/mt/wec/downloads For didactical reasons we apply OLS regression models, but weighted effect coded interactions can be used in any generalized linear model Throughout this text we use the word ‘interaction’, while other researchers prefer ‘moderation’

Interactions between categorical variables


Dummy coded interaction 
When directional interaction hypotheses are tested and categorical (ie, ordinal or nominal scaled) predictor variables are involved, dummy coding is often appropriate In this parameterization the main effects relate to a particular subset of respondents and for the remaining subsets the dummy coded interaction effects reflect deviations from these main effects To create dummy coded interaction variables one has to multiply the original, 0/1 coded, dummy variables (Hardy 1993) As an empirical example we will investigate to what extent the mean BMI differs across three age categories in a group of respondents with one or more children and in a childless group (Umberson et al 2011) We use data on self-reported body length and weight, in three random samples (n = 3314) drawn from the Dutch population (aged 18–70) in 2000, 2005, and 2011 (Eisinga et al 2002, 2012a, b) We created the dummy coded variables Childlessdc with code 1 for respondents with no children and code 0 for respondents with one or more children, Middledc (code 1 for the middle-aged and 0 for both young and older respondents) and Olderdc (1 for older and 0 for both young and middle-aged respondents) The dummy coded interaction variables Childlessdc × Middledc, and Childlessdc × Olderdc are multiplications of these dummy coded variables (see Table 1 and our website for details) First, we estimated the main effects without interaction (see Table 4, Model 1) and second, we added the two interaction variables (Table 4, Model 2) Note that the reference categories (a) respondents with children, (b) youngsters, and (c) childless youngsters are omitted from the two models, which means that their estimates are set to zero 

Table 1

Coding scheme for the dummy coded main and interaction effects for the childless, middle-aged and older-aged (references/omitted categories are with children, young, and childless × young)

/pdf/a-novel-method-for-modelling-interaction-between-categorical-58ml6cflx4.pdf

Manfred te Grotenhuis

Papers

Welfare States and Dimensions of Social Capital

The between and within Effects of Social Security on Church Attendance in Europe 1980–1998: The Danger of Testing Hypotheses Cross-Nationally

The non-uniqueness property of the intrinsic estimator in APC models

Weighted Effect Coding for Observational Data with wec

A novel method for modelling interaction between categorical variables