scholarly journals What does it mean to feel loved: Cultural consensus and individual differences in felt love

2017 ◽  
Vol 36 (1) ◽  
pp. 214-243 ◽  
Author(s):  
Saeideh Heshmati ◽  
Zita Oravecz ◽  
Sarah Pressman ◽  
William H. Batchelder ◽  
Chelsea Muth ◽  
...  

Cultural consensus theory is a statistical framework (CCT) for the study of individual differences in the knowledge of culturally shared opinions. In this article, we demonstrate how a CCT analysis can be used to study individual differences and cultural consensus on what makes people feel loved, or more generally any social behaviors that are governed by cognitive schemata. To highlight the advantages of the method, we describe a study in which people reported on their everyday experiences of feeling loved. Our unique approach to understanding this topic is to focus on people’s cognitive evaluations on what feeling loved (both romantically and nonromantically) entails by exploring the shared agreement regarding when one is most likely to feel loved and the individual differences that influence knowledge of these shared agreements. Our results reveal that people converge on a consensus about indicators of expressed love and that these scenarios are both romantic and nonromantic. Moreover, people show individual differences in (1) the amount of knowledge they have about this consensus and (2) their guessing biases in responding to items on love scenarios, depending on personality and demographics—all conclusions made possible by the CCT method.

2020 ◽  
Author(s):  
Don van den Bergh ◽  
Eric-Jan Wagenmakers

In many forensic psychiatric hospitals, patients’ mental health is monitored at regular intervals. Typically, clinicians score patients using a Likert scale on multiple criteria including hostility. Having an overview of patients’ scores benefits staff members in at least three ways. First, the scores may help adjust treatment to the individual patient; second, the change in scores over time allows an assessment of treatment effectiveness; third, the scores may warn staff that particular patients are at high risk of turning violent, either before or after release. Practical importance notwithstanding, current practices for the analysis of mental health scores are suboptimal: evaluations from different clinicians are averaged (as if the Likert scale were linear and the clinicians identical), and patients are analyzed in isolation (as if they were independent). Uncertainty estimates of the resulting score are often ignored. Here we outline a quantitative program for the analysis of mental health scores using cultural consensus theory (CCT; Anders & Batchelder, 2015). CCT models take into account the ordinal nature of the Likert scale, the individual differences among clinicians, and the possible commonalities between patients. In a simulation, we compare the predictive performance of the CCT model to the current practice of aggregating raw observations and, as an alternative, against often-used machine learning toolboxes. In addition, we outline the substantive conclusions afforded by the application of the CCT model. We end with recommendations for clinical practitioners who wish to apply CCT in their own work.


Field Methods ◽  
2018 ◽  
Vol 30 (3) ◽  
pp. 241-257 ◽  
Author(s):  
Michael G. Lacy ◽  
Jeffrey G. Snodgrass ◽  
Mary C. Meyer ◽  
H. J. Francois Dengah ◽  
Noah Benedict

The most widely used formal approach to culture, the cultural consensus theory (CCT) of Romney, Weller, and Batchelder, originally relied on a priori definitions of cultural groups to map their unity and diversity. Retaining key features of classical CCT, we provide techniques to identify two or more cultural subgroups in a sample, whether those groups are known in advance or not. Our method helps CCT practitioners connect to contemporary approaches to culture in anthropology and related disciplines, which emphasize complexity. We suggest that our method provides reasonable and easily implementable approximations of cultural unity and diversity within a sample. In pursuing these matters, we contribute to other ongoing efforts to bring CCT closer to contemporary theorizing on cultural multiplicity, thus rendering CCT potentially more useful to a wider range of practicing social scientists.


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