What does it take to feel loved?
- Abstract
Cultural consensus theory (CCT) is a statistical framework that allows for the analysis of individual differences and the sharing of culturally related opinions. We will show you how to use a CCT analysis to analyze individual differences and cultural consensus about what makes people feel love culture. We describe a study that asked people to share their daily experiences of feeling loved in order to highlight the benefits of this method.
This topic requires us to examine people’s cognitive assessments on how they feel about being loved. We do this by looking at the shared agreement on when we are most likely to feel loved, and the individual differences that can influence our understanding of these agreements. The results show that people Best I Can Do Meme agree on indicators of love culture expressed and that they are both romantic as well as unromantic.
Love culture scenarios based on personality and demographics
People also have individual differences in (1) their knowledge about this consensus and (2) how they respond to items on love culture scenarios based on personality and demographics. All of these conclusions are possible with the CCT method.
The following scenario is possible: Your partner tells you how much they care about you, and then starts texting or calling you often throughout the day, asking you where you are and what your thoughts are. You feel loved and warm inside. Another person could experience the same behavior from their partner, but they might not feel loved. They might feel controlled or even violated.
So, this brings up the question: Do you agree on which experiences in everyday life make you feel loved? These experiences would elicit love culture in all people. Is there a significant difference between people who identify this behavior as a sign of love? How can we measure the consensus among people about the concept of feeling love in a way that takes into account individual differences? Is there a relationship between individual differences in consensus awareness and personality characteristics?
This article demonstrates how cognitive psychometric methodology, based on cultural consensus theory (CCT) can simultaneously reveal the nature of generalized beliefs or cognitive schemata governing social behavior and the knowledge of the individual about these schemata. CCT is a collection of cognitive response models that can be used in various questionnaire formats. These include True/False, ordered groups (Likert as in grading essay question) and continuous responses (as seen in probability judgements).
CCT models allow us to derive shared beliefs, knowledge and content domains through the application of a formal model for decision-making. CCT is used most frequently with knowledge domains that do not require a ground truth or scientifically verified correct answers. However, it assumes there is a cultural consensus within the domain of shared knowledge and beliefs. Consensus refers to the agreement of all cultural members on a particular content domain or a shared knowledge and opinion on a theory concept. A culture can simply be any group that shares knowledge and beliefs. This could include adults in the U.S. or Facebook users.
To understand and operationalize a concept in a culture or group of people, we gather responses from a variety of items within the content domain and use CCT models to simultaneously extract the consensus and knowledge.
It is not assumed that everyone in the group has all the answers. Therefore, individuals can vary in their cultural knowledge and their response biases. CCT allows researchers to find a concise, practical, and clear definition of a concept that is acceptable by a group with some common beliefs or knowledge.
CCT models have been extensively used in a variety of domains. They have been used in the study of medical knowledge and beliefs in anthropology (Weller de Alba Garcia, Rochad, & Rochad 2012), in extracting information form eye-witness testimonies(Waubert de Puiseau), Abfalg and Erdfelder 2012), and in inferring personality traits in social network (Agrawal & Batchelder 2012; Batchelder Kumbasar & Boyd 1997).
The central element of all CCT models is the way cultural truth is defined. Models for True/False, True/False/Don’t know questionnaires usually describe truth as dichotomous (True and False). Batchelder (2012) and Anders (2012) offer a model of a True/False questionnaire, where truth is on a continuum like fuzzy logic.
CCT models that are ordinal (e.g. Anders & Batchelder 2015) and those that require continuous (slider-like) responses (e.g. Anders, Oravecz & Batchelder 2014) both treat truth as a continuum. Batchelder, Oravecz, and Batchelder (in press) provide a comprehensive overview of the CCT framework.
CCT is a particularly important approach because it allows you to explore group beliefs and not just aggregate between individual responses.
The methodology relies on quantifying an individual’s knowledge and the consensus on related items. This is done by weighing the responses of each person according to their competence and then aggregating them across people. The overall goal of a CCT model is (1) to identify latent “cultural groups” who share a consensus on the answers to a set questions; (2) to decide if the data supports a statistical model used to do so; and (3) to estimate the parameters of that model.
Parameters refer to the cultural salience of each question or its difficulty, the cultural competence of each informant or their calibration, and the biases in each informant’s responses. If more than one consensus truth/cultural category is identified, the CCT model will estimate the parameters of each informant’s membership. (Anders & Batchelder 2012).
