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Variables, Conceptualization
Conceptualization is a process of defining meaning of the terms used in a study (e.g., definition using concepts and words) based on previous scholarship. Utilizing prior research provides a basis for creating agreement upon variable conceptualization within the field. However, when reviewing a scholarly article, it is common (and important) for researchers to critically question how something is conceptualized and how it is operationalized. This entry focuses on conceptualization in the variable construction process:
Conceptualization → Definition → Operationalization → Measurement
Researchers use various terminologies throughout the conceptual, theoretical, and empirical stages of research. Occasionally, these terminologies are utilized interchangeably; however, there are distinctions. Variables have conceptual definitions. These definitions include elements of interest to researchers. Elements are specifically identi-fied in the hypotheses and research questions. Hypotheses are predictive statements about the relationship between the variables, and research questions are statements about the relationship between the variables without specific predictions (rather more exploratory and descriptive in nature). These elements must be able to be expressed as more than one value or category. For instance, in a study examining paternal behaviors of both mothers and fathers, the researcher would have two variable elements. However, exclusively examining mothers does not allow the parental sex to vary and, therefore, it is a constant. If a variable has only one characteristic or a fixed value that does not change, it is referred to as a constant.
This entry discusses the differences between conceptualization and concepts. It explains the derivation of concepts in a discussion of both direct and indirect observations. In addition, concepts and constructs are used synonymously, and this entry dispels the differences and similarities between those terms. Next, this entry further narrows variable conceptualization through a discussion of indicators, dimensions, and examples. Finally, this entry closes with a discussion of the distinction that occurs for inductive and deductive approaches.
Conceptualization and Concepts
Researchers start with concepts that are mental images comprising observations, feelings, or ideas. These can be represented as objects (e.g., newspaper articles, Snapchat photos, or love letters), events (e.g., relationship turning points, childbirth, or onset of menopause), relationships (e.g., familial bonds, doctor–patient relationships, or Facebook friends), or processes (e.g., presidential speechmaking, cross-fit lifting techniques, or creating grandma’s eulogy). Often these conceptions are ambiguous and imprecise notions that must be made more specific and precise. Thereby, when a concept is conceptualized, researchers specify what they mean by a term, which makes it possible to communicate a working definition. The process of coming to agreement on what is specified by terminology used is conceptualization and the outcome is a concept.
Conceptualization is a continual process for researchers. Therefore, the researcher should give focused emphasis on it during the beginning of the research design, though it should be revisited throughout the data collection and result interpretation processes. Even when researchers begin to standardize a definition, or commonly utilize the identical or unchanged conceptualization, it still must undergo the same rigor. Pre-existing conceptualizations affirmed by the field, extensively tested, and adopted across numerous studies have advantages; nevertheless, nuances can occur and often multiple conceptualizations exist in a variety of contexts. Still, readers should look to the literature, to see how researchers are conceptualizing the variables, whether in disagreement or as standardized. An explicit variable definition should be present upon conceptualization.
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- Creating and Conducting Research
- Creation of Research Project
- Authoring: Telling a Research Story
- Body Image and Eating Disorders
- Hypothesis Formulation
- Methodology, Selection of
- Program Assessment
- Research Ideas, Sources of
- Research Project, Planning of
- Research Question Formulation
- Research Topic, Definition of
- Research, Inspiration for
- Social Media: Blogs, Microblogs, and Twitter
- Testability
- Ethics
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- Activism and Social Justice
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- Post Hoc Tests: Least Significant Difference
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- p value
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- Multitrial Design
- Null Hypothesis
- One-Group Pretest–Posttest Design
- Orthogonality
- Overidentified Model
- Pilot Study
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- Quantitative Research, Purpose of
- Quantitative Research, Steps for
- Quasi-Experimental Design
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- Replication
- Research Proposal
- Rigor
- Sampling Theory
- Sampling, Determining Size
- Solomon Four-Group Design
- Stimulus Pre-test
- Two-Group Pretest–Posttest Design
- Two-Group Random Assignment Pretest–Posttest Design
- Variables, Control
- Variables, Dependent
- Variables, Independent
- Variables, Latent
- Variables, Marker
- Variables, Mediating Types
- Variables, Moderating Types
- Within-Subjects Design
- Linear Approaches to Statistics
- Analysis of Residuals
- Bivariate Statistics
- Bootstrapping
- Confidence Interval
- Conjoint Analysis
- Contrast Analysis
- Correlation, Pearson
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- Discriminant Analysis
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- Partial Correlation
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- Semi-Partial r
- Simple Bivariate Correlation
- Statistical Measurement Issues
- Z score
- Categorization
- Cluster Analysis
- Data Transformation
- Errors of Measurement
- Errors of Measurement: Attenuation
- Errors of Measurement: Ceiling and Floor Effects
- Errors of Measurement: Dichotomization of a Continuous Variable
- Errors of Measurement: Range Restriction
- Errors of Measurement: Regression Toward the Mean
- Frequency Distributions
- Heterogeneity of Variance
- Heteroskedasticity
- Homogeneity of Variance
- Hypothesis Testing, Logic of
- Intraclass Correlation
- Mean, Arithmetic
- Mean, Geometric
- Mean, Harmonic
- Measures of Central Tendency
- Median
- Mode
- Mortality in Sample
- Normal Curve Distribution
- Relationships Between Variables
- Sampling, Probability
- Sensitivity Analysis
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- Standard Deviation and Variance
- Standard Error
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- Statistical Power Analysis
- Type I error
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- Univariate Statistics
- Variables, Categorical
- Variables, Continuous
- Variables, Defining
- Variables, Interaction of
- Statistical Models
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- Cloze Procedure
- Cross Validation
- Cross-Lagged Panel Analysis
- Curvilinear Relationship
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- Lambda
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- Meta-Analysis: Estimation of Average Effect
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- Meta-Analysis: Literature Search Issues
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- Multivariate Analysis of Variance (MANOVA)
- Multivariate Statistics
- Odds Ratio
- Ordinary Least Squares
- Path Analysis
- Probit Analysis
- Quasi-F
- Sobel Test
- Structural Equation Modeling
- Time-Series Analysis
- Analysis of Variance Approaches
- Understanding the Scope of Communication Research
- Areas of Inquiry
- Acculturation
- African American Communication and Culture
- Agenda Setting
- Applied Communication
- Argumentation Theory
- Asian/Pacific American Communication Studies
- Bad News, Communication of
- Basic Course in Communication
- Business Communication
- Communication and Aging Research
- Communication and Culture
- Communication and Evolution
- Communication and Future Studies
- Communication and Human Biology
- Communication and Technology
- Communication Apprehension
- Communication Assessment
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- Communication Education
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- Communication Privacy Management Theory
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- Conflict, Mediation, and Negotiation
- Corporate Communication
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- Cross-Cultural Communication
- Cultural Studies and Communication
- Cyberchondria
- Dark Side of Communication
- Debate and Forensics
- Development of Communication in Children
- Diaspora
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- Dime Dating
- Disability and Communication
- Distance Learning
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- English as a Second Language
- Environmental Communication
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- Film Studies
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- Gender and Communication
- GeoMedia
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- Structure of Research Community
- Areas of Inquiry
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