bluuebunny/crossref_metadata_2025 huggingface dataset:
Analysis of Covariance
Authors: Peter Christina
Published: November 2017

Analysis of covariance (ANCOVA) allows analyzing the relationship between two variables while controlling for a third variable. In a first step, its effect on the dependent variable is calculated, so that in a second step the unique effect of the independent variable on the dependent variable can be assessed. ANCOVA is useful in analyzing both experimental and observational data, but with different implications. Throughout this entry, the logic of ANCOVA and its assumptions are described. Examples of when and when not to apply ANCOVA are provided for observational as well as experimental data.
Analysis of Variance
Authors: Ellithorpe Morgan E.
Published: November 2017

Analysis of variance (ANOVA) is a statistical tool for when a researcher has a continuous dependent variable and wishes to test whether the means on that variable differ meaningfully based on nominal groups. Conceptually, ANOVA tests whether the variation in means between groups is larger than the variation within groups—that is, whether differences in group means can be attributed to actual difference or to error. Beyond the
F
statistic, a metric that tells the researcher whether there is any difference between the means in general, researchers must contend with tests that compare the groups more specifically and with effect size estimates. In addition, factorial ANOVA can provide information regarding the effects of multiple independent variables, both separately and interactively.
Analysis of Variance, Repeated Measures
Authors: Yegiyan Narine
Published: November 2017

This entry provides an overview of the analysis of variance (ANOVA) for the repeated‐measures designs. Advantages of the method and specific pitfalls are discussed. Specifically, the issue of the homogeneity assumption is addressed. The logistics of replacing it with the sphericity assumption is outlined. Alternative solutions such as trend analysis and multivariate analysis are also considered.
Audience Ratings, Media Industries
Authors: Napoli Philip M. , Bjur Jakob
Published: November 2017

Audience ratings are a central component of the operation of media industries. This entry describes both the industrial and methodological dimensions of audience ratings, providing an overview of the history and evolution of audience ratings, as well as considering the contemporary challenges confronting audience measurement and the means by which the audience ratings industry is attempting to address these challenges.
Big Data, Analysis of
Authors: Trilling Damian
Published: November 2017

This entry describes what so‐called Big Data are and how they can be analyzed in communication science and the computational social sciences more broadly. It briefly addresses the epistemological questions around this type of analysis and its historical development in communication science and related disciplines. The entry then moves on to a more practical description of the technical requirements, the structure of such datasets, and their storage in different types of databases. It gives an overview of different analytical approaches, including, but not limited to, visualizations, supervised and unsupervised machine learning, natural language processing, and network analysis. Ethical implications and future directions are discussed.
Bootstrapping
Authors: Scharkow Michael
Published: November 2017

Bootstrapping is a method for drawing statistical inferences based on repeated resampling (with replacement) from the sample data. In contrast to classic asymptotic inference, bootstrapping makes no assumptions about the sampling distribution of a statistic and has been shown to work well even with smaller samples and nonnormal data. Bootstrapping can be used to estimate sampling variability and bias, produce
p
‐values for hypothesis tests, or, more commonly, to calculate confidence intervals for any statistic of interest.
Categorical Data Analysis
Authors: Kühne Rinaldo
Published: November 2017

The general linear model, which incorporates statistical analyses, such as ordinary least squares regression,
t
‐test, and analysis of variance, is based on a series of assumptions about the independent variables, the dependent variable, and the error terms. Because these assumptions are often violated in the analysis of categorical data, alternative techniques should be employed. This entry reviews regression models which are adequate for the analysis of categorical dependent variables.
Causal Inference
Authors: Lee Sungkyoung
Published: November 2017

Consideration of causal inference is essential to scientific research. Experimental design is most often used when exploring cause‐probing research questions. Here special attention is given to some foundational concepts associated with causal inference, such as cause, effect, and two different types of causal relationships—causal description and causal explanation. Different experimental designs capable of establishing causal inference are also discussed.
Communication Activism
Authors: Breede Deborah Cunningham
Published: November 2017

While a relatively new outgrowth of the communication discipline, communication activism is rooted within similar traditions of rhetorical analysis and social science. Influenced by the epistemologies, philosophies, methodologies, and positionalities emerging from participatory research, cooperative inquiry, action research, and other processes whose research goal is one of social justice, communication activism achieves consensus building, collective action, and solutions to social problems on local and global levels. Definitions, histories, influences, ethicalities, and emergent technologies are reviewed in this entry.
Communicology
Authors: Catt Isaac E.
Published: November 2017

Communicology is the human science of embodied discourse. Method in this discipline is informed by philosophy of communication, structured by theory, and empirically grounded in qualitative logic. The philosophical basis is found in American pragmatism and European philosophy, theory in semiotics, and logic in the descriptive‐critical methodology of phenomenology. Communicology employs theappositional methodology of semiotic phenomenology to study the processes and events of communication that constitute human consciousness and that experientially and logically precede instrumental message‐bound views of communication. As a human science, focus is instead on the structure and meaning‐as‐lived of conscious experience.
Computational Simulation Methods
Authors: Álvarez‐Gálvez Javier
Published: November 2017

There is growing interest in applying computational simulation methods to the field of social sciences. Although computer simulation was originally developed in the field of natural sciences, the fundamental motivation to simulate differs little from that of the social sciences such as anthropology, economics, political science, psychology, or sociology. In cases where analytic solutions are not possible or appropriate for studying complex phenomena, computational simulation may offer an alternative or even a complementary method to provide answers to unresolved research problems that cannot be explained through standard methodologies. These novel methods are especially valuable for simulating phenomena where there is significant uncertainty in data and also for modeling complex systems dynamics. Computational simulation methods have traditionally been used in the field of social sciences to obtain a better understanding of social phenomena and particularly to predict human behaviors, but simulation models can also be used to discover new research questions, demonstrate the validity of a certain theory, educate the general public and policy‐makers, or to identify the crucial mechanism that determines the macro‐process that we are trying to explain. Thus, the reasons to use simulation models in social sciences, and in particular in communication research, extend beyond prediction purposes.
Conjoint Measurement
Authors: Arning Katrin
Published: November 2017

Conjoint measurement (or conjoint analysis) is a quantitative empirical research method for studying individual preferences or decision‐making processes, determining trade‐offs, segmenting groups, and simulating reactions to novel products or scenarios. It is one of the most prominent research tools in marketing research, which also can provide valuable contributions to communication science research and practice. After an introduction to the basic assumptions of conjoint measurement, the main focus of this entry is on the procedure and design of conjoint analyses, followed by their potential fields of application in communication research, some critical remarks, and an outlook on future developments of conjoint analyses.
Content Analysis, Automatic
Authors: Scharkow Michael
Published: November 2017

Automatic content analysis (
ACA
) is a technique for coding messages with the help of computer algorithms. Unlike computer‐aided content analysis,
ACA
is defined as any method in which the actual coding decision, that is, assigning codes to documents or single textual or audiovisual elements, does not require human judgment and therefore is performed automatically. Since
ACA
relies on the computing capabilities of machines rather than human coders, it can be applied to very large samples of documents. Moreover, automatic coding is highly reliable in the sense that any analysis can be reproduced exactly given the same material and software and all errors are, in principle, deterministic, that is, stemming from misspecification or program errors.
Content Analysis, Qualitative
Authors: Wright Kallia Odette
Published: November 2017

This entry provides a description of qualitative content analysis, which is the study of nonliving material or cultural texts. The analysis of texts or immaterial data provides insight into social worlds, ideologies, or dominant worldviews. Content or texts are broadly defined to include personal documents, such as diaries or blogs, and public documents, such as organizational annual reports, brochures, advertising, and press releases. This entry contains a summary of the types of texts that can be investigated using this qualitative communication research method. Examples of research designs and analytical approaches in content analysis demonstrate the far reach of this communication research method. The strengths and limitations of qualitative content analysis are also offered.
Content Analysis, Quantitative
Authors: Coe Kevin , Scacco Joshua M.
Published: November 2017

Quantitative content analysis is a research method in which features of textual, visual, or aural material are systematically categorized and recorded so that they can be analyzed. Widely employed in the field of communication, it also has utility in a range of other fields. Central to content analysis is the process of coding, which involves following a set of instructions about what features to look for in a text and then making the designated notation when that feature appears. Conducting a successful content analysis requires careful attention to unitizing (segmenting the texts for analysis), sampling (selecting an appropriate collection of units to analyze), reliability (different researchers making codes consistently), and validity (using a coding scheme that adequately represents the specified phenomena).
Copy Test and Starch Test
Authors: Sar Sela , Rodriguez Lulu
Published: November 2017

The Starch method is a copy test that measures a reader's recognition of print advertising. It measures whether the reader (i) has seen the ad, (ii) has seen the name of the product or company the ad endorses, and (iii) has read half or more of the written material. These three measurements represent the dimensions of the Starch test. This entry describes the nature of Starch testing as an advertising readership measurement technique and examines its strengths and weaknesses.
Correlation Analysis
Authors: Hüsser Andreas Philippe
Published: November 2017

Correlation analyses are widely used to describe the association between two variables in a survey or time series. This entry provides a brief overview of the properties and limitations of several widely used correlation coefficients, and introduces the concept of Pearson's correlation coefficient. The major factors that affect the interpretability of these correlation coefficients (e.g., small sample size) are also discussed.
Critical Incident Technique
Authors: Kreps Gary L.
Published: November 2017

This entry describes the Critical Incident Technique (
CIT
), which is a powerful qualitative research method that generates insightful narrative data about user experiences with specific programs and services. The key steps in conducting and reporting
CIT
are described, as are the uses and applications of
CIT
data. The data gathered with
CIT
provide strong rationale and direction for program development.
Critical Theory and Research
Authors: Framer Melissa , Chevrette Roberta
Published: November 2017

Critical theory and research incorporates the reflexive critique of social and cultural processes. Critical theory questions dominant discourses and works toward social justice in an effort to provide space for all lived experiences. Utilizing autoethnography, ethnography, participant‐observation, arts‐based research, interviews, focus groups, critical discourse analysis, and other textual methods, critical theory researchers challenge the privileging of certain voices over others while remaining cognizant of their privileged role in knowledge production.
Crystallization
Authors: Ellingson Laura L.
Published: November 2017

Crystallization enables qualitative researchers to combine multiple methods and multiple genres into a single text or series of texts to embody a postmodern form of validity. The juxtaposition of both social scientific and narrative, performative, or artistic representations generates rich and openly partial accounts of phenomena that problematize their own construction, highlight researchers' positionality, make claims about socially constructed meanings, and reveal the indeterminacy of all knowledge claims. Integrated crystallization combines multiple genres into a single text, while dendritic crystallization involves a dispersed process of making meaning through multiple methods and multiple genres in a series of separate, yet related texts.
Data Conferencing
Authors: Braithwaite Dawn O. , Allen Jordan , Moore Julia
Published: November 2017

Validating findings in qualitative research is a challenging task. While some methods of verification exist, for example, member checking, engaging the expertise of other scholars can be an important and fruitful step. We describe data conferencing, a process of peer verification that engages the methodological, theoretical, and analytical expertise of scholars in related areas of study to help assess the results and argue for validity of qualitative studies.
Data Imputation
Authors: Rosenthal Sonny
Published: November 2017

Data imputation involves representing missing values in a dataset. Missing data create a number of potential challenges for statistical analysis. Missing values can increase the chances of making Type I and Type
II
errors, reduce statistical power, and limit the reliability of confidence intervals. There are a number of statistical procedures available for researchers to replace missing values with reasonable estimations. Basic methods, such as mean substitution, regression imputation, and hot deck imputation may bias imputed values depending on the mechanism of missingness. Advanced methods include expectation maximization, full information maximum likelihood, and multiple imputation. These methods produce more reliable estimations of missing values, particularly when missingness is not at random.
Data Saturation
Authors: Faulkner Sandra L. , Trotter Stormy P.
Published: November 2017

Data saturation refers to the point in the research process when no new information is discovered in data analysis, and this redundancy signals to researchers that data collection may cease. Saturation means that a researcher can be reasonably assured that further data collection would yield similar results and serve to confirm emerging themes and conclusions. When researchers can claim that they have collected enough data to achieve their research purpose, they should report how, when, and to what degree they achieved data saturation.
Data, Types of
Authors: Heiss Raffael
Published: November 2017

This entry deals with types of data in communication research: quantitative and qualitative data, different types of raw, categorized, and numerical data, cross‐sectional, panel and process‐oriented data, individual and aggregate data, primary and secondary data, metadata and paradata.
Delphi Studies
Authors: Wehmeier Stefan
Published: November 2017

The Delphi technique is an intuitive and heuristic method that tries to objectify estimations of complex and ambiguous future developments. Therefore, Delphi studies are a method for decision‐making in future tasks. The data are gathered through a series of questionnaires interspersed with controlled and anonymous feedback. The goal of most Delphi studies is to gain a group consensus. Although the method is named after the ancient Greek oracle at Delphi, it is a young research method in the social sciences. It was a spin‐off from defense research at the beginning of the Cold War.
Diction(Software)
Authors: Hart Roderick P.
Published: November 2017

This entry provides a description of DICTION, a “Big Data” program for quantifying the tonal properties of a verbal passage. It includes a brief description of the program's conceptual structure and the kinds of data it provides. The entry also distinguishes DICTION from other software used to analyze language behavior.
Discourse Analysis
Authors: Scharp Kristina M. , Thomas Lindsey J.
Published: November 2017

Discourse analysis is used in reference to a variety of methodological approaches and practices. Generally, discourse analysis is employed to explore the ways in which people create, convey, share, acquire, and/or (mis)understand the meaning(s) of moments, events/experiences, lives, and (sub)cultures. These meaning‐laden processes reflect and reify multilevel structures, and thus, discourse analysis can be used to examine multifaceted forms, patterns, organizations, orders, and/or systems of, in, and absent from communication (inter)actions. This entry offers an overview of approaches to discourse analysis, including theoretical assumptions of, and methodological approaches to, a variety of discourse analyses. The entry concludes with a description of general steps for conducting discourse analysis.
Economic Analysis
Authors: Jayakar Krishna
Published: November 2017

Media economics is the application of economic theories and concepts to systematically analyze the media and communication industries. A wide range of industries are studied in media economics, from the traditional print media, music, filmed entertainment, radio, television, satellite broadcasting, telephony, cable, and broadband, to the relatively new mobile communication and social media sectors. Though rooted in economic analysis, it is by nature multidisciplinary and accommodates a wide range of theoretical inputs from fields as diverse as consumer psychology, legal and regulatory analysis, management, finance, and public policy studies. Methodologically, too, the inputs into media economics are quite diverse, from economic modeling, econometric analysis, game theory, financial analysis, and case studies. This entry surveys the various methods used in the study of media economics.
Emic Approach to Qualitative Research
Authors: Scarduzio Jennifer A.
Published: November 2017

This entry explores the emic approach to qualitative research by explaining how it is used to collect and analyze qualitative data. The emic approach centers on the participants' point of view and examines the research setting by describing the participants' ways of communicating, behaving, and interacting in the scene. During analysis, a researcher using the emic approach starts with the ideas, themes, and behaviors that are emerging from the scene and the participants and then proceeds forward to connect these findings to relevant theory.
Epistemology
Authors: Potter W. James
Published: November 2017

Epistemology is the branch of philosophy concerned with the nature of knowledge, its possibility, scope, and general basis. For millennia, philosophers have debated a variety of epistemological beliefs about the value of different sources of knowledge, especially rationalism, idealism, and empiricism. With the rise of the physical sciences, a belief in the value of empiricism became dominant, and this belief also shaped the development of the social sciences in general and communication in particular. The field of communication is currently being influenced most strongly by the epistemological belief in positivism, while other epistemological beliefs, such as interpretivism, constructivism, and constructive realism, continue to challenge the dominance of positivism.
Ethics of Empirical Research
Authors: Keith Paul K. , Fairfield Joshua A. T.
Published: November 2017

The ethics of empirical research are governed by fundamental ethical principles applied through regulations and procedural requirements. The philosophical ideologies of consequentialism and deontology find specific expression through foundational documents of research ethics—the Nuremberg Code, the Declaration of Helsinki, the Belmont Report, the Menlo Report, and the U.S. Federal Common Rule. These documents establish the fundamental principles of autonomy, beneficence, and justice. Institutional review boards (IRBs) enforce these ethical principles by reviewing research protocols and monitoring ongoing experiments. IRBs ensure that researchers comply with requirements for informed consent, weighing risks and benefits, and harm minimization, among others. As the nature and practice of research evolves, reform proposals—like the Notice of Proposed Rulemaking (NPRM) for the U.S. Federal Common Rule—attempt to ensure that research in the modern world is ethically defensible. Despite the continued focus on research ethics, lingering ethical issues threaten to undermine the legitimacy of researchers and research institutions. Successfully parsing these issues is essential to our continued scientific progress and our humanity.
Ethnodrama and Ethnotheatre
Authors: Marsh Jaclyn Sanders , Davis Christine S.
Published: November 2017

Ethnodrama (ethnotheatre) merges performance studies, theatre, and ethnography with the aim of critical engagement of social issues, dramatization of an experience, engagement between the work and the audience, and broad dissemination of research findings. Seminal contributors in the field of ethnodrama include Johnny Saldaña, Norman Denzin, and Ronald Pelias. Other significant scholarship in ethnodrama has been conducted by Carolyn Ellis, Art Bochner, Craig Gingrich‐Philbrook, Sarah Amira de la Garza, Elissa Foster, Chris Poulos, and Tami Spry, among others.
Etic Approach to Qualitative Research
Authors: Monteagut Lorraine E.
Published: November 2017

The etic approach to qualitative research (often referred to as the “deductive” approach), makes use of conceptual categories and disciplinary knowledge as the basis for understanding a particular setting or study. A researcher taking an etic approach privileges pre‐existing theories and hypotheses and seeks to construct accounts and analyses in terms of a specific disciplinary paradigm. Such a researcher would conduct a study to show the application of a theory on a broad scale.
Experiment, Field
Authors: Holz Ivory Adrienne
Published: November 2017

Field experiments are true randomized experiments that are taken out of the lab and conducted in naturalistic settings. Field experiments tend to have higher external validity than laboratory experiments; however, field experiments sometimes necessitate sacrifices in internal validity due to lesser control over extraneous factors in naturalistic settings. Ideally, field experiments attempt to maintain the internal validity of other types of randomized experiments while adding the higher generalizability gained by conducting a study in a natural setting.
Experiment, Natural
Authors: Almond Anthony
Published: November 2017

Natural experiments are a specific type of experimental methodology used when a treatment variable is varied through an event that is exogenous to the outcome variable. The natural experiment is similar to both the laboratory and field experiment as well as observational studies, but differs in a few key areas including replicability, control, and ecological validity. This entry aims to provide support for the use of natural experiments in communication research.
Factor Analysis, Confirmatory
Authors: Ellithorpe Morgan
Published: November 2017

Confirmatory factor analysis (CFA) is a versatile instrument for dimension reduction, construct validation, measurement comparison, and generalization. There are many challenges and considerations that researchers must deal with before and after CFA analysis to maximize the effectiveness of CFA, but when used correctly in ideal conditions it can be a very powerful tool for researchers in many different contexts.
Frame Analysis
Authors: David Clarissa C. , Baden Christian
Published: November 2017

Frames are “central organizing ideas” that provide context, structure, and meaning to information, facilitating a specific interpretation of an issue. Counting among the most popular ideas in communication study in recent years, variants of frame analysis have developed within a variety of disciplines inside and outside of communication. Methodologically, qualitative as well as quantitative approaches to frame analysis can be organized along three dimensions: their capture of latent versus manifest meanings, their adherence to inductive versus deductive processes, and their focus on generic or issue‐specific frames. Increasing numbers of studies using frame analytic techniques have resulted in a contested and fragmented set of methods, the most influential of which are discussed in this entry.
Grounded Theory
Authors: Olson Loreen N.
Published: November 2017

Grounded theory is a qualitative methodology designed to generate or capture an explanation (theory) of a process, action, or interaction. The method was created in the 1960s by sociologists Glaser and Strauss. Because of its systematic approach to data collection, analysis, and verification, grounded theory came to be viewed as a legitimate form of qualitative research in the heavily positivist era of mid‐century social science. Constructivist grounded theory and situational analysis are two contemporary approaches that have grown out of these original 1960s roots. These newer forms reflect more postmodernist sensibilities. This entry describes the history of grounded theory as well as summarizes the newer approaches. Also reviewed are common characteristics associated with all grounded theory analyses, such as theoretical sampling and theoretical saturation, theoretical sensitivity, treatment of the literature, memos and diagrams, concepts/codes and categories/themes, constant comparative method coding process, open coding, axial coding, selective coding processes, and in vivo codes. The entry ends with a description of the procedures involved with conducting a traditional grounded theory from start to end.
Interpretive Research
Authors: Quinlan Margaret M.
Published: November 2017

Interpretive research, often called qualitative research, situates meaning‐making practices at the center of scientific exploration.
Interview Guide
Authors: Smulowitz Stacy
Published: November 2017

Interview guides consist of either questions, topics or a combination that run from unstructured to highly structured. Depending on researcher preferences, the interview guide will use one of these methods. One of the most common interview guide methods is a semi‐structured interview where many precise questions and their follow‐up questions are listed along with a few topic areas. Interviewer(s) should be flexible with their interview to permit participants to expand on their initial answers. This allows for the inclusion of additional rich data to the research study.
Interview Methods, Quantitative
Authors: Möhring Wiebke , Schlütz Daniela M.
Published: November 2017

Quantitative interviewing is one of the major data collecting methods and thus has great importance in communication research. Interviews are central to measure media use, the evaluation and effects of communication, as well as the state and trend of public opinion. Under certain circumstances, quantitative surveys can produce representative results. As the questionnaire is central to ensure the quality of the whole interview, it is crucial to consider the cognitive answering process and possible errors that may go along with it. Interviews can be administered personally (face‐to‐face), via telephone, or as a self‐administered questionnaire. All modes can be supported by use of a computer, for instance as a CATI interview or a web survey.
Interviewing Vulnerable Populations
Authors: Clark Lou
Published: November 2017

Interest in researching populations considered to be vulnerable such as patients, elderly, those at risk for mental illness, prisoners, pregnant women, children, and certain cultural groups like Native Americans has become increasingly prevalent in the past 40 years. The term
vulnerable population
may be synonymous with other terms including at‐risk, marginalized, and special populations. Interviewing is the most frequently used qualitative data collection method including for studies with vulnerable populations. Techniques for interviewing vulnerable populations draw on established qualitative interview practices made popular by sociologists James Holstein and Jaber Gubium as well as Kathy Charmaz. It is widely agreed that researchers should maintain additional sensitivity toward interviewees who are part of vulnerable populations including, and especially, with regard to language. While interviewing members of vulnerable populations may induce empowering effects that come with story sharing, referring to interviewees as
vulnerable
may lower their self‐confidence and inadvertently victimize them.
Interviewing, Qualitative
Authors: Phillips Joshua Daniel
Published: November 2017

The purpose of this entry is to discuss interviewing as a qualitative research method within the field of communication studies. First, it will discuss the history of interviewing. Second, it will look at the different types of interviews communication studies researchers might facilitate. For example, different types of interviews create different tones and context for responses, and the researcher's objective will determine what type of interview to perform. Next, this entry discusses how to find potential participants as well as some of the potential challenges faced during the interview and how to navigate those challenges. Lastly, it will offer some advice on analyzing interviews through the process of categorizing, coding, and thematic analysis.
Legal Methods
Authors: Fargo Anthony L.
Published: November 2017

The communication law researcher is interested in understanding and explaining how actions by governmental bodies affect the ability of persons and media corporations to express themselves. Legal researchers find and analyze the materials produced by the branches of government relevant to the topics they are interested in and in the context of their meanings to the legal community. Much of the methodological challenge for the legal researcher is in learning what documents are relevant and how to find them. Work produced through legal research can affect the policy decisions of political actors and contribute to critiques of existing theories about the importance of free expression or help develop new theories.
Measurement of Attitudes
Authors: Rubenking Bridget , Appelbaum Michael
Published: November 2017

Attitudes toward other individuals, groups of individuals, social issues, candidates, brands, health behaviors, messages, and more have been measured in a number of empirical ways across communication subdisciplines. While explicitly asking how positive or negative a respondent feels toward an attitude object remains a norm in communication research, newer implicit, or indirect measurements have been imported from research in social psychology. Common explicit and implicit measures used in media processing and effects, media entertainment, health communication, advertising, political communication, and interpersonal/group communication are discussed.
Measurement of Behavior
Authors: Von Sikorski Christian
Published: November 2017

The present entry deals with the question of how behavioral effects of (mass‐)mediated communication can be systematically measured. Behavior is defined as an occurrence that emanates from an organism or may be detected by an observer on the outside of an organism (e.g., media recipient). A three‐stage process for measuring behavior in communication research is proposed, and several examples of observational studies (e.g., mobile eye tracking, psychophysiological measures) are provided. The implementations for future research are discussed.
Measurement, Formative and Reflective
Authors: Sommer Katharina
Published: November 2017

Latent variables are assessed by observable measures (indicators). The measurement model describes the relationship between these manifest indicators and the latent construct. Measurement models can be reflective or causal in nature. In reflective models, the indicators are affected by the latent variable, whereas in causal models the indicators define the latent variable.

This entry describes the differences between the two types of measurement models and discusses the assessment of criteria for reliability and validity. Furthermore, it explains the requirements for estimating formative and reflective measurement models, describes the consequences of model misspecification, and provides insights into the criticism of formative modeling. Finally, it presents the difference between covariance‐ and variance‐based structural equation modeling techniques.
Metaphor Analysis
Authors: Redden Shawna Malvini
Published: November 2017

In recent decades, scholars have considered metaphors as instruments of meaning making and how people construct reality. Metaphors, which are figures of speech used to compare one thing to another, signal more complex meaning making and thus can be useful as a means of qualitative data collection and analysis. Viewed as mechanisms that are integrated in how people think, act, and communicate, metaphors, then, offer an important avenue for researchers interested in understanding why people act and communicate as they do. Subsequently, metaphor analysis has become a popular tool for understanding social phenomena including how people conceptualize organizational change, education, health issues, relationships, crisis experiences, and more.
Missing Values and Missing Data
Authors: Hagenah Jörg
Published: November 2017

Communication studies often have the goal of generalizations by analyzing a random sample. Nevertheless, noncoverage and nonresponse cause incomplete datasets. Problems arise when missing data and missing values lead to distorted distributions or false inferences. According to the identified missing mechanism—missing completely at random (MCAR), missing at random (MAR), or missing not at random (MNAR)—an appropriate compensation strategy, such as direct analysis, weighting procedures, or imputation methods, is used.
Mobile Experience Sampling Method (
MESM
)
Authors: Hedstrom Alex , Irwin Matthew
Published: November 2017

Mobile experience sampling method (MESM) is a technological update of the experience sampling method (ESM) in which researchers sample from a population multiple times a day over an extended period of time. Used to research theoretically interesting aspects of everyday life, the introduction of mobile technology has allowed for more refined and flexible ESM studies. Mobile technology has simultaneously increased the specificity of measurement and reduced the burden of reporting for the participant. Utilizing ESM study traditions updated with the latest mobile technology, the ability for researchers to measure naturalistic contextual elements alongside subjective reports of life, objective measures of activity, and some physiological indicators allows for rich and increasingly ecologically valid datasets. Considerations regarding the design, implementation, and theoretical concerns of ESM studies using mobile technology are presented alongside traditional methods to illustrate the increased research power and flexibility afforded through these equipment advances.
Multidimensional Scaling
Authors: Wonneberger Anke
Published: November 2017

Multidimensional scaling (MDS) is a class of statistical models that are used to represent proximity data, such as similarity judgments, preference or choice data, in a multidimensional space. MDS is frequently applied in psychology and marketing but it is also a valuable method for many questions in communication science. This entry introduces the main principles and types of MDS discerning exploratory and confirmatory approaches.
Multitrait‐Multimethod Matrix
Authors: Shen Fei
Published: November 2017

The multitrait‐multimethod matrix (MTMM), developed by Campbell and Fiske (1959) is a method for assessing construct validity, in particular, convergent validity and discriminant validity. The method requires the researcher to measure a set of traits using multiple methods in one study. With measures of different traits by different methods, researchers could generate a correlation matrix—the multitrait‐multimethod matrix. The correlation data in the matrix could be used to judge the measurement quality of intended constructs. Confirmatory factor analysis (CFA) could also be used to assess the validity of constructs with formal statistical tests.
Narrative Ethics
Authors: Adams Tony E. , Bolen Derek M.
Published: November 2017

In this entry, the authors provide an overview of narrative ethics. They examine narratives as “equipment” we use to live; ethics of narrative time; concerns about hindsight, memory, and truth; narrative privilege; and considerations for reading and evaluating narratives.
Negative Case Analysis
Authors: Hanson Ardis
Published: November 2017

Negative case or deviant case analysis is a critical analytic strategy for ensuring validity in qualitative research. Negative cases are integral to the constant comparative method of data analysis used to establish points of comparison with normal cases. They are often seen as a study “control” in the development of theoretical generalizations in a case study, which adds rigor to qualitative studies.
Normal Science and Paradigm Shift
Authors: Ewoldsen David R.
Published: November 2017

The concept of paradigms emerged from Thomas Kuhn's (1970)
Structure of Scientific Revolutions
. In this theory of science, Kuhn posited that mature sciences operate within a scientific paradigm. A paradigm emerges out of the classic research exemplars within a research domain. These exemplars operate as models for how to do science within that paradigm. The culture (or paradigm) that emerges from the basic exemplars reflect the rules for doing science. Normal science is the day‐to‐day research that scientists conduct in order to fill in the gaps in scientific knowledge that are found within the dominant paradigm. Kuhn argued that paradigms always have intractable problems that result in research anomalies. Findings will emerge that the paradigm cannot explain and decrease the scientific community's confidence in the paradigm. At some point, scientists will begin operating outside of the dominant paradigm in an attempt to resolve these anomalous results and alternative paradigms will be developed. One day, the dominant paradigm may shift to a new paradigm in what is referred to by Kuhn as a scientific revolution. Popper proposed instead that all science should be considered revolutionary because it attempts to progress through attempts at falsifying theories.
Observational Methods
Authors: Alison Bryant J. , Liebeskind Kara , Gestin Riwanon
Published: November 2017

Observational research utilizes a naturalistic setting, where the researcher gathers data by watching events or conversations unfold. There is a range of approaches to observation, including covert versus overt, participant and naturalistic versus controlled. Although the strength of this methodology lies in the participants being more likely to behave and speak in ways that are more candid, it has some ethical concerns when the researcher does not make their intentions to collect data known.
Online Research Methods, Qualitative
Authors: Webb Lynne M.
Published: November 2017

Research methods commonly used in qualitative studies of online communication are identified. The reader is guided through the processes of selecting an object of study, selecting representative text, selecting a specific method of qualitative analysis (i.e., online interviews, participant observations, case study, content analysis, thematic analysis, discourse analysis), as well as deciding whether to employ computer analyses.
Oral History Interviewing
Authors: Chawla Devika
Published: November 2017

Oral history interviews are a cross‐disciplinary genre of qualitative research that function as a method for recording and preserving history. They are considered a form of personal experience narrative grounded in experiences of the past rather than a testimony about contemporary events. This entry provides a short history of the beginnings of oral history as a field in the North American context and offers an explanation for how oral history interviewing differs from other forms of qualitative interviewing.
Oral History
Authors: House Christopher A.
Published: November 2017

Oral history methodology is the recording and preservation of the memories of firsthand witnesses to matters under consideration. As such, oral history is concerned with the meaning‐making aspect of an oral document as opposed to a written historical document or artifact. The interviewer and interviewee (or in oral history parlance, the narrator) work together to cocreate a permanent record of past events that is then archived for future use by others including scholars, the general public, and other oral historians.
Performance Studies
Authors: Rudnick Justin , Chawla Devika
Published: November 2017

This entry describes the historical rise of the field of performances studies in the United States in the mid to late twentieth century. Relying closely on the work of performance scholars, D. Soyini Madison and Judith Hamera (2005), four different ways of understanding performance—as the art of public speaking, as the art of interpretation, as social behavior, and as experience—are explicated. This is followed by a discussion of performativity, which is considered a central conceptual idea and underpinning of performance studies. Finally, performance ethnography and performative writing are engaged as ways that the performance framework is most visible in the field of communication.
Personal Narratives as a Method of Writing
Authors: Tullis Jillian A.
Published: November 2017

Personal narrative as a method is relatively new in the discipline of communication studies. Personal narrative as a method of writing in communication studies draws upon the personal experiences of participants and sometimes researchers to understand the ways in which people communicate. By storying experiences and data, audiences are able to access knowledge with relative ease. This entry outlines the origins of the method and the rationale for its use, and describes the process for creating personal narratives. The entry briefly discusses the ethics of the method and concludes with recommendations for additional readings.
Power Analysis
Authors: Sun Shaojing , Fan Xitao
Published: November 2017

Statistical power is an important factor for researchers to consider when designing and conducting quantitative studies. Lack of statistical power may lead to erroneous conclusions. To boost statistical power, however, may entail a larger sample size or better design to detect an effect size. This entry discusses the nature of statistical power, and further introduces basic procedures and caveats for conducting power analysis.
Probability Distributions
Authors: Lull Robert B.
Published: November 2017

This entry describes probability distributions and the basic concepts underlying probability, such as random sampling, levels of measurement, and sampling distributions of sample statistics. Some of the more commonly used distributions are described, such as binomial, normal,
t
, χ
2
, and
F
. Workable examples are provided for demonstration purposes.
Propensity Score Method
Authors: Yanovitzky Itzhak
Published: November 2017

Propensity score methods is a family of statistical techniques for modeling and testing causal relationships in observational studies. These techniques are designed to minimize bias in estimates of treatment effects due to subjects self‐selecting into treatment or nontreatment groups as opposed to being randomized to treatment. The propensity score is the probability of treatment assignment calculated for each subject conditional on observed baseline characteristics. Matching or classifying subjects on the propensity score has the effect of balancing any baseline differences among subjects, as would be the case when they are randomly assigned to treatment conditions, and therefore allows for non‐biased estimates of treatment effects.
Question Wording and Item Formulation
Authors: Elson Malte
Published: November 2017

In most empirical research with human subjects, at least some data are collected by recording self‐reported responses. However, the overlap between exhibiting a behavior and reporting about the same is less than perfect. Responding to self‐report instruments is itself a behavior governed by a number of factors, such as item wording, that can bias data and inferences. Thus, the methodology underlying the formulation of questions is essential to many topics in communication research. Researchers can adopt strategies to maximize the diagnostic value of their items and scales, such as avoiding item order effects, selecting an appropriate response format, reducing ambiguity in the wording, and controlling for careless responding.
Readership Research Methods
Authors: Maier Michaela , König Mathias , König Wolfgang
Published: November 2017

Readership research methods are mainly used to gather data regarding (a) the reach of specific print media and the characteristics of their audiences, and (b) the distribution of readers' attention among specific parts of the publication and evaluation of contents and layouts. Surveys and copy tests are the principal methods applied in such research. While (usually representative) polls are used to identify and describe the audiences of specific print products, classic and innovative forms of copy tests help to determine which parts of the product attract readers' interest.
Real‐Time Ratings
Authors: Seiffert‐Brockmann Jens , Jarolimek Stefan
Published: November 2017

Real‐time rating (RTR) is a method for the analysis and understanding of communication and its cognitive processing by individuals. RTR measures the spontaneous impression formation of individuals while they are exposed to media content. RTR is best used in combination with other methods such as content analysis, surveys, and psychophysiological measurements. Due to the technological progress in the field of computerization and network technology, RTR can now be applied more easily than ever before.