Analyzing media messages Daniel Riffe, Stephen Lacy and Frederick Fico
H1 – Introduction
Quantitative content analysis (defined in H2) involves:
- Drawing representative samples of content
- Training coders to use rules and
- Measuring the reliability of coders in applying the rules
The collected data are then analyzed to describe typical patterns or characteristics or to define important
relationships among the content qualities examined. Content analysis assumes an empirical approach.
An important distinction involves reductionism (understanding comes through reducing a phenomenon to
smaller, more basic, individual parts) and holism (wholes can be more than or different from the sum of
their individual parts). Perspective: animal and human behaviors can be seen as a stimulus-response
complex.
Assumptions about media effects:
- Assumptions about powerful media effects are consistent with the early-20th-century behaviorist
tradition and contribute to early models or theories of communication effects that used metaphors
such as hypodermic needle or bullet. Content analysis found a legitimate home within the powerful
effects perspective because of the implicit causal role for communication content described in the
models. It was believed to have an effect, so it was important to study.
- The assumption of powerful effects was later replaced by the limited effects approach.
- Contingency effects approach = effects of mass media are contingent on a variety of factors and
conditions.
1
, Content analysis remains an important tool for researchers exploring more directly how individual-level
cognitive processes and effects relate to message characteristics. News content is the product or
consequence of routines, practices and values, constructed by news workers and reflects both the
professional culture of journalism and the larger society. Symbols that show up in media messages at
particular points in time are consequences of the dominant culture and ideology and reflect cultural
values.
Goals of research projects may be
- Theory building
- Reality checks
- Descriptive studies / ‘real-world’ comparisons
Content analysis has a long history of use in psychology, because messages presumably indicate the
psychological state of the communicator.
H2 – Defining content analysis as a social science tool
Quantitative content analysis = the systematic and replicable examination of symbols of communication,
which have been assigned numeric values according to valid measurement rules, and the analysis of
relationships involving those values using statistical methods, to describe the communication, draw
inferences about its meaning, or infer from the communication to its context, both of production and
consumption.
- Systematic – in terms of method (scientific) and design (planning of procedures)
- Replicable – objectivity, reproducibility; personal views should not influence method or findings and
concepts should be operationalized
- Symbols of communication – all communication uses verbal, textual or visual symbols; the
meaning of these symbols vary from person to person, depending on medium and functions
- Numeric values according to valid measurement rules and statistical analysis of
relationships – numeric values are used to represent measured differences; validity of rules and
consistency/reliability of their application are crucial; quantitative content analysis reduces the set of
units to numbers that retain important information about the content units but are amenable to
arithmetical operations that can be used to summarize or describe the whole set
- Describing and inferring – simple description of content has its place in communication research
and from this data, researchers seek to answer theoretically significant questions by inferring the
meaning or consequences of exposure to content or inferring what might have contributed to the
content’s form and meaning; a researcher must be guided by theory.
Criticism on quantitative content analysis:
- Method puts too much emphasis on comparative frequency of different symbols’ appearance
2
H1 – Introduction
Quantitative content analysis (defined in H2) involves:
- Drawing representative samples of content
- Training coders to use rules and
- Measuring the reliability of coders in applying the rules
The collected data are then analyzed to describe typical patterns or characteristics or to define important
relationships among the content qualities examined. Content analysis assumes an empirical approach.
An important distinction involves reductionism (understanding comes through reducing a phenomenon to
smaller, more basic, individual parts) and holism (wholes can be more than or different from the sum of
their individual parts). Perspective: animal and human behaviors can be seen as a stimulus-response
complex.
Assumptions about media effects:
- Assumptions about powerful media effects are consistent with the early-20th-century behaviorist
tradition and contribute to early models or theories of communication effects that used metaphors
such as hypodermic needle or bullet. Content analysis found a legitimate home within the powerful
effects perspective because of the implicit causal role for communication content described in the
models. It was believed to have an effect, so it was important to study.
- The assumption of powerful effects was later replaced by the limited effects approach.
- Contingency effects approach = effects of mass media are contingent on a variety of factors and
conditions.
1
, Content analysis remains an important tool for researchers exploring more directly how individual-level
cognitive processes and effects relate to message characteristics. News content is the product or
consequence of routines, practices and values, constructed by news workers and reflects both the
professional culture of journalism and the larger society. Symbols that show up in media messages at
particular points in time are consequences of the dominant culture and ideology and reflect cultural
values.
Goals of research projects may be
- Theory building
- Reality checks
- Descriptive studies / ‘real-world’ comparisons
Content analysis has a long history of use in psychology, because messages presumably indicate the
psychological state of the communicator.
H2 – Defining content analysis as a social science tool
Quantitative content analysis = the systematic and replicable examination of symbols of communication,
which have been assigned numeric values according to valid measurement rules, and the analysis of
relationships involving those values using statistical methods, to describe the communication, draw
inferences about its meaning, or infer from the communication to its context, both of production and
consumption.
- Systematic – in terms of method (scientific) and design (planning of procedures)
- Replicable – objectivity, reproducibility; personal views should not influence method or findings and
concepts should be operationalized
- Symbols of communication – all communication uses verbal, textual or visual symbols; the
meaning of these symbols vary from person to person, depending on medium and functions
- Numeric values according to valid measurement rules and statistical analysis of
relationships – numeric values are used to represent measured differences; validity of rules and
consistency/reliability of their application are crucial; quantitative content analysis reduces the set of
units to numbers that retain important information about the content units but are amenable to
arithmetical operations that can be used to summarize or describe the whole set
- Describing and inferring – simple description of content has its place in communication research
and from this data, researchers seek to answer theoretically significant questions by inferring the
meaning or consequences of exposure to content or inferring what might have contributed to the
content’s form and meaning; a researcher must be guided by theory.
Criticism on quantitative content analysis:
- Method puts too much emphasis on comparative frequency of different symbols’ appearance
2
