Content analysis, Identify a research problem, determine a relevant population, select a representative sample, define measurement categories and analyze the results

CONTENT ANALYSIS – AN INTRODUCTION

Content analysis is a research tool used to determine the presence of certain words or concepts within texts or sets of texts. Researchers quantify and analyze the presence, meanings and relationships of such words and concepts, then make inferences about the messages within the texts, the writer(s), the audience, and even the culture and time of which these are a part. Texts can be defined broadly as books, book chapters, essays, interviews, discussions, newspaper headlines and articles, historical documents, speeches, conversations, advertising, theater, informal conversation, or really any occurrence of communicative language.

CONTENT ANALYSIS:

People have defined content analysis in many ways:-         

            Walizar and Wienir (IW8) defined it as “any systematic procedure devised to examine the content of recorded information”.

            Krippendorf (1980) defines it as a “research technique or making replicable and valid references from data to their context”.          

Kerlingers (1386) definition is “fairly typical content analysis is method of studying and analyzing communication in a systematic objective and quantities”.

Ole Holsti defined content analysis as "any technique of making interferences by objectively and systemically identifying specified characteristic of message”.

Taken together, these definitions clarify two critical functions of content analysis.

Ø    To provide a systematic and verifiable ascription of manifest and latent content of narrative discourse.

Ø    To produce logically valid and reliable inferences about a narrative's content. The context of discourse refers to its “empirical environment."

IDENTIFYING RESEARCH PROBLEM:

The first step entails deciding exactly what one wants to know, including whether the descriptive, inferential or both. One general aims have been identified the researcher must clarify specific objectives.

One problem is to avoid in content analysis is the "counting-for-the-sake-of counting" syndrome. The ultimate goal of the analysis must be clearly articulated, to avoid aimless exercises in data collection that has little utility for mass media research.

                 Example:

One might want to describe persuasive strategies used in prime time television advertising or alternately to infer conclusion about the social effect of the roles blacks play in movie today.

Notice that these hypothetical problem of interest (persuasive strategies used in prime time television advertising or alternately, to infer conclusions about the social effects of the roles blacks play in moves today.

The hypothetical objectives not only specify the problem of interest but they also clarify the narrative data base that is suitable for studying the problem. The second inquiry stage elaborates on the critical question of finding appropriate data bases.

DETERMINING A RELEVANT POPULATION:

To "determine the relevant population” is to specify the boundaries of the body of content to be considered, which requires an appropriate operational definition of the relevant population. If researchers are interested in analyzing the content of popular songs they must define what is meant by a popular song. All songs listed in T. V.'s Hot song" chart or the back page of “Radio & Record", the top SU songs or the top IO? They must also ask what time period will be considered: The six month, a month only?

Basically two dimensions are used to determine the relent population for a content analysis (i.e.). The topic area and the time period. The significance of topic area is greater. The specification of the topic area should be logically consistent with the research question and related to the goals of the study.

The time period to be examined should be sufficiently long so that the phenomenon under study has ample chance to occur.

Be clearly sp6cify the topic area and the time period, the researchers is providing a basic requirement of content analysis: a concise statement that spell out the parameters of the investigation.

                 Example:

This study consider the news content on the front page of the " Washington Post" and the "New York Times" ex ding Sundays, from January 1 to December 31" of the past year.

SELECTING A REPRESENTATIVE SAMPLE:

Once the research problem and its associated population and data base have been Clarified, the analyst must select a representative sample of data.

There are two preliminary steps. The researcher must opertionalize the population of interest.

                 Example:

If a researcher is interested in studying how social minorities are portrayed on television. To concretize the narrative population what “television" consist of for this study. Commercial television only? If so, what aspects of commercial television? Prime time programme only? If so, are advertisements included? What programming time period is used for defining the population? The past six moths? The past weak? When population boundaries are precisely defined, the researcher must specify the smallest entities with a population that are of interest (i.e.) the elements.

                 Example:

In a study of television portrayal of women, elements might be all female characters that appear in drama of comedy shows or prime time commercial television. Alternately we might decide to limit our study to female characters who have speaking roles. To become more restrictive, we defend our elements as female characters having major or starring roles.

SAMPLE SELECTION APPROACHES:

When the element of a well defined population has been specified, the researchers in ready to select a representative sample. Two sample selection approaches are often used.

1.         The researcher can construct a sampling frame that lists every element in population, and then select a representative sample using simple random, systematic or stratified probability methods. Since this approach can be quite time a consuming especially with large populations, a multistage cluster procedure is often used.

2.         A representative sample of subsets is selected from the population, where-upon either a representative sample is analyzed or all the elements appearing in the selected subsets are studied.

                 Example:

Say, one is interested in television portrayal of minorities. Suppose that his population consists of "all time commercial television programmers excluding advertisements that were broadcast ever the past six moths." and the elements are minority characters with speaking roles."

Using single-stage sampling approach, indeed to identify every minority character with speaking role in six months' worth of prime time programming, a further formidable task. Once a list (sampling frame) has been complied, he will apply a method like simple random sampling or systematic approach involves choosing every ` k'th (perhaps every 50^th or every 75^th) element for the sample, picking the first element for a random.

Using multistage cluster approach, he might begin by selecting at random collection of weeks (primary sampling unit) from the within each selected week randomly select a series of days (secondary sampling unit). He can then select at random a group of minorities characters from with in each programming day as his final sample. A randomly selected group of programmers from with each selected clay might be useful as an added sampling unit. His final sampling unit or elements, minority speaking roles would then be drawn from the sampling of television programme. If a multistage cluster method includes three or more stages, researchers often analyze the entire array of elements appearing in the last selected clusters. Taking this approach, he should study every minority seeking character that appears in the final set randomly selected programs.

DEFINING MEASUREMENT CATEGORIES

The researcher is now ready to perform one of the most important task in content analysis; devising a scheme of measuring data. Like congenital data, narrative data or usually measured nominally by grouping related information into categories. For example, a researcher analyzing television portrayal of women might measure elements by sorting them into the categories developed Suzanne Pingree and her associates:

1.         Decorative sex objects.

2.         Full time house wives and mothers.

3.         House wives and mothers who have culturally insignificant outside work.

4.         Working professional women.

5.         Women having  no explicit or implicit gender-based roles.

Whether research is begins with pre-formulated categories or generate their categories during observation depends on whether they take a theory-to-data or a data-to-theory research perspective. Theory-to-data researcher developed categories in advance; based on contextual formation, that they believe will adequately characterize a given narrative.

The measurement process then serves as a mean of testing the predetermined categories system. In a data-to-theory analysis, there are no preconceived categories; rather, these emerged naturally from narrative content. The preferred approach depends on whether appropriate category system is available and on the research objectives -principally whether a researcher wishes to develop or to test theoretical categories.

Regardless of perspective ail content analyst employ raters or judges to independently sort narrative data into related categories, a process called coding.

ANALYZING THE RESULT

The result of content analysis is typically represents the frequency with which elements are placed into each category associated with the chosen nominal scales.

Pingree and her colleague applied above given five category scale of female role to a random sample 447 advertisements that appeared during one year in "Play boys", "Time", "News Week" and "Ms" magazines.

The resulting data showed that across ail four magazines 27% of the ads fell into the `' Decorative sex object" category, 48% portrayed women as full time housewives and mothers, 4% were coded as working housewives and mothers, 19% depicted women as independent professionals and 2% of ads evidenced no sexual stereotyping. Pingree and her associates used a chi-square statistic to analyze these frequencies of significance differences. Results are displayed as numerals, usually frequencies, which are then statistically tested for significant population differences.