Content analyses were developed to study newspaper articles and political speeches in the USA in the 1920s. They are based on the theory that repeating certain elements of discourse (words, expression or similar meanings) revealed the interests or concerns of the persons involved. Their purpose is to analyze the manifest contents of a communication.
The ‘content analysis’ category, includes several different methods which, although they all follow the steps presented in Figure 16.1, differ in terms of the coding units selected and the methods used for analyzing the results.
We will restrict ourselves herein to presenting the most common methods in organizational research, while acknowledging nevertheless that many other methods, such as those applied to newspaper studies, non-verbal communication analysis and linguistic analyses do exist (see, for more details, Robert, 1997; Stubbs, 1983).
1. Collecting Data
Content analysis is performed on data that has been collected according to nonstructured or semi-structured methods, such as (free or semi-directive) interviews.
Certain replies to questions in surveys can also be processed in this way. These are usually replies to simple open-ended questions, for example, ‘How do you evaluate your staff’s work?’ More generally, any kind of verbal communication or written material can be processed by content analysis.
2. Coding Data
As for any coding process, the discourse or text is broken down into units of analysis, then classified into the categories defined according to the purpose of the research.
2.1. Defining the units of analysis
There are basically two types of content analyses which can be defined according to the units of analysis defined:
- Lexical analyses, which are the more frequently used, examine the nature and range of vocabulary used in the discourse or text and analyze the frequency with which words appear. In this case, words are the unit of analysis.
- Thematic analyses adopt sentences, portions or groups of sentences as their unit of analysis. This last type is more common in organizational studies (D’Aveni and MacMillan, 1990; Dougherty and Bowman, 1995).
2.2. Defining categories
Depending on the coding unit selected, categories are usually described:
- Either in the form of a concept that will include words with related meanings (for example, the category ‘power’ could include words like strength, force or power). Computer-aided content analyses and their associated dictionaries, which automatically assign words to categories, can be used here. They offer several advantages: they reduce the amount of time spent on defining and validating categories, they standardize the classification process, and they facilitate comparisons with other studies.
- Or in the form of broader themes (for example, competitive strategies) which include words, groups of words or even whole sentences or paragraphs (depending on the unit of analysis defined by the researcher). The main difficulty lies in defining the breadth of selected categories. For example, a category like ‘organizational strategies’ is broader than ‘competitive strategies’ or ‘competitiveness factors’. Defining the breadth of the category must be related to both the researcher’s objectives (narrow categories make comparative analysis more difficult) and the materials used (it is easier to construct narrow categories based on rich, in-depth interviews than on letters to shareholders, which are generally more shallow).
- In certain cases, the categories may be assimilated to a single word. So there will be as many categories as there are different words that the researcher has decided to study (even if their meaning is similar). In this case, the words competitors and rivals, for example, will constitute two different categories.
- Finally, the categories can be characteristics of types of discourse, such as pauses, different intonations, grammatical forms or different types of syntax.
Defining categories before or after coding In the a priori method, categories are defined prior to coding – on the basis of experience or the results of earlier research. This method is used when attempting to verify hypotheses arising from other studies. The organizational verbal behavior classification system used by Gioia and Sims (1986) is an excellent example of a priori classification in which the categories stem from earlier research. Boland and Pondy (1986) also relied on a priori classification to code transcriptions of budgetary meetings. The categories were defined in terms of the decision model used (fiscal, clinical, political or strategic) and the mode of analyzing the situation (instrumental or symbolic).
In the ex post method, the categories are defined during the coding process. The choice of categories springs from the contents themselves. The idea is usually to create an inventory of the different themes in the text or discourse being studied. The text must be read and reread several times in order to isolate the essential themes in relation to the purpose of the study. Themes whose importance is underlined by repetition should suggest ideas for categories.
3. Analyzing Data
3.1. Quantitative analysis
Content analysis sprang from a desire for quantification in reaction to literary analysis. The qualitative notion was therefore foreign to its original concerns. So in general, the first step of the analysis is to calculate, for each category, the number and frequency of the units of analysis. Therefore, for each document studied, the number of units of analysis in each category studied is counted in order to deduce the category’s importance. The analyses performed in Boland and Pondy’s (1986) work dealt essentially with word-frequency counts. However, frequency calculation runs into several problems. In the first place, when the categories correspond to single words, they may have different meanings depending on their context (whence the need to combine both quantitative and qualitative analysis). In addition, the use of pronouns, which often are not counted, can bias the frequency analysis if it involves nouns only.
Researchers performing content analysis also have at their disposal various statistical data-analysis techniques, of which factor analysis is the most commonly used. It enables us, for example, to associate the presence of a greater or lesser number of units in a given category to the presence of a greater or lesser number of units in another category. Other types of analysis, such as regressions, discriminant analyses, and cluster analysis, can also be performed. It is up to each researcher to determine the most appropriate analyses for the purposes of the research. Therefore, to study the relation between managerial attribution and verbal behavior in manager-subordinate interaction during a simulated performance appraisal, Gioia and Sims (1986) performed content analysis on verbal behavior. This analysis was based particularly on a set of statistical analyses: multivariate analysis of variance, t test and correlation analysis.
3.2. Qualitative analysis
A more qualitative analysis, aimed at judging, rather than measuring, the importance of the themes in the discourse, can also be performed. The difference between quantitative and qualitative analysis lies in the way they perceive the notion of importance for a category: ‘how often’ for quantitative analysis or ‘theme value’ for qualitative analysis. Qualitative analysis tries to interpret the presence or absence of a given category, taking into account the context in which the discourse was produced (which can explain the presence or absence of certain categories). Qualitative analysis also allows for a more refined approach, studying the units of analysis in their context in order to understand how they are used (with which other units of analysis do they appear or are they associated to in the text?).
Qualitative analysis enables the researcher to go beyond simple content analysis of a discourse or document. It enables us to formalize the relations between the different themes contained in a communication in order to reveal its structure. Thanks to content analysis, it is equally possible to study the contents or the structure of a discourse or document.
Finally, content analysis can be used for descriptive, comparative or explanatory ends. Content analysis allows us to go beyond plain description of the contents of a communication and to discover the reasons for certain strategies or behaviors. By revealing the importance of certain themes in the discourse, content analysis can lead to explanations for the behaviors or strategies of the authors of the discourse analyzed. It is also possible to make certain unrecognized variables or influence factors appear, or to reveal relations between different organizational behaviors and different concerns of the organization’s leaders. By analyzing the contents of letters to shareholders, the work of D’Aveni and MacMillan (1990) succeeded in revealing the relation between the decisionmakers’ main points of interest (focused on the internal or external environment) and the companies’ capacity to weather a crisis.
Source: Thietart Raymond-Alain et al. (2001), Doing Management Research: A Comprehensive Guide, SAGE Publications Ltd; 1 edition.
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