Analysis of results is a crucial step in any research or study. Marketing, human resources, consumer behavior or even basic human psychology are just some of the examples of research fields that require analysis. In all fields of research, results are either qualitative or quantitative. Qualitative results are information about qualities: data that cannot be quantified or measured. Some examples of qualitative data include the eye color of study participants, their age, gender. This is data about qualities or characteristics about the studied subject. On the other hand, quantitative results are information that can be counted, measured or quantified. Examples include the number of participants in a study, the number of cars they own, how much time they spend doing a certain activity or maybe how many of them are smokers. This type of data can be easily measured or calculated. What is Content Analysis? sometimes the research purpose requires qualitative data to be quantifiable; so what do we do when the data received from a research or study is all qualitative? The answer lies within Content Analysis, but what exactly is content analysis?
What Is Content Analysis?
Content analysis is an extensively used research procedure. Lately, it has become widespread among organizational researchers. A basic answer to what is content analysis: it is a method of research that converts qualitative data into quantitative figures. This is done by making effective interpretations through reading and coding the qualitative data. In other words, texts are assigned labels, also known as codes, to show the existence of important patterns. Qualitative data may include documents, texts, charts or even oral communications.
Content analysis helps in the study of many significant but difficult-to-study issues of interest to organizational researchers in many diverse areas. This may include organizational behavior strategy, managerial reasoning, human resources, societal issues management, technology and innovation management as well as international management.
In addition, content analysis methods can help in many aspects regarding business problems. It allows to bridge the gap between researches with large samples and ones with smaller samples. That is why each sample size has its own benefits and drawbacks. Small sample research can help in collecting primary data as well as in-depth analyses. However, it may suffer from external validity problems. On the other hand, large sample research may have internal validity issues. Therefore, content analysis will help in enhancing the quality of the organizational research by considering the benefits of both the small- sample research as well as the large sample research.
Moreover, content analysis allows the researcher to assess the proportions of patterns in the data, in addition to understanding the links between patterns. Programs that analyze the qualitative documents offer efficient work-flow and controlling tools for coding. However, simple computational methods can deliver descriptive information for instance length of documents. This is the main reason why computers are progressively used in content analysis.
Content Analysis Approaches
The application of content analysis revolves around three main methods/approaches: conventional, directed, and summative. All mentioned methods are similar in their intent; they are used to understand the meaning of a text from its content. Consequently, they all follow the realistic paradigm. On the other hand, there are several major differences among the approaches mentioned. These differences are mainly concerned with coding systems, origins of codes, as well as threats to credibility.
Conventional content analysis develops the labeling system directly from the text documents. However, during the analysis of a directed method the process starts with relevant research results as guidance for initial codes. Regarding a summative content analysis, it implicates counting and comparisons of keywords or content, followed by the interpretation of the underlying context. Nonetheless, understanding each approach in full detail is a significant matter to researchers. This is mainly to identify which method will work best in their study of interest.
What Is Content Analysis’s Three Approaches?
- Conventional content analysis
Conventional content analysis is commonly used with a study whose main intentions is to designate a certain incident. It is usually applicable when research literature is limited regarding the examined phenomenon. During conventional analysis, researchers avoid using fixed categories. As an alternative, they let the categories as well as their designated labels to flow from the literature. In addition, researchers usually engage themselves in the data in order to allow new observations to develop. This method is also known as inductive category development. Many qualitative approaches use this preliminary process to study design and analysis.
The process is as follows: first, data is analyzed in a general manner through reading all information continually. This is done in order to achieve engagement and gain a sense of the whole idea. This step is similar to one reading a novel. Next, data is read again, however this time it is cautiously prepared word by word. This step involves first highlighting the precise words from the information read that appear to capture crucial concepts. Then, researcher addresses the text by making notes of the main impressions, thoughts, and initial analysis found. This is done with the aim of developing codes. As this process lasts, labels for codes develop that reflect the main thoughts of the data it examines. After that, codes are organized into groups. The grouping system is done based on how codes are correlated. Thus, the grouping’s main aim is to group codes into meaningful clusters.
- Directed Content Analysis
The researcher would choose a directed approach to content analysis when preceding incomplete research about the examined phenomenon is available. The preceding research could help the researcher for developing additional research which is the main objective of using a directed approach. Additional benefits from existing prior research would include: supporting the development of the research question as well as providing forecasts about the key variables and the links between those variables. Consequently, this can help in determining the preliminary coding scheme. This is also known as deductive category application.
The process of using a directed approach is more organized than using a conventional approach. The process starts by identifying main variables as preliminary coding categories. Next, operational definitions for each category are determined. Based on the research question, data and the researcher’s goals, labeling/coding can follow two main strategies. If the research objective is to classify and sort all cases of the examined phenomenon then reading and highlighting all data that seems to represent the needed reactions is considered the best solution. The step that follows is to label all highlighted information by the predetermined codes. Any data that could not be coded from the existing coding scheme would be given a new code. The second strategy that can be used in directed content starts with immediate labeling using the predetermined codes. In this case, the researcher is confident that initial coding will not bias the identification of relevant text. Through the process, information that could not be given a code are recognized and analyzed later to decide if they denote a new category in the coding scheme.
- Summative Content Analysis
A study using a summative approach begins with classifying and counting specific words in the examined data. The main objective of this step is to investigate the usage of specific words and not understand the meaning of content. This type of analysis is also known as manifest content analysis. If the analysis process ends at this step, the analysis would be considered as a quantitative analysis. However, a summative method main goal includes discovering underlying data meaning through analysis as well as quantifying words. Understanding the meaning of data is also known as Latent content analysis.
As mentioned earlier, the process of a summative approach starts with counting the occurrences of the examined words. This can be done either by hand or by computer. Quantifying words in data can help in identifying patterns as well as inspect the codes. In addition, it helps in understanding the text related with the usage of certain words.
A summative approach has various advantages. First, it is an inconspicuous method to explore the concerned aspect. In addition, it can deliver simple insights on the usage of certain words. On the other hand, this approach has some drawbacks as well; the results using this method are restricted to the extensive meanings existing in the data. Besides, this approach generally depends on reliability, thus in order to validate reliability, the documented results have to be compatible with the interpretation.
In Short, What Is Content Analysis?
The simple answer to the previous question: What is Content Analysis? It is is a method used for describing written, verbal or graphic communications. Mainly, it develops a quantitative description that is derived from a qualitative one. This helps researchers in studying, developing and extending knowledge on different studies.
Content analysis has three main approaches that could be used in a certain study; conventional, directed, and summative. Depending on the research objective, different research designs and analysis are used. Therefore, choosing the type of approach to content analysis is based on the research objective and the research available beforehand. In addition, creating and following a coding scheme will increase the validity of a certain study.