CONTENT ANALYSIS

In Brief

• You will find the best Research Areas/Topics for future researchers enrolled in  Statswork . These topics are researched in-depth at the University of Columbia, brandies, Coventry, Idaho, and many more and many more.
• In order to identify the future research topics, we have reviewed the stats work on the content analysis in text, images data.
• The present article was developed based on background data conducted on recent peer-reviewed articles addressing stats work.
• Qualitative content analysis is an approach is used to analyze the data that focuses on describing the topics that are evident in the context of words.
• Content analysis was originally introduced to bring out the analytical rigor in the communication sciences.
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In this data-centric world, researchers find data in images and textual form.  To start with, this is difficult to tell in the abstract. However, I will try to explain what a content analysis is and how it is useful in analyzing text, images data.

What is a Content Analysis?

To begin with, in the content analysis, the images or text contents are transferred to a measurable quantity through coding. The big challenge is involved in the construction of a coding scheme that is, selecting a suitable sampling technique like cluster sampling and a proper statistical measure for Data Analysis. Content analysis is of two types: 1. Quantitative content analysis, and 2. Qualitative content analysis (Coe & Scacco 2017). Qualitative content analysis focusses on content generated by the existing repository of information such as television content, newspapers, historic documents, content from media transcripts, blogs, telephonic conversations, and written communications through email, letters, etc. Whereas the Quantitative content analysis focusses on methods, where the text data are methodically recorded then those data are categorized for the statistical data analysis. Qualitative content analysis is an approach to analyze the data that focus on describing the topics that are evident in the context of words and meanings when framed against the research objectives of the study (Roller & Lavrakas 2015). The requirements for conducting a successful content analysis are

Figure 1: The Graph showing the Native women experience more violence because they’re Native women

Figure 2: The Graph showing the journey of the company

1. careful attention is needed in separating the texts for analysis,
2. an appropriate sample of units for the analysis by using a suitable sampling technique
3. reliability in using the statistical measures and the code used to analyse the textual content, and
4. validity of the method used.

A more frequent applications of content analysis, is in analysing the textual data from the newspapers and magazines or a recorded/written material like administrative speeches, advertisements in television or other media. For instance, one may wish to analyse the ongoing hot topic from the news, or to analyse the number of times a particular word has appeared in a public speech, etc., then the content analysis will be a suitable one for these kind of instances. In all these instances, the text data are documented and converted into numerical data for analysing the trends and patterns.

Regularized and methodical observations of patterns in the textual data clearly differentiate the quantitative content analysis from other research methodologies in explaining the trends and interpreting the textual content(for example, qualitative content analysis, text mining, and discourse analysis) (Krippendorff 1980). Data scientist engaging with such qualitative methods are often suitable for small number of texts in the data whereas; quantitative content analysts is suitable for a larger number of textual data and the numerical transformations provides the patterns and trends for the study.

Researchers used the Qualitative Data Analysis to make sense of the sampled data, comes in various approaches, which tend to be associated with particular conceptual areas and methods such as cluster analysis. Depending on the objective of the study, the textual data from the content analysis can served as response or explanatory variables and finally could be analysed in a sequential order with other methods. For example, qualitative content analysis from a most popular show in the television may give us idea for identifying the suitable explanatory variable in an effective way and could be used in testing and experimenting the effects of media.

Qualitative content analysis possess a unique characteristic which focuses on a sequential process of developing the meanings from the sampled textual data based on new findings. Quantitative content analysis works by framing the coding scheme before the data collection and analyse them by modifying the same. However, qualitative content analysis keeps on updating the methodical scheme while the data collection itself to provide better understanding from the sampled data (Williamson et al 2018). In this way, qualitative content analysis makes the researchers to do the analysis in a cyclic way for better way of interpreting the data.

Content analysis was originally introduced to bring out the analytical rigor in the communication sciences. Examples of content analysis involves the length of the articles in the newspaper, which can be measured in inches or in a different way. In order to identify the contents, the calculation does not need the whole data, but a fraction of it say, 20 – 30% may be sufficient. Cohen’s kappa is one such measure to be used for the content analysis.

Here is the list of area where content analysis could be useful

1. Studies seeks to understand the messages in visual texts often called as Semiotics
2. From the language spoken by the people in producing the meaning through texts often referred as discourse analysis.
3. Capturing the hidden meaning in the texts – Interpretative analysis
4. Analysing the structure of talk – Conversation analysis
5. Data oriented analysis to develop theories.

There are three major purpose of using the content analysis. Firstly, to make inference about the antecedents of communications, eg. Analysing the legal and evaluative evidence, to infer the cultural change, etc. Secondly, to make or describe the inference about the characteristic of communications eg. Analysing the pattern of communications, infer about the trends in message communications, etc. Lastly, to make inference about the consequences of communications eg. in assessing the responses to communication, and in analysing the flow of information’s, etc. 

During the 19^th century, the textual data from newspapers are analysed manually to measure the number of lines and space for a given subject. However, in recent times, with the advent of contextual data, strong technological skills and rich computer configurations makes the more complex problem into simple computational techniques. Thus, content analysis is an emerging statistical topic especially in the big data field. In order to identify the better technique, it is utmost important to know the objectives you intend to achieve. Depending upon your objective, you can select which type of content analysis will be a best match. R

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