Content Anlaysis - Statswork
Content
Analysis-Statswork
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.
- Download our Stats Work References such as tutorials,
proprietary materials, research projects and many more @ http://www.statswork.com/directory/
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
- careful attention is needed in separating the texts for
analysis,
- an appropriate sample of units for the analysis by
using a suitable sampling technique
- reliability in using the statistical measures and the
code used to analyse the textual content, and
- 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.
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
- Studies seeks to understand the messages in visual
texts often called as Semiotics
- From the language spoken by the people in producing the
meaning through texts often referred as discourse analysis.
- Capturing the hidden meaning in the texts — Interpretative
analysis
- Analysing the structure of talk — Conversation analysis
- 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 19th 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
References
- Coe, K., & Scacco, J. M. (2017). Content Analysis,
Quantitative. The International Encyclopedia of Communication Research
Methods, 1–11.
- Content analysis — https://en.wikipedia.org/wiki/Content_analysis
- Williamson, K., Given, L. M., and Scifleet, P. (2018).
Qualitative data analysis. Research Methods (2nd Edition). pp. 453–476.
- Bytheway, A. — Qualitative Research Without Money:
Experiences With a Home-grown Qualitative Content Analysis Tool — http://ci-journal.net/index.php/ciej/article/view/978/1058
- Roller & Lavrakas (2015).Applied Qualitative
Research Design: A Total Quality Framework Approach, pp. 241–244.
- Krippendorff, K. (1980). Content Analysis: An
Introduction to its Methodology. Beverly Hills: Sage Publications.
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