Content Analysis

• A content analysis is a way of converting qualitative data into quantitative data for analysis.
• A content analysis will explore the themes in qualitative data and count the number of similarities.
• Some common coding schemes used in content analysis are thematic coding, numerical coding, and categorical coding.
• The different types of content analysis include quantitative content analysis, qualitative content analysis, and mixed methods content analysis.
• Content analysis allows researchers to systematically analyse large amounts of data, identify patterns and themes, and draw objective conclusions based on the content of the data.
• Content analysis is a flexible and cost-effective method that can be applied to various types of data.
• Content analyses are common when investigating behaviour from qualitative sources.
• Content analyses increase in reliability as conclusions are based on quantitative data.
• Content analysis can use thematic analysis to highlight the themes in the qualitative data.
• Clinical and Child Psychology both use content analysis to investigate behaviour.
• Conclusions in content analysis can be made more reliable by using more than one researcher to analyse the information.
• Quantitative data in content analysis allows for statistical analysis, provides objective and measurable results, and allows for comparisons across different sources or time periods.
• Qualitative data in content analysis refers to non-numerical data that is collected through observations, interviews, or textual analysis, and is used to gain insights into the meanings, themes, and patterns within a given text or set of texts.
• Content analysis can be used to compare information across different time periods by systematically analysing and categorising the content of texts or media from each time period, allowing for quantitative comparisons of themes, trends, or changes over time.
• Content analyses interpret qualitative data, such as text or media, allowing researchers to uncover patterns, themes, and trends in the data.