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Content Analysis (Qualitative Methods in Social Research)


The qualitative method is one of the data collection and analysis approaches used in psychological research. The approach is common in psychological studies because social scientists can understand how individuals feel, think, or behave in particular situations. For example, psychologists use qualitative methods to help tell how patients experience their treatment process. The collected data is interpreted or analyzed using various techniques to determine the frequency of particular characteristics or traits. The common methods for analyzing qualitative data include grounded theory, framework, content, narrative, and discourse analysis. Throughout the current study, I will examine content analysis in detail. The paper will also provide a detailed content analysis of a given kind of popular media.

Description of Content Analysis as it Applies to Qualitative Method

Content analysis is a research method used to evaluate particular phases, ideas, or themes in a given qualitative data. Content analysis is a research approach for subjective interpretation of text content through a systematic categorization process of coding and defining patterns or themes (Marvasti, 2019). The definition of content analysis shows the approach is focused on an integrated view of texts or speech and their contexts. The research method extends beyond counting words or obtaining objective content to examining patterns and themes that might manifest in a given text (Marvasti, 2019. Therefore, content analysis allows researchers to comprehend social reality scientifically and subjectively.

While using content analysis, scholars and researchers quantify and assess the meanings and relationships of concepts, themes, or words. The qualitative data analyzed using content analysis include open-ended questionnaires, interviews, field research notes, conversations, or any occurrence of communicative language (Schreier et al., 2019). Comparing content analysis in qualitative research with its quantitative counterpart can improve one’s understanding of the research approach. The research areas upon which they are created are varied. While quantitative content analysis is broadly used in mass communications to count manifesting textual elements, it is often critiqued for missing semantic and syntactical info entrenched in the texts (Schreier et al., 2019). However, qualitative content analysis was developed mainly in sociology and psychology to explore the meanings of underpinning physical messages (Schreier et al., 2019). Therefore, qualitative content analysis is more inductive, basing its theme and topic examinations on the collected data. Qualitative content analysis is focused on generating a theory to be applied in quantitative analysis.

The qualitative content analysis comprises purposively chosen texts that inform the research questions being examined. The purposive sampling is appropriate for qualitative psychological research, especially where the researcher is more interested in informants with excellent knowledge about the investigative topic (Marvasti, 2019). Similarly, qualitative content analysis generates typologies or descriptions alongside expressions from subjects reflecting how they perceive the social world. The respondents’ perspectives can be better understood by the readers and investigators (Marvasti, 2019). Therefore, qualitative content analysis pays attention to that specific themes or patterns that show the range of meanings of a psychological or sociological phenomenon other than the occurrence’s statistical significance. The qualitative content analysis deals with antecedent forms or patterns of a given set of words in the actual research.

The content analysis starts during the initial phases of qualitative data gathering. The early involvement of the method allows one to move back and forth between data collection and concept development (Schreier et al., 2019). Therefore, the subsequent data collection methods focus more on addressing the research questions. For reliable and valid inferences, the content analysis includes transparent and systematic procedures for data processing. Regardless of the study goals, content analysis is more standardized and flexible with various steps (Schreier et al., 2019). The first step involves preparing the collected data. The sources of the data are justified in the first step. The second phase involves defining the data analysis units. The texts are classified in their basic form – the unit analysis can affect coding decisions and outcome comparability.

Thirdly, the researcher develops categories and coding schemes. The coding schemes are derived from theories, collected data, and related studies (Luo, 2019). The coding schemes are developed deductively or inductively. For consistent coding, numerous coders are used comprising names and rules of assigning codes. Fourth, the coding scheme of the text is tested to validate the scheme (Luo, 2019). The consistency needs to be checked in the current stage. The fifth stage involves coding all the texts as new themes continue to be identified. Throughout the stage, checking coding consistency will ensure that the meaning of texts does not deviate. The next step is concluding the coded data (Luo, 2019). The results’ transferability, credibility, confirmability, and dependability are used as the criteria to assess the research quality. The researcher later reports the methods and research findings.

Content Analysis of a Particular Form of Popular Media

Since the twentieth century, psychologists and other social scientists have been interested in popular media content. Scientists like Max Weber saw mass media as a tool for monitoring the societal “cultural temperature” (Schreier, 2020). Content analysis is often adopted to study a wide range of texts, from interview transcripts and clinical study discussions to TV programs’ narratives and magazines’ advertising content (Gaus et al., 2021). Popular media also uses content analysis to study propaganda. As a result, media content analysis has become an increasingly prevalent research methodology in social sciences and mass communication studies.

The media content analysis involves dismantling media texts using qualitative research approaches. Content analysis hints at a more organized and restricted method to eliciting information from popular media snippets (Devendorf et al., 2020). The approach can include watching a clip and engaging in open debate or discussion on the clip’s impact and themes. Today, media content analysis helps researchers understand popular media profiles by evaluating messages critics and assigning qualitative ratings to television, internet, and print coverage (Gaus et al., 2021). The media groups can also suggest suitable public responses or actions. Through this section, I will focus on depression presentation in popular media, particularly the internet coverage.

The public presentations for psychological disorders such as anxiety and depression influence illness beliefs and public stigma literacy. Numerous researchers have critically reviewed images, messages, and information concerning depression’s conceptualization, coping mechanisms, causes, and curability (Devendorf et al., 2020). The internet has thousands of contents, including articles, images, and videos about depression presentation in society. Journalists and researchers use content analysis to identify the public’s health literacy on depression and other psychological problems (Devendorf et al., 2020). The framing of healthcare messages impacts health-related behaviors and attitudes, especially in the mental health realm. Individuals obtain health data from social communication (such as YouTube or websites), external social environment (like family), or personal experiences (Gaus et al., 2021). Therefore, it is critical to examine the significance of messages, images, and information on depression coping and treatment.

Research on the topic focuses on aspects that define depression and anxiety, such as timeline, consequences, causes, treatment, strengths, conceptualization, and curability. The content analysis focusing on YouTube messages and content will help the researchers and readers understand the public presentation on depression (Devendorf et al., 2020). YouTube and other social media apps trigger essential discussions about a given topic affecting society. Unfortunately, few studies have systematically examined the public representation of mental illnesses (Gaus et al., 2021). Some researchers have explored survey data about community beliefs or reviews in print media. Meta-analysis indicates acceptance of biological causes has increased significantly over the years (Devendorf et al., 2020). But the role of public relations in informing the beliefs is unclear. Without systematic and qualitative data, psychological researchers can only guess about the incidence of particular presentations in popular media (Gaus et al., 2021). Therefore, content analysis remains an essential research tool to examine social media messages and classify them into different categories.

For the YouTube content analysis, there are numerous questions to consider. For example, the first question would be “what are the commonly mentioned causal models for anxiety or depression?” or “to what degree is anxiety presented as a categorically distinguished entity?”. Searches would include words such as ‘causes,’ ‘depression,’ ‘treatment,’ ‘anxiety,’ and ‘recurrence’ (Devendorf et al., 2020). The unit of analysis for the content involved every complete video, including text, images, and audio presentation. A codebook for the media content captured depression-related concepts or themes like the mention of recovery, causal presentation, mention of recurrence, treatment, or strengths. After the researchers developed the final codebook, a sample of twenty videos was coded to clarify the coding procedure (Devendorf et al., 2020). Sometimes, coders keep a notebook to record arising confusion, questions, or discussion thoughts. The researchers then meet to address the arising issues to ensure the accuracy of the themes.

Content analysis requires the researchers to establish a least moderate agreement on all codes by examining the inter-class correlation for continuous measures. Software packages such as SPSS can also determine the reliability of the content analysis results. According to Devendorf et al. (2020), the reliability rating for all codes met the least moderate agreement. Each video’s likes, dislikes, comments, and views provide critical information about the video’s acceptance and influence on the viewers. The content analysis matrix for the results is shown below:

Code Definition Public Presentation
Causes Beliefs about the contributing factors of depression “Negative thinking styles drive depression.”
Recurrence Beliefs about the chronicity of the disease “Once depression goes, it stays away.”
Consequences Perceived impacts of depression “Depression is treatable.”
Curability Person’s hope for recovery “Depression makes me late to work.”
Conceptualization Beliefs of perceived differentness of depression as a continuum or distinct entity “No two depressions are alike.”
Treatment Beliefs of perceived efficiency of treatment and coping behavior mindfulness practices, medication, therapy, diet/exercise

Qualitative content analysis aligns perfectly with knowledge about public beliefs about psychological diseases. Most causes were mentioned in the videos, and the results align with meta-analysis survey data (Devendorf et al., 2020). The biological and environmental causes were consistent with a German study where respondents were requested to show the common causes of depression (Devendorf et al., 2020). Further, the findings that informal therapy in the video content is more endorsed than medication align with community surveys from Canada and Australia (Devendorf et al., 2020). The main benefits of content analysis are collecting unobstructed data, transparency, and reproducibility, and it is incredibly scalable. However, a content analysis might unnecessarily disregard ambiguity and context to focus on phrases or words in isolation.

Extant Gaps in Literature and Recommendations for Further Research

Media has existed for hundreds of years in human society, and its changes influence or reflect human behavior. The analysis of media whether social media appear to be evocative of societal norms a human behavior (Schreier, 2020). Though the coding concept is complicated, no two individuals can code similarly in media analysis. A researcher can examine different media and develop a different set of research questions and analyses depending on their history and biases (Schreier, 2020). Today, social media apps display different content for each person, even when sharing friends and interests. People watch themed content on YouTube and later receive a different suggestion. In other words, social media apps provide various content that is analyzed to understand a particular phenomenon.

In the selected area, public presentations on depression were observed. Further investigation on content analysis should optimize the depression presentation and understand their impact on the public. Such study can be conducted using interviews or open-ended questionnaires. Further research can examine the combination of messages that generate salient implications for depression and human behaviors and attitudes (Gaus et al., 2021). Most YouTube videos indicate numerous themes, such as causes and curability. However, it is essential for future research to how numerous diseases interact.

Similarly, future studies can combine qualitative and quantitative content analysis to understand empirically-based public messages on YouTube. The research should also extend the framework and method to other mental disorders affecting society. For instance, more discussion and debate should be on whether addiction results from a bad environment or a psychological disease. Going beyond depression will ensure the public and researchers understand how other mental disorders are affected by the messages presented in popular media outlets. Therefore, qualitative content analysis generates high-reliability results that provide more details about a particular phenomenon.


Devendorf, A., Bender, A., & Rottenberg, J. (2020). Depression presentations, stigma, and mental health literacy: A critical review and YouTube content analysis. Clinical Psychology Review78, 101843.

Gaus, Q., Jolliff, A., & Moreno, M. A. (2021). A content analysis of YouTube depression personal account videos and their comments. Computers in human behavior reports3, 100050.

Luo, A. (2019). Content Analysis | A Step-by-Step Guide with Examples. Scribbr. Retrieved from

Marvasti, A. (2019). Qualitative content analysis: A novice’s perspective. SSOAR-Social Science Open Access Repository.

Schreier, M. (2020). Content analysis, qualitative. SAGE Publications Limited.

Schreier, M., Stamann, C., Janssen, M., Dahl, T., & Whittal, A. (2019). Qualitative Content Analysis: Conceptualizations and Challenges in Research Practice-Introduction to the FQS Special Issue” Qualitative Content Analysis I.” In Forum Qualitative: Qualitative Social Research (Vol. 20, No. 3, p. 26). DEU.


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