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Using video-based observation research methods in primary care health encounters to evaluate complex interactions

Onur asan , phd, enid montague , phd.

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Corresponding Author: Enid Montague, 750 North Lake Shore Drive Chicago, IL 60611, [email protected] Tel: + 1312.503.6454 Fax: 312.503.6455

The purpose of this paper is to describe the use of video-based observation research methods in primary care environment and highlight important methodological considerations and provide practical guidance for primary care and human factors researchers conducting video studies to understand patient-clinician interaction in primary care settings.

We reviewed studies in the literature which used video methods in health care research and, we also used our own experience based on the video studies we conducted in primary care settings.

This paper highlighted the benefits of using video techniques such as multi-channel recording and video coding and compared “unmanned” video recording with the traditional observation method in primary care research. We proposed a list, which can be followed step by step to conduct an effective video study in a primary care setting for a given problem. This paper also described obstacles researchers should anticipate when using video recording methods in future studies.

With the new technological improvements, video-based observation research is becoming a promising method in primary care and HFE research. Video recording has been under-utilized as a data collection tool because of confidentiality and privacy issues. However, it has many benefits as opposed to traditional observations, and recent studies using video recording methods have introduced new research areas and approaches.

Keywords: Primary care research, Video recording, Observations

1. Introduction

The health care system is complex and involves a range of people from various backgrounds and perspectives who communicate, interact, and collaborate. Several US Institute of Medicine reports have addressed major problems in healthcare delivery, such as medical errors, poorly designed medical technologies, and poorly designed work environments. 1 To this end, an Institute of Medicine (IOM) report proposed a partnership between health care and industrial and system engineering, including Human Factors Engineering (HFE), to create solutions for these problems. 2 HFE is the study of interactions of humans with the systems, products and environment and takes a system approach to study interactions. 3 Primary care is one of the main components of the health care system and involves the widest scope of health care, including a variety of demographics such as patients of different ages and socioeconomic backgrounds as well as patients with different kinds of chronic and acute health problems. 4 There are several HFE issues specific to the primary care environment that human factors researchers can address with various methods. Some of them are related to information processing, standardization, simplification, work pressure and work load, organizational design, information access, technology acceptance, usability, and the effect of EHR use on doctor-patient interaction. 5 Depending on the context, HFE researchers are tasked with determining which components of the system are likely to influence patient outcome measures (e.g. satisfaction, trust, and adherence to treatment). Therefore, the HFE discipline can play a major role in improving overall primary care health systems, leading to better health outcomes. 4

Observational research is a commonly used method in primary care studies. However, direct observation is not always the best choice for analysing primary care encounters 6 , as it is difficult for researchers to capture all details in a live setting, particularly when components occur simultaneously. 7 Video recording may eliminate some of the challenges that occur in direct observation research in a primary care setting 8 , 9 , since video recording accurately records clinical events, allows researchers to verify their observations, and allows for the collection of systematic feedback by means of strategic participant review. 10 Video data can also give researchers insight into the consistency between self-assessment and observable behavior. Finally, the video recording of subjects’ ongoing activities in their natural setting 11 can also be a particularly useful way to employ ethnographic studies in a complex primary care environment.

However, using video effectively requires determining appropriate research questions and identifying types of data required beforehand, to inform study design. Video recording research also requires technical knowledge to ensure the appropriate selection of cameras, video quality adjustment, and positioning of cameras. 12 , 13 Currently, enhanced video technology allows for richer data and facilitates the data collection process with alternatives such as multi-channel streams and remote controlled cameras. 14 , 15 It is essential to note that the research purpose may affect the type of technology used in the study design.

This paper outlines the steps for using video methods in a primary care setting. This paper also addresses potential benefits of using video observation and video analysis methods, which can be used by human factors and health care researchers in primary care settings.

1.1. Background on the use of video recording in primary care research

Primary care researchers began using video recordings to study consultations in the late 1970s. 16 In one early study, a communication analyst videotaped primary care consultations with a single video camera and subsequently analyzed the communication patterns between doctors and patients to improve doctors’ communication skills. 17 The results showed that doctors’ communication styles affected patient satisfaction. Recent studies have used video data to analyze nonverbal communication cues to inform more effective doctor-patient interactions. 18 – 20 Video data was also utilized to train doctors to improve their interactions with patients. 16 In addition, studies have used video recordings to explore doctor-patient-computer interactions. 21 – 29 These studies were instrumental in identifying the best spatial organization of an exam room, better design of exam-room computers, impact of computer use on communication and effective use of the computer by the doctor during the clinical visit. Several studies also utilized video elicitation interviews (which are basically interviews done after the recording, asking the doctors or patients to reflect on what they see on the video) to analyze doctor-patient interaction in the visits for teaching purposes. 30 , 31 Video elicitation allowed researchers to integrate the data from the video recording and participants’ related thoughts, beliefs and emotions obtained from the elicitation interviews. 32 Although traditional observation can provide a range of interesting and insightful information about primary care encounters, the encounter occurs through complex and multiple interactions, which can be explored by video data better. Finally, video data has also been used in health care settings in addition to primary care consultation for various purposes. 33

2. Considerations for collecting video data in primary care

Video-recording methods require careful planning in order to gather data that effectively answers potential research questions. Table 1 , which is derived from our experience of several studies 26 – 28 , summarizes the steps to conduct a video observation study in a primary care setting for a given problem.

Steps followed to conduct this video study

Some of the elements listed in different categories in table 1 have inter-dependent nature, for instance, number of participants, time frame of the study, time needed for ethical approval and the instruments may all have mutual effect. Furthermore, video data might have “identifiable private information” and involve human subject data, therefore requires some additional requirements for IRB review. 34 In video data collection, compared to traditional observation, studies conducted in US showed that physicians might have concerns about potential liability. 35 Therefore, there should be a consensus between administrators and investigators about the purpose of the research and the methods used. Studies in US reported that it can also be effective to have some strategies to overcome doctors’ concerns with confidentiality and liability, such as obtaining certificates of confidentiality 36 or becoming familiar with the liability coverage at the clinic where data will be collected. 37 As added protection, a previous study reported that patients were generally less worried than doctors about being videotaped. 32 However, it is still essential to get certificates of confidentiality to protect the participants’ identifiable information from forced disclosure. IRB approval requires confidentiality, but in the case of some sort of legal case (such as a malpractice case), the court might be able to force researchers to reveal this information. Certificates of confidentiality-which allows the investigator and others who have access to research records to refuse to disclose identifying information on research participants in any civil, criminal, administrative, legislative, or other proceeding, whether at the federal, state, or local level- might prevent this potential conflict between IRB and legal jurisdictions with respect to discoverability. 38

With technological advancements, some researchers have started to use more complex video methods for data collection to capture all interactions in detail - such as body language and gazing direction. 9 , 14 , 39 A multi-channel video might be a superior method to single-channel video depending on research question as it collects a greater amount of information, allowing the research to see both the care-provider and the patient simultaneously from different angles. 14 For instance, some researches created a multi-channel video technique and software to capture all the computer use (including screen-capture, key stroke, and mouse movement), and doctor-patient interaction in detail, which enabled them to view simultaneously all data relating to any time or activity. 25 Another study used multi-channel video recording focusing on the patient’s face, the physician’s face and the overall interaction to capture eye gaze patterns. 27 , 28

Furthermore, as video recording technology becomes more complex, researchers are faced with a wide variety of options, so it is important to choose the methods and equipment best suited to a given study. Researchers should standardize the camera operation protocols and have back up cameras in case of malfunctioning. In addition, multi-channel video and audio recording can collect so much data that the process of analysis becomes more complicated and time consuming. Therefore, it is essential to determine the specific research problems to minimize data collection and analysis time.

3. The benefits and drawbacks of video methods

Table 2 illustrates the pros and cons of traditional human observation method and video recording by “unmanned” cameras. This table was established based on our own experience and previous studies. 6 , 7 , 36 , 37 , 39 , 40 – 42

The benefits and drawbacks of video method and traditional observational method

Video methods can be effective for research that can be conducted in a single room (e.g. the patient exam room in a primary care clinic), since the cameras can be set up in a fixed position, specifically focusing on the interaction in the exam room. In addition, cameras can also be used in various ways based on research questions, because cameras can be carried, placed in multiple rooms, or cameras’ angle can be changed in real-time by remote control. When the required conditions are met, the video method can provide a rich collection of data. For instance, in one study, we used multiple small cameras with sufficient battery time and SD cards and hooked them to the walls or side of the desks in the room. Remote control was utilized to start and stop the camera and a remote control was left with the doctor so the doctor could stop the recording if the patients did not feel comfortable or the conversation topic becomes highly confidential, such as drug use or suicide.

Furthermore, video method also limits the Hawthorne effect -which is the possibility of altering the behavior of participants-, since video cameras have been shown to influence participant behavior far less than a human observer. 43 However, some people may be less willing to be videotaped as opposed to live observation and feel there is more risk involved in video data due to the several reasons: a) video recordings may be viewed by multiple people over time, b) outsiders may gain access to video data that is improperly stored, and c) a person’s identity may be more readily determined from a video recording than from written data. On the other hand, video data might improve ecological validity, since the video data gives more complete (and visual) information about the real environment rather than traditional observers’ observation notes. 44

4. Video data management and analysis

Observation data, including both video and non-video data, are confidential. However, video data introduce more risk to overall confidentiality because video data keeps all interaction in a high fidelity format for several years and might be accessed by multiple people for research or non-research purposes unless sufficient precautions are taken. Video data should be stored on a secure storage without links to other identifiable information, such as address, name, social security number. 32

Coding is a standard procedure to analyze the video data. Coding is an established procedure that facilitates analyzing the video by identifying the tasks and interactions in the video. 19 A coding scheme classifies variables of interest in the video according to the purpose of the analysis, and it speeds up the coding process. Development of coding scheme should be informed by the literature. 45 Each variable in the coding scheme should be well defined, and the start and stop time of all variables should be standardized. This may help to improve the reliability of data coding and decrease biases of different coders. For example, in one study, coders were interested in the gaze direction of the doctor and patient 46 and created a coding scheme including the subject (patient or doctor) and the object of the gaze (patient, care provider, computer, chart, etc.). This scheme allowed for a thorough and specific analysis of gaze based on subject, object, and duration, such as total duration of doctor’s gaze at computer and patient during a visit.

Video data can be coded both quantitatively and qualitatively depending on the purpose of the research. Quantitative data might include the duration of specific behaviors in the visit. Software packages can help quantify all continuous behavior (such as gazing or typing) to obtain relevant data with respective time frames. 27 It is also possible to visualize the sequence of the behaviors using software. Qualitative analysis might be a thematic description of a practitioner’s behavior during the entire visit, such as patient-focused or computer-focused. Qualitative data might also be gathered based on verbal communication, such as analyzing turn takings, sequence of utterances. 18 Some studies also used tools such as check lists (physicians’ behavior checklist) to capture human performance data from the video recording 47 , such as counting the occurrence of specific doctors’ behaviors during the doctor-patient encounter in the video data. 48

4.1. Video analysis tools

Several computer programs have been used to analyze videos effectively and accurately. These programs comprise different features to capture and analyze video and audio and can produce different types of results, such as numeric and visual. A few of these programs used in previous studies 27 , 44 , 49 , 50 are listed in Table 3 below.

Video Analysis Computer Programs utilized in several studies- partially adapted from (4, 43)

5. Potential uses of video data in primary care research

Evaluating complex constructs and interactions in real, complex, and dynamic clinical environments plays an important role in improving health care system; thus, it is a priority for HFE researchers. Effective functioning of the health care system depends on the interactions among people (patients, physicians, and other medical staff) and the interaction between people and technology. 4 Therefore their interactions should be explored in detail to improve overall health care systems. Video data can contribute to studies exploring doctor-patient interaction for different research purposes, such as analysing the decision-making process between doctor and patient 30 , determining the effects of nonverbal behaviors between patient and doctor that influence their decisions 31 , exploring factors which yield misunderstanding and disagreement during the interactions 51 , and investigating patients’ responsiveness to specific doctor behaviors. 52 One study also reported a list of seven different goals to use video recorded consultations. 39 Furthermore, video data can also contribute to the analysis of people-technology interaction in primary care settings. 53 For instance, it is critical to capture accurately both the pathways users take and the errors users commit while conducting a usability test of a mobile device. The traditional observation method might fail to obtain all data related to pathways and errors during real patient encounters, so video recording could record all necessary data from the screen to be analysed. In addition, with the integration of an eye gaze tracker, video data can provide rich information about eye gaze pathways to analyse the usability of medical software programs.

Video data has also been used to create and test a number of different interactions models in the primary care environment. Provided below is a list of several studies that used video data, along with the various methods and models they used to analyze verbal, nonverbal, and technology interactions in the clinical environment ( Table 4 ).

Type of analysis used by video observation studies

Video data can also contribute to doctors’ training since it provides an opportunity for doctors to review their own activities. 40 Multiple studies have recorded consultations in the primary care environment to assess clinical competence and design educational interventions. 14 Video data were also used with simulations for medical education. 67 Clinicians’ interaction style with patient and computer during the visit can influence patient outcomes such as satisfaction, trust, and adherence 68 , so video data analysis can also contribute to teaching medical students better ways of interacting with patients and EHRs during the encounter.

5.1. Video data and sociotechnical design

The components of a sociotechnical system include the individual (such as health care workers), tasks, tools and technologies, the physical environment, and organizational conditions. 69 It is essential to understand users of the system and interactions among these users in real settings to address socio technical design concerns. 70 It is also necessary to better understand the impact of boundaries on sociotechnical systems and their implications for physical, cognitive, and psychosocial ergonomics. Furthermore, effective design, implementation, and use of newly introduced technologies into the overall system is strongly related to the fundamentals of human factors ergonomics. 71 A number of studies have focused on the concept of sociotechnical factors that complicate health information systems deployment 72 , including the interaction between the technical features of a health information system and the social features of a health care work environment. 73 After a new system implementation, sociotechnical interactions have a direct effect on the success of the process. In the future, many new medical technologies will be introduced into the system. Video recording might also be a strong tool to explore technology interventions, which can make sociotechnical systems more effective and efficient. For instance, video data can be used to analyze the current medical technology such as Electronic Health Records (EHR) and to inform how new EHR can be integrated into the sociotechnical system more effectively.

6. Conclusion

Video-based observation research is a promising method in primary care and HFE research. Video recording has been under-utilized as a data collection tool because of confidentiality and privacy issues. However, it has many benefits, and recent studies using video recording methods have introduced new research areas and approaches. There are several possible applications of video recording in HFE and sociotechnical research as well as in traditional clinician training, such as performance evaluation and analyzing clinician-patient interactions. This paper is intended to prepare researchers for using video-based observation studies in primary care settings by evaluating the necessary steps involved, including the legal and confidentiality processes, technical aspects, data collection, and data analysis, and by describing its contribution to human factors research.

A systematic analysis of video recordings gives researchers opportunities to find solutions for human factors-related problems, as well as a sociotechnical systems analysis of interventions in primary care. Video recording method will be increasingly used in future research not only in the health care domain but also in other domains, such as usability and, social interaction. Video recording observation studies in primary care environment will continue helping to answer a variety of emerging research questions in primary care.

Acknowledgments

The project described was supported by the Clinical and Translational Science Award (CTSA) program, through the NIH National Center for Advancing Translational Sciences (NCATS), grant UL1TR000427.

• 1. Kohn LT, Corrigan JM, Donaldson MS. To err is human: building a safer health system. National Academies Press; 2000. [ PubMed ] [ Google Scholar ]
• 2. Reid PP. Building a better delivery system: a new engineering/health care partnership. Natl Academy Pr; 2005. [ Google Scholar ]
• 3. Dul J, Bruder R, Buckle P, Carayon P, Falzon P, Marras WS, van der Doelen B. A strategy for human factors/ergonomics: developing the discipline and profession. Ergonomics. 2012;55(4):377–395. doi: 10.1080/00140139.2012.661087. [ DOI ] [ PubMed ] [ Google Scholar ]
• 4. Carayon P. Handbook of human factors and ergonomics in health care and patient safety. CRC; 2007. [ Google Scholar ]
• 5. Beasley JW, Hankey TH, Erickson R, Stange KC, Mundt M, Elliott M, et al. How many problems do family physicians manage at each encounter? A WReN study. The Annals of Family Medicine. 2004;2(5):405–10. doi: 10.1370/afm.94. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 5. Carayon P. Emerging role of human factors and ergonomics in healthcare delivery–A new field of application and influence for the IEA. Work: A Journal of Prevention, Assessment and Rehabilitation. 2012;41:5037–40. doi: 10.3233/WOR-2012-0096-5037. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 6. Carthey J. The role of structured observational research in health care. Quality and safety in health care. 2003;12(suppl 2):ii13–ii6. doi: 10.1136/qhc.12.suppl_2.ii13. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 7. Wears RL. Beyond error. Academic Emergency Medicine. 2000;7(11):1175–6. doi: 10.1111/j.1553-2712.2000.tb00457.x. [ DOI ] [ PubMed ] [ Google Scholar ]
• 8. Jeffcott SA, Mackenzie CF. Measuring team performance in healthcare: Review of research and implications for patient safety. Journal of Critical Care. 2008;23(2):188–96. doi: 10.1016/j.jcrc.2007.12.005. [ DOI ] [ PubMed ] [ Google Scholar ]
• 9. Kumarapeli P, de Lusignan S. Using the computer in the clinical consultation; setting the stage, reviewing, recording, and taking actions: multi-channel video study. Journal of the American Medical Informatics Association. 2013;20(e1):e67–e75. doi: 10.1136/amiajnl-2012-001081. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 10. Seagull FJ, Guerlain S, editors. Observation Measure of Team Process and Performance in Health Care. Human Factors and Ergonomics Society; 2003. [ Google Scholar ]
• 11. Schaeff Jh. Videotape: New techniques of observation and analysis in anthropology. Principles of visual anthropology. 2009:255. [ Google Scholar ]
• 12. Theadom A, De Lusignan S, Wilson E, Chan T. Using three-channel video to evaluate the impact of the use of the computer on the patient-centredness of the general practice consultation. Informatics in Primary Care. 2003;11(3):149–56. doi: 10.14236/jhi.v11i3.563. [ DOI ] [ PubMed ] [ Google Scholar ]
• 13. Sheeler I, Koczan P, Wallage W, de Lusignan S. Low-cost three-channel video for assessment of the clinical consultation. Informatics in Primary Care. 2007;15(1):25–31. [ PubMed ] [ Google Scholar ]
• 14. Leong A, Koczan P, De Lusignan S, Sheeler I. A framework for comparing video methods used to assess the clinical consultation: a qualitative study. Informatics for Health and Social Care. 2006;31(4):255–65. doi: 10.1080/14639230600991668. [ DOI ] [ PubMed ] [ Google Scholar ]
• 15. Refsum C, Kumarapeli P, Gunaratne A, Dodds R, Hasan A, De Lusignan S. Measuring the impact of different brands of computer systems on the clinical consultation: a pilot study. Informatics in primary care. 2008;16(2):119–27. doi: 10.14236/jhi.v16i2.683. [ DOI ] [ PubMed ] [ Google Scholar ]
• 16. Pilnick A, Hindmarsh J, Gill V. Beyond ‘doctor and patient’: developments in the study of healthcare interactions. Sociology of Health & Illness. 2009;31(6):787–802. doi: 10.1111/j.1467-9566.2009.01194.x. [ DOI ] [ PubMed ] [ Google Scholar ]
• 17. Frankel R. The laying on of hands: Aspects of the organization of gaze, touch, and talk in a medical encounter. The social organization of doctor-patient communication. 1983:19–54. [ Google Scholar ]
• 18. Bensing JM, Verheul W, van Dulmen AM. Patient anxiety in the medical encounter: A study of verbal and nonverbal communication in general practice. Health Education. 2008;108(5):373–83. [ Google Scholar ]
• 19. Zandbelt L, Smets E, Oort F, Godfried M, de Haes H. Patient participation in the medical specialist encounter: does physicians’ patient-centred communication matter? Patient Education and Counseling. 2007;65(3):396–406. doi: 10.1016/j.pec.2006.09.011. [ DOI ] [ PubMed ] [ Google Scholar ]
• 20. Bensing J, Tromp F, Van Dulmen S, van den Brink-Muinen A, Verheul W, Schellevis F. Shifts in doctor-patient communication between 1986 and 2002: a study of videotaped general practice consultations with hypertension patients. BMC Family Practice. 2006;7(1):62. doi: 10.1186/1471-2296-7-62. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 21. Pearce C, Kumarpeli P, de Lusignan S. Getting seamless care right from the beginning-integrating computers into the human interaction. Studies in health technology and informatics. 2010;155:196. [ PubMed ] [ Google Scholar ]
• 22. Frankel R, Altschuler A, George S, Kinsman J, Jimison H, Robertson NR, et al. Effects of Exam-Room Computing on Clinician-Patient Communication: A Longitudinal Qualitative Study. Journal of General Internal Medicine. 2005;20(8):677–82. doi: 10.1111/j.1525-1497.2005.0163.x. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 23. Pearce C, Dwan K, Arnold M, Phillips C. Analysing the doctorpatientcomputer relationship: the use of video data. Informatics in Primary Care. 2006;14(4):221–6. [ PubMed ] [ Google Scholar ]
• 24. Pflug B, Kumarapeli P, van Vlymen J, Chan T, Ammenwerth E, de Lusignan S, editors. Measuring the impact of the computer on the consultation: An application to synchronise multi-channel video, automated monitoring, and rating scales. 2008. [ Google Scholar ]
• 25. De Lusignan S, Kumarapeli P, Chan T, Pflug B, Van Vlymen J, Jones B, et al. The ALFA (Activity Log Files Aggregation) toolkit: A method for precise observation of the consultation. Journal of Medical Internet Research. 2008;10(4) doi: 10.2196/jmir.1080. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 26. Asan O, Montague E. Technology-mediated information sharing between patients and clinicians in primary care encounters. Behaviour & Information Technology. 2013:1–12. doi: 10.1080/0144929X.2013.780636. (ahead-of-print) [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 27. Asan O, Montague E. Physician interactions with electronic health records in primary care. Health systems. 2012;1(2):96–103. doi: 10.1057/hs.2012.11. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 28. Montague E, Asan O. Dynamic modeling of patient and physician eye gaze to understand the effects of electronic health records on doctor–patient communication and attention. International journal of medical informatics. 2014;83(3):225–34. doi: 10.1016/j.ijmedinf.2013.11.003. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 29. Kazmi Z. Effects of exam room EHR use on doctor-patient communication: a systematic literature review. Informatics in Primary Care. 2014;21(1):30–9. doi: 10.14236/jhi.v21i1.37. [ DOI ] [ PubMed ] [ Google Scholar ]
• 30. Saba GW, Wong ST, Schillinger D, Fernandez A, Somkin CP, Wilson CC, et al. Shared decision making and the experience of partnership in primary care. The Annals of Family Medicine. 2006;4(1):54–62. doi: 10.1370/afm.393. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 31. Henry SG, Forman JH, Fetters MD. ‘How do you know what Aunt Martha looks like?’ A video elicitation study exploring tacit clues in doctor–patient interactions. Journal of evaluation in clinical practice. 2011;17(5):933–9. doi: 10.1111/j.1365-2753.2010.01628.x. [ DOI ] [ PubMed ] [ Google Scholar ]
• 32. Henry SG, Fetters MD. Video elicitation interviews: a qualitative research method for investigating physician-patient interactions. The Annals of Family Medicine. 2012;10(2):118–25. doi: 10.1370/afm.1339. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 33. Iedema R, Forsyth R, Georgiou A, Braithwaite J, Westbrook J. Video research in health. Qualitative Research Journal. 2006;6(2):15–30. [ Google Scholar ]
• 34. Menikoff J. Where’s the Law-Uncovering the Truth about IRBs and Censorship. Nw UL Rev. 2007;101:791. [ Google Scholar ]
• 35. Guerlain S, Turrentine B, Adams R, Calland JF. Using video data for the analysis and training of medical personnel. Cognition, Technology & Work. 2004;6(3):131–8. [ Google Scholar ]
• 36. Broyles L, Tate J, Happ M. Videorecording in clinical research: mapping the ethical terrain. Nursing research. 2008;57(1):59. doi: 10.1097/01.NNR.0000280658.81136.e4. [ DOI ] [ PubMed ] [ Google Scholar ]
• 37. Weinger M, Gonzales D, Slagle J, Syeed M. Video capture of clinical care to enhance patient safety. Quality and Safety in Health Care. 2004;13(2):136. doi: 10.1136/qhc.13.2.136. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 38. Wolf LE, Zandecki J, Lo B. The certificate of confidentiality application: a view from the NIH Institutes. IRB: Ethics and Human Research. 2004;26(1):14–8. [ PubMed ] [ Google Scholar ]
• 39. Coleman T. Using video-recorded consultations for research in primary care: advantages and limitations. Family Practice. 2000;17(5):422. doi: 10.1093/fampra/17.5.422. [ DOI ] [ PubMed ] [ Google Scholar ]
• 40. Mackenzie C, Xiao Y. Video techniques and data compared with observation in emergency trauma care. Quality and Safety in Health Care. 2003;12(suppl 2):ii51. doi: 10.1136/qhc.12.suppl_2.ii51. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 41. Grimshaw AD. Sound-Image Data Records for Research on Social Interaction. Sociological Methods & Research. Special Issue on Sound-Image Records in social Interaction Research. 1982;11(2):121–144. [ Google Scholar ]
• 42. Pearce C, Arnold M, Phillips C, Dwan K. Methodological considerations of digital video observation: beyond conversation analysis. International Journal of Multiple Research Approaches. 2010;4(2):90–99. [ Google Scholar ]
• 43. Pringle M, Stewart-Evans C. Does awareness of being video recorded affect doctors’ consultation behaviour? The British Journal of General Practice. 1990;40(340):455. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 44. Brewer MB. Research design and issues of validity. Handbook of research methods in social and personality psychology. 2000:3–16. [ Google Scholar ]
• 45. Creswell JW. Qualitative inquiry & research design: Choosing among five approaches. Sage Publications, Inc; 2007. [ Google Scholar ]
• 46. Montague E, Xu J, Chen P, Asan O, Barrett BP, Chewning B. Modeling Eye Gaze Patterns in Clinician–Patient Interaction With Lag Sequential Analysis. Human Factors: The Journal of the Human Factors and Ergonomics Society. 2011 doi: 10.1177/0018720811405986. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 47. Ong L, De Haes J, Hoos A, Lammes F. Doctor-patient communication: a review of the literature. Social Science & Medicine. 1995;40(7):903–18. doi: 10.1016/0277-9536(94)00155-m. [ DOI ] [ PubMed ] [ Google Scholar ]
• 48. Blanchard CG, Labrecque MS, Ruckdeschel JC, Blanchard EB. Information and decision-making preferences of hospitalized adult cancer patients. Social science & medicine. 1988;27(11):1139–45. doi: 10.1016/0277-9536(88)90343-7. [ DOI ] [ PubMed ] [ Google Scholar ]
• 49. Harrison BL, editor. Proceedings of the Human Factors and Ergonomics Society Annual Meeting. SAGE Publications; 1991. Video annotation and multimedia interfaces: From theory to practice. [ Google Scholar ]
• 50. De Lusignan S, Kumarapeli P, Debar S, Kushniruk A, Pearce C. Using an open source observational tool to measure the influence of the doctor’s consulting style and the computer system on the outcomes of the clinical consultation. Studies in Health Technology and Informatics. 2009;150:1017–21. [ PubMed ] [ Google Scholar ]
• 51. Cegala DJ, McGee DS, McNeilis KS. Components of patients’ and doctors’ perceptions of communication competence during a primary care medical interview. Health Communication. 1996;8(1):1–27. [ Google Scholar ]
• 52. Cromarty I. What do patients think about during their consultations? A qualitative study. The British Journal of General Practice. 1996;46(410):525. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 53. Pearce C, Dwan K, Arnold M, Phillips C, Trumble S. Doctor, patient and computer--A framework for the new consultation. International Journal of Medical Informatics. 2009;78(1):32–8. doi: 10.1016/j.ijmedinf.2008.07.002. [ DOI ] [ PubMed ] [ Google Scholar ]
• 54. Hermansson GL, Webster AC, McFarland K. Counselor deliberate postural lean and communication of facilitative conditions. Journal of Counseling Psychology. 1988;35(2):149–53. [ Google Scholar ]
• 55. Roter DL. Patient participation in the patient-provider interaction: the effects of patient question asking on the quality of interaction, satisfaction and compliance. Health Education & Behavior. 1977;5(4):281. doi: 10.1177/109019817700500402. [ DOI ] [ PubMed ] [ Google Scholar ]
• 56. Connor M, Fletcher I, Salmon P. The analysis of verbal interaction sequences in dyadic clinical communication: A review of methods. Patient Education and Counseling. 2009;75(2):169–77. doi: 10.1016/j.pec.2008.10.006. [ DOI ] [ PubMed ] [ Google Scholar ]
• 57. Hall JA, Irish JT, Roter DL, Ehrlich CM, Miller LH. Gender in medical encounters: An analysis of physician and patient communication in a primary care setting. Health Psychology. 1994;13(5):384. doi: 10.1037//0278-6133.13.5.384. [ DOI ] [ PubMed ] [ Google Scholar ]
• 58. Burchard KW, Rowland-Morin PA. A new method of assessing the interpersonal skills of surgeons. Academic Medicine. 1990;65(4):274–6. [ PubMed ] [ Google Scholar ]
• 59. Kraan H, Crijnen A, Zuidweg J, Van der Vleuten C, Imbos T. Communicating With Medical Patients. Newbury Park, CA: Sage; 1989. Evaluating undergraduate training—a checklist for medical interviewing skills; pp. 167–77. [ Google Scholar ]
• 60. Duggan P, Parrott L. Physicians’ nonverbal rapport building and patients’ talk about the subjective component of illness. Human Communication Research. 2001;27(2):299–311. [ Google Scholar ]
• 61. Als AB. The desk-top computer as a magic box: patterns of behaviour connected with the desk-top computer; GPs’ and patients’ perceptions. Family practice. 1997;14(1):17. doi: 10.1093/fampra/14.1.17. [ DOI ] [ PubMed ] [ Google Scholar ]
• 62. Collins L, Schrimmer A, Diamond J, Burke J. Evaluating verbal and non-verbal communication skills, in an ethnogeriatric OSCE. Patient Education and Counseling. 2010 doi: 10.1016/j.pec.2010.05.012. [ DOI ] [ PubMed ] [ Google Scholar ]
• 63. D’Agostino TA, Bylund CL. The Nonverbal Accommodation Analysis System (NAAS): Initial application and evaluation. Patient Education and Counseling. 2010 doi: 10.1016/j.pec.2010.07.043. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 64. Newman W, Button G, Cairns P. Pauses in doctor–patient conversation during computer use: The design significance of their durations and accompanying topic changes. International Journal of Human-Computer Studies. 2010;68(6):398–409. [ Google Scholar ]
• 65. Pearce C, Trumble S, Arnold M, Dwan K, Phillips C. Computers in the new consultation: Within the first minute. Family Practice. 2008;25(3):202–8. doi: 10.1093/fampra/cmn018. [ DOI ] [ PubMed ] [ Google Scholar ]
• 66. Mast MS, Hall JA, Klöckner C, Choi E. Physician gender affects how physician nonverbal behavior is related to patient satisfaction. Medical Care. 2008;46(12):1212–8. doi: 10.1097/MLR.0b013e31817e1877. [ DOI ] [ PubMed ] [ Google Scholar ]
• 67. Cooper JB, Barron D, Blum R, Davison JK, Feinstein D, Halasz J, et al. Video teleconferencing with realistic simulation for medical education* 1. Journal of clinical anesthesia. 2000;12(3):256–61. doi: 10.1016/s0952-8180(00)00148-3. [ DOI ] [ PubMed ] [ Google Scholar ]
• 68. Roter D, Frankel R, Hall J, Sluyter D. The expression of emotion through nonverbal behavior in medical visits. Journal of General Internal Medicine. 2006;21(S1):S28–S34. doi: 10.1111/j.1525-1497.2006.00306.x. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 69. Carayon P. Human factors of complex sociotechnical systems. Applied Ergonomics. 2006;37(4):525–35. doi: 10.1016/j.apergo.2006.04.011. [ DOI ] [ PubMed ] [ Google Scholar ]
• 70. Wilson JR. Fundamentals of ergonomics in theory and practice. Applied ergonomics. 2000;31(6):557–67. doi: 10.1016/s0003-6870(00)00034-x. [ DOI ] [ PubMed ] [ Google Scholar ]
• 71. Lawler EK, Hedge A, Pavlovic-Veselinovic S. Cognitive ergonomics, socio-technical systems, and the impact of healthcare information technologies. International Journal of Industrial Ergonomics. 2011 [ Google Scholar ]
• 72. Berg M. Implementing information systems in health care organizations: myths and challenges. International journal of medical informatics. 2001;64(2–3):143–56. doi: 10.1016/s1386-5056(01)00200-3. [ DOI ] [ PubMed ] [ Google Scholar ]
• 73. Ludwick D, Doucette J. Adopting electronic medical records in primary care: lessons learned from health information systems implementation experience in seven countries. International journal of medical informatics. 2009;78(1):22–31. doi: 10.1016/j.ijmedinf.2008.06.005. [ DOI ] [ PubMed ] [ Google Scholar ]
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The Power of Video: Why to Add Video to Your Quantitative Research

In today’s world, where data-driven decisions are the norm, integrating video into your quantitative research strategy can provide a transformative edge. Traditionally, quantitative research has relied heavily on numbers and statistical significance to inform decisions. However, video is emerging as a powerful tool to complement and enhance these data-driven insights.

By Aneesh Dhawan

Integrating video into your quantitative research strategy offers substantial benefits that go beyond traditional survey data. By providing rich context, improving communication, capturing emotional and behavioral insights, and validating findings, video can significantly enhance the value and impact of your research. Embracing video as a complementary tool can lead to more comprehensive, engaging and actionable insights, positioning your business for greater success in an increasingly competitive market.

Here’s why adding video to your quantitative research can be a game-changer for your business.

The strategic advantage of integrating video into quantitative research

Enhanced context and interpretation.

Quantitative research excels at revealing “what” is happening through numbers and patterns, providing a clear view of trends, frequencies and correlations. However, it often falls short of explaining the “why” behind these findings. This gap in understanding can limit the depth of insights derived from purely numerical data.

Video adds a critical layer of context that bridges this gap by capturing the subtleties of human behavior, interaction and response. Unlike static numbers, video can illustrate how consumers interact with products, react to marketing stimuli and articulate their thoughts and feelings. This visual and auditory information provides a richer, more nuanced understanding of the underlying factors driving quantitative trends.

A study published in the International Journal of Qualitative Methods emphasizes this point, highlighting that integrating visual and video data can significantly enhance the interpretation of quantitative results. According to the study, combining video with quantitative analysis helps to contextualize numerical data, leading to a more comprehensive understanding of consumer motivations and preferences. By observing and analyzing real-world interactions and emotional responses captured on video, researchers can gain insights that are not readily apparent from numerical data alone. This enriched perspective allows for more informed decision-making and strategic planning.

Increased engagement and comprehension

Video content is inherently more engaging than static data presentations, a fact supported by extensive research into digital content consumption. According to HubSpot’s 2024 research, visual content, including video, is 40 times more likely to be shared on social media compared to text alone.  This significant disparity in sharing rates underscores the powerful role that video plays in capturing attention and driving engagement.

The increased engagement associated with video content can have a direct impact on the comprehension and retention of research findings. When data is presented through dynamic video summaries, it becomes more accessible and relatable. Videos can use visual storytelling techniques, such as animations, infographics and real-life examples, to illustrate complex data points in a more digestible format. This visual and auditory stimulation not only makes the content more memorable but also helps stakeholders grasp key insights more effectively.

Additionally, videos often evoke stronger emotional responses than text-based content. This emotional engagement can further enhance the retention of information. According to a report by Forbes, 95% of viewers remember a call to action after watching a video, compared to just 10% who retain this information after reading text.  By leveraging these video advantages, organizations can ensure that their research findings resonate more deeply with their audience, leading to more actionable and impactful decision-making.

Effective communication of complex findings

Video content is inherently more engaging than static data presentations, and this increased engagement can have a profound impact on how research findings are communicated and understood. Unlike traditional text-based reports or spreadsheets, which often present data in a straightforward and sometimes dry manner, video introduces a dynamic element that captures viewers’ attention and makes complex information more accessible.

One of the primary reasons video content is so compelling is its ability to combine visual, auditory and emotional elements. Through animations, infographics and real-life footage, video can illustrate data in a way that is not only visually stimulating but also easier to comprehend. For example, a video summary of research findings might include animated graphs that highlight trends over time, while a voiceover provides a narrative that explains the significance of these trends in plain language. This multimodal approach helps break down complex concepts and makes them more digestible for a broader audience.

Moreover, videos can effectively utilize storytelling techniques to make the data more relatable. By incorporating real-life examples, case studies or testimonials, videos can contextualize numbers and statistics within real-world scenarios. This narrative approach helps viewers connect emotionally with the content, which can enhance their understanding and retention of the information. For instance, a video showcasing customer feedback and reactions can provide a more vivid picture of consumer sentiments than a chart displaying survey results alone.

Emotional and behavioral insights

These visual and auditory cues are crucial for understanding the full spectrum of consumer responses. Emotions such as excitement, frustration or satisfaction can be more clearly observed through video than inferred from quantitative data alone. For instance, while a survey might show a neutral average rating for a product, a video recording might reveal that while participants are verbally neutral, their body language and expressions convey frustration or disappointment. Such insights can help businesses understand the root causes of customer satisfaction or dissatisfaction that raw numbers might not fully reveal.

Moreover, video can capture contextual factors that influence consumer behavior. Observing how consumers interact with products in real-life settings can uncover environmental or situational variables that affect their responses. For example, a video might show how a product’s placement in a store or its packaging affects consumer decisions, providing insights that are not immediately apparent from sales data alone. This contextual understanding allows businesses to make more informed adjustments to product design, marketing strategies or customer service practices.

Video also enables researchers to gather detailed behavioral data. By observing how consumers use a product or navigate a website, researchers can identify patterns and pain points that quantitative metrics might not fully capture. For example, a video recording of users interacting with a website can reveal where they struggle or become frustrated, offering valuable insights into usability issues that could impact user satisfaction and conversion rates.

Enhancing quantitative research with video: Gaining deeper insights

Embracing video as part of your research toolkit can lead to richer insights, improved stakeholder engagement and more effective strategies. As the research landscape evolves, leveraging video to enhance quantitative analysis positions your business to better understand and respond to consumer needs, driving success and innovation in a dynamic environment.

Knit’s approach: Quantitative + video research

At Knit, we harness the combined power of quantitative and video data to provide a comprehensive view of your research needs. Our platform seamlessly integrates over 100 quantitative question varieties with VOC Video OE responses to ensure you can address both the “what” and the “why” behind your key questions. With Knit, you can swiftly gather hundreds of video responses within hours, delivering insights that are six times richer than traditional qualitative methods.

And don’t stress over the need to analyze (let alone watch) those hours and hours of video footage. Knit will analyze all of your qual data – summarizing findings, generating themes and subthemes, and visualizing it into digestible charts. Our no-code video editing and storytelling tools further enhance this experience, allowing you to easily clip, compile and share compelling customer stories and showreels. By bringing the voice of your consumer to life, Knit enables you to engage more deeply with your data and share impactful insights across your team. Experience the next level of research with Knit and turn data into actionable, dynamic stories – schedule your demo today .

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Quantitative video analysis for qualitative research, next episode, introduction.

Overview Teaching: 10 min Exercises: 5 min Questions What is a digital video file? Objectives Understand the basics of digital video, including framerate, number of planes and bitrate.

Introduce yourself Please spend one minute to introduce yourself in the digital pad we will use during the workshop.

Why video analysis?

For researchers interested in studying humans and human motion, a regular video recording is often the easiest, fastest and cheapest solution to start with. Nowadays, everyone has access to fairly high quality video cameras even in their mobile phones, and the cost of professional-quality video cameras is also within the reach for many researchers.

One of the positive things about video analysis is that it opens for a broad range of analysis techniques, from purely qualitative methods to purely quantitative. For example, having a video recording that can be played back multiple times, and at various speeds, is very useful for visual inspection. And, as we shall see later, even a regular video recording can be used to extract meaningful quantitative motion data. Furthermore, it is also common to use video recordings as a reference when recording motion with sensor-based motion tracking technologies. In such cases, the video recording can be used to help the qualitative interpretation of numerical results.

What is a video file? Let us start by understanding more about what a video file contains. Find a video file on your computer, for example the file dance.avi from the example folder. On most systems (Linux, Mac, Windows) you should be able to see some basic information about the video content by selecting something like “properties” from the file inspector. What are the dimensions? What is the framerate? What type of compression is used? The important thing to understand here, is that a digital video file can be seen as a series of still images. This opens for doing various types of mathematical operations on the file.

This is a typical file dialog with information about a file:

Here we can see the dimensions (640 pixels wide, 480 pixels tall) and framerate (30 frames per second).

Compression

The properties of the video file described above above, shows that the video stream has been compressed with the H.264 standard. This is the most common video compression codec these days. It is a lossy codec , meaning that it throws away lots of data when it compresses the file. The H.264 standard is also a time-based compression codec, meaning that it compares frames over time, and only stores the information that change between so-called keyframes. This is an efficient way of creating good-looking videos, but it is less ideal for analytical purposes.

For analysis we often prefer to use MJPEG (Motion JPEG), which is a format that stores a complete image for each frame. This leads to larger video files, but faster and easier processing.

When dealing with video files in Matlab, we have found that the good old .AVI container format is the most reliable. Matlab can handle other formats too, but .AVI files are the only ones that work on all platforms (Linux, Mac, Windows).

Video as a stream of numbers

As this figure illustrates, a video file is just a series of matrices with numbers:

In a normal video file, each pixel is stored with a number between 0 and 255, where 0 means black and 255 means white. Colour files have four planes, while greyscale images only need one plane.

Recording video for analysis

One thing to bear in mind is that a video recording meant for analytical purposes is quite different from a video recording shot for documentary or artistic purposes. The latter type of video is usually based on the idea of creating an aesthetically pleasing result, which often includes continuous variation in the shots through changes in the lighting, background, zooming, panning, etc. A video recording for analysis, on the other hand, is quite the opposite: it is best to record it in a controlled studio or lab setting with as few camera changes as possible. This is to ensure that it is the content of the recording, that is, the human motion, which is in focus, not the motion of the camera or the environment.

Even though a controlled environment may be the best choice from a purely scientific point of view, it is possible to obtain useful recordings for analytical purposes also out in the field. This, however, requires some planning and attention to detail. Here are a few things to consider:

Foreground/background: place the subject in front of a background that is as plain as possible, so it is possible to easily discern between the important and non-important elements in the image. For computer vision recordings it is particularly important to avoid backgrounds with moving objects, since these may influence the analysis.

Lighting: avoid changing lights, as they will influence the final video. In dark locations, or if the lights are changing rapidly (such as in a disco or club concert), it may be worth recording with an infrared camera. Some consumer cameras come with a “night mode” that serves the same purpose. Even though the visual result of such recordings may be unsatisfactory, they can still work well for computer-based motion analysis.

Camera placement: place the camera on a tripod, and avoid moving the camera while recording. Both panning and zooming makes it more difficult to analyse the content of the recordings later. If both overview images and close-ups are needed, it is better to use two (or more) cameras to capture different parts of the scene in question.

Image quality: it is always best to record at the highest possible spatial (number of pixels), temporal (frames per second) and compression (format and ratio) settings the camera allows for. However, the most important is to find a balance between image quality, file size and processing time.

As mentioned earlier, a video recording can be used as the starting point for both qualitative and quantitative analysis. We will here look at a couple of different possibilities, moving from more qualitative visualisation methods to advanced motion capture techniques.

Preparing video for analysis in Matlab

You can use a number of different types of video for analysis, but if we should highlight a few things, these would be:

• Video: use MJPEG (Motion JPEG) as the compression format. This compresses each frame individually. Use .AVI as the container, since this is the one that works best on all platforms.
• Audio: use uncompressed audio (16-bit PCM), saved as .WAV files (.AIFF usually also works fine). If you need to use compression, MP3 compression (MPEG-1, Layer 3) is still more versatile than AAC (which is used in .MP4 files). If you use a bitrate of 192 Kbs or higher, you should not get too much artifacts.

FFMPEG is a very useful (free) tool for doing all sorts of audio/video manipulation, and can be installed on most systems. It it somewhat intimidating for beginners, but the trick is just to know what works. Here is a oneliner that will convert from an .MP4 file into a .AVI file with MJPEG and PCM audio:

Key Points A video file contains a series of images, and each image is a matrix that can be operated on. A framerate of 25 fps means that there are 25 frames (images) per second. The bit rate tells about the variation of each pixel, an 8-bit image stores values from 0-255. A colour image uses 4 planes, Alpha, Red, Green, Blue, while greyscale only uses 1 plane.

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Content Analysis – Methods, Types and Examples

Table of Contents

Content analysis is a widely used research technique that systematically examines and interprets textual, visual, or multimedia content to identify patterns, themes, and meanings. It is a cornerstone method in qualitative research but can also be employed quantitatively to measure the frequency of certain elements within data. This article explores the definition, methods, types, and examples of content analysis, highlighting its importance and applications across various fields.

Content Analysis

Content analysis is a research method used to analyze, categorize, and interpret the content of communication in a systematic and replicable manner. It involves breaking down material—such as text, images, or audio—into manageable data categories, often to identify trends, patterns, or underlying themes.

For example, a researcher analyzing political speeches might use content analysis to quantify how often certain keywords, like “freedom” or “equality,” are used and interpret their significance in shaping public opinion.

Key Features of Content Analysis

• Systematic Approach: Content analysis involves clearly defined rules and procedures to ensure consistency and replicability.
• Flexible Data Sources: It can analyze a variety of content types, including written documents, video recordings, and social media posts.
• Dual Purpose: It serves both qualitative purposes (understanding themes) and quantitative purposes (measuring frequency or volume).

Importance of Content Analysis

Content analysis plays a significant role in research for the following reasons:

• Understanding Communication: It helps researchers explore the meaning, structure, and function of communication.
• Tracking Trends: Content analysis is useful for monitoring changes in cultural norms, public opinion, or market behavior over time.
• Cross-Disciplinary Applications: This method is used in various fields, including sociology, marketing, media studies, and psychology.

Types of Content Analysis

1. qualitative content analysis.

Qualitative content analysis focuses on understanding the underlying themes, patterns, and meanings within a dataset. It is interpretative in nature, often exploring how content conveys emotions, opinions, or values.

For example, analyzing customer reviews to identify recurring sentiments about a product, such as satisfaction or dissatisfaction.

2. Quantitative Content Analysis

Quantitative content analysis involves counting the frequency of specific elements, such as words, phrases, or symbols, within a dataset. This type of analysis is used to quantify content trends.

For instance, studying how often particular political ideologies are mentioned in news articles during an election cycle.

3. Summative Content Analysis

Summative analysis combines both qualitative and quantitative approaches. It starts with quantitative counting and progresses into qualitative interpretation, providing a richer understanding of the context.

For example, counting mentions of “sustainability” in corporate reports and then examining how the term is used to frame environmental initiatives.

4. Relational Content Analysis

Relational analysis explores relationships between concepts, phrases, or themes in a text. It identifies connections and assesses how ideas are interrelated within the content.

For instance, analyzing a novel to determine how often two characters are mentioned together and what this implies about their relationship.

Methods of Conducting Content Analysis

1. define research questions and objectives.

Clearly articulate what you aim to discover through content analysis. For example, a marketing researcher might ask: “How do customers describe our brand on social media?”

2. Select Data Sources

Choose appropriate content sources, such as books, social media posts, videos, or interviews, depending on the research objectives.

3. Develop a Coding Framework

Establish categories and codes to classify data systematically. Codes can be predefined (deductive approach) or generated from the data itself (inductive approach).

4. Analyze Data

• Quantitative Approach: Count the frequency of codes or themes.
• Qualitative Approach: Interpret the significance of patterns and relationships.

5. Interpret Results

Evaluate findings in the context of the research questions, identifying key insights, trends, or patterns.

Steps in Content Analysis

• Data Preparation: Gather and organize the content to be analyzed.
• Coding: Segment data into meaningful categories or codes.
• Categorization: Group similar codes into broader themes.
• Analysis: Examine the data for trends, patterns, or relationships.
• Validation: Ensure reliability by double-checking the coding process or using multiple coders.
• Reporting: Present findings in a structured format, such as tables, graphs, or narratives.

Examples of Content Analysis

Example 1: social media analysis.

A business analyzing customer feedback on Twitter might use content analysis to identify common themes, such as product satisfaction, customer service complaints, or brand loyalty.

Example 2: Political Campaigns

Researchers studying election campaigns might examine speeches, advertisements, or social media posts to determine the frequency of keywords like “progress” or “change” and interpret their appeal to voters.

Example 3: Academic Research

A scholar analyzing gender representation in children’s books might classify characters based on gender roles and count their frequency to highlight disparities.

Example 4: Market Research

Content analysis of customer reviews on e-commerce platforms can reveal recurring themes, such as product durability, value for money, or delivery experiences.

Advantages of Content Analysis

• Versatility: Applicable to diverse data types, including text, visuals, and multimedia.
• Non-Intrusive: Uses pre-existing data, eliminating the need for direct interaction with subjects.
• Quantitative and Qualitative Integration: Combines numerical and interpretative insights.
• Rich Insights: Provides an in-depth understanding of communication patterns and underlying themes.

Disadvantages of Content Analysis

• Time-Intensive: Coding and analyzing large datasets can be laborious.
• Subjectivity in Interpretation: Qualitative content analysis is prone to bias, especially if coding frameworks are inconsistent.
• Limited Context: Analyzing isolated content may overlook broader contextual factors.
• Over-Reliance on Frequency: Quantitative content analysis may prioritize volume over significance.

Applications of Content Analysis

• Media Studies: Analyzing news articles or advertisements to identify biases, trends, or representations.
• Marketing: Exploring customer feedback to understand brand perception and preferences.
• Health Communication: Evaluating public health campaigns to determine their effectiveness in raising awareness.
• Education: Studying educational materials to assess inclusivity or curriculum focus.
• Sociology: Investigating societal attitudes by examining cultural artifacts, such as films, books, or songs.

Content analysis is a versatile and powerful research method for examining communication and extracting meaningful insights. By categorizing and interpreting data systematically, researchers can uncover patterns and trends across diverse fields, from media and marketing to sociology and education. While it requires careful planning and execution, the ability to analyze and interpret both qualitative and quantitative aspects of content makes it an invaluable tool for academic and practical applications.

• Krippendorff, K. (2018). Content Analysis: An Introduction to Its Methodology (4th ed.). SAGE Publications.
• Neuendorf, K. A. (2017). The Content Analysis Guidebook (2nd ed.). SAGE Publications.
• Weber, R. P. (1990). Basic Content Analysis (2nd ed.). SAGE Publications.
• Elo, S., & Kyngäs, H. (2008). The qualitative content analysis process. Journal of Advanced Nursing , 62(1), 107-115.
• Mayring, P. (2000). Qualitative content analysis. Forum: Qualitative Social Research , 1(2).

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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