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Challenges of Secondary Data Analysis: Analyzing Psychological Distress and Positive Mental Health of the Food Service Workers in Canada

By: Published: 2020 | Product: SAGE Research Methods Cases Part 2
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This case study will report on a secondary data analysis from the cross-sectional 2012 Canadian Community Health Survey–Mental Health, analyzing the levels of mental health and psychological distress of food service workers in Canada with the Demand-Control-Support model. It draws linkages between work conditions, stress levels, and mental health of employees in the restaurant industry. Prior to this research, few studies analyzed these linkages within this particular sector. This study also highlights the importance of considering both positive and negative factors affecting mental health. Secondary data analysis helps the student to understand how scientific research works and its challenges. The first challenge is data preparation, which is exhausting and repetitive! Researchers must clean the database, personalize the data with new code, and recode all the variables needed for the analysis. Despite this effort, secondary data analysis can be a very valuable method for making sense of large data sets such as the one used in this research: the 2012 Canadian Community Health Survey–Mental Health.

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Secondary data analysis

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