First, I used statsmodels in python to perform linear regression on respondents age, household income and 'Disscore' to explore how well does discrimination relate to age and financial status, I also performed z tests to assess the relationship between perceived age discrimination and individual covariates, and multivariable logistic regression analysis to estimate the odds ratios of experiencing perceived age discrimination adjusting for all covariates. The discriminatory situations were analysed in separate models to identify the significance of different sociodemographic characteristics. Interactions between age and wealth were also tested. A cross-sectional design weight was applied to all analyses to correct for non-response.
To find the answer of my problem, I will set hypothesis first, the hypothesis is that older people suffers severer age discrimination in daily life. To test the hypothesis, I will classify samples into 4 groups:age between 50 - 59, age between 60 - 69, age between 70 - 79 and age over 80, based on the proportion of perceived ageism frequency, I plan to perform a z test. Additionally, I will select features of samples such as marital status, income and education level to run multilinear regression to see what factors correlated to the ageism phenomenon.
Results
All analyses were conducted using STATA 12. The primary outcome was the perception of age discrimination in any of the five discriminatory situations. The secondary outcomes were perceptions of age discrimination in each of the five situations.