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my For research project I will be studying the effects on income by age as well as education. The initial research question asks: what is the effect that age has on the respondents income? The reader should be interested to know whether or not a person's income increases as they get older. There will always be exceptions to questions like this, for example, the rare individual that strikes it rich at a very young age. Most people would assume that as people get older, they make more money, up until a certain age where there is less opportunity for promotions or retirement becomes a factor. For purposes of this project I will only be looking at the association of age only up to a certain point and grouping all people over fifty together. Two Variable Hypothesis: Older individuals are more likely to receive a higher income than younger individuals. There are several reasons that I expect that my research will show this correlation. First, is that age and experience go hand in hand. Older individuals have probably received more training in their respective careers based on the fact that they have had more time to receive training. Second, older respondents may have spent more time within one career and will have been promoted throughout the years. Independent Variable: Age Age is conceptualized as the respondents age, in years, when they were administered the survey. The original GSS question is a numerical data question which asks the age of the respondent. To simplify the tables for this analysis I recoded the variable into four categories. Under 25 The GSS surveys only individuals eighteen years and older. Therefore, under twenty five is actually respondents age eighteen to twenty five. 26-35 All respondents who are at least twenty six years old and under thirty six years old. 36-49 All respondents who are at least thirty six years old and under fifty years old. 50+ - All respondents who are at least fifty years old. The GSS is question is a numerical response and only includes data up to ninety nine years old. Therefore 50+ is actually individuals from age 50-99. Dependent Variable: Income Income is the respondents total family income for the year 2004. The original GSS question is as follows: In which of these groups did your total family income, from all sources, fall last year before taxes, that is? Just tell me the letter. Therefore, to conceptualize income, income is the total amount of money that the household received within the specific year before taxes. I recoded the original GSS data into two categories: Under $25,000 Under $25,000 means that the family's total income was between $0 and $24,999. $25,000 or more The total family income was any number $25,000 or higher. Measurement Validity Each question used appears to have good face validity. Asking respondents age is a reliable question and directly relates to the topic of the research question. Family income is also directly related to the project. The questions also establish content validity because they cover just about any response that an individual would make. Age is numerical so all responses are included, income groups respondents into two groups, all individuals fall into one of the two categories. Causal Validity By looking at the two-variable table the reader can see that there is a roughly a 23% association between age and income. Just by seeing this association it seems that income increases with age, but it is also obvious that there are many other factors that could be involved, making this association one that is in fact, spurious. A researcher would have a difficult time arguing age as the only factor to the respondents total income. Population and Sample The population this for study is all US residents in the year 2004. The sample for the GSS survey is 2,457 random US residents in the year 2004. The GSS contains data for several decades but I filtered the results to only include the year 2004 so that the sample size was a reasonable amount of people for this project. The sample size was less than 2600 most likely because some individuals choose not to respond to questions about their income. Two Variable Analysis As I stated earlier, the table shows roughly a 23% association between age and income. That association being between individuals under 25 and those age 36-49. There is a slight drop off in income seen in respondents over fifty years old, about an 8% association is seen. My hypothesis predicted an association between age and income and 23% seems like a reasonably large enough association to support the hypothesis, although it is most likely spurious. Three Variable Hypothesis: Using education to control the relationship between age and income will show that age is not the most influential factor when measuring income. The level of education the individual has achieved will have a positive impact on their impact. It is reasonable to assume that education is more important than age, and younger individuals can achieve high levels of income if they have more education than older respondents. The relationship can be considered as a causal chain, as well as somewhat of a spurious relationship. A causal chain because age has some effect on degree and degree, in turn, has a strong effect on income. It is also spurious because education effects income much more than age. Control Variable: Education Education is conceptualized as the highest degree that the respondent has achieved. The original GSS question is as follows: Do you (Does [he/she]) have any college degrees? (IF YES: What degree or degrees?) CODE HIGHEST DEGREE EARNED. The question allows the respondent several different responses but I recoded the variable to simply the responses. Individuals fit into one of three degree categories: None includes all individuals that never graduated high school, or achieved any degree for that matter. High School individuals that have graduated high school but have not received any further degrees. College individuals have received at least some kind of degree from a secondary school. That includes associates degrees (junior college), bachelors degrees, or any graduate school degree. Measurement Validity The variable achieves face validity because the degree earned directly relates to the level of education of a specific individual. Content validity is established because all respondents will fit into one of the categories, even if they are currently in college they still have not earned a degree and will fit into the high school group. Three Variable Analysis The three variable table shows an incredibly strong relationship between education in income. Of those that had a college degree, 91.3% earned over $25,000 while only 45.7% without any type of degree fit into the same income group. Also, for those with college degrees, the association seen between age and income has shrunk to under 15%, showing the reader that education is a much more important factor of income than age. Within each table, age does not appear to have a strong association at all. The largest association of age and income is seen in those who have college degrees, 17% between those under 25 and those 36-49. This association contributes to the assumption that the relationship is a causal chain. Overall it seems that the tables support the hypothesis, education has a large effect on income while age only correlates with income to an extent.
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Path: Cal Poly >> EE >> 100 Spring, 2008
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Path: Cal Poly >> EE >> 100 Spring, 2008
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Path: Cal Poly >> EE >> 100 Spring, 2008
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Path: Cal Poly >> EE >> 100 Spring, 2008
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Path: Cal Poly >> EE >> 100 Spring, 2008
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Path: Cal Poly >> EE >> 100 Spring, 2008
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Path: Cal Poly >> EE >> 100 Spring, 2008
Description: How to be a Successful Student* Introspection and self-discipline Time management Taking a course Homework Exams Further suggestions * based on: `How to Be a Student,\' by Jerome Breitenbach: http:/www.ee.calpoly.edu/~jbreiten/htbas.html The En...
