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Job Satisfaction Versus Family Income (GSS)

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Coursera project using the General Social Survey (GSS). For the Coursera course Data Analysis and Statistical Inference (DASI) offered by Duke U. It uses basic statistics to evaluate the GSS data about inflation-adjusted familly income and levels of job satisfaction.
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  25/10/14 01:11ob satisfaction versus family income (GSS)Page 1 of 10file:///Users/paulacookimac/Documents/MATHIEU/MOOC/Statistics/Dat…0and%20Stat%20Inference%20Duke/Project/dasi_project_template.html Job satisfaction versus familyJob satisfaction versus familyincome (GSS)income (GSS) October 18, 2014 October 18, 2014  Introduction:Introduction: Steve Jobs once said “The only way to be truly satisfied is to do what you believe is great work, and theonly way to do great work is to love what you do”  . This is a quote from Apple Inc.’s deceased legend butsuch a belief is widespread among the World’s elite, big corporations and beyond. It is believed that asatisfied sta !  will produce a great work, which in turn will make the company successful. But is there afinancial reward for employees that satisfies such expectations? In other words, does satisfiedemployees have on average higher family income than less satisfied ones? Or we could ask the questionthe other way round: Does a well paid job brings on average more satisfaction?To evaluate those questions with data, a R-ready data-set from the General Social Survey (Smith et al  ,2013) was used (see below for the full citation). In particular, the subsequent statistical analysis userespondents answers about job satisfaction and (inflation adjusted) family income. Data:Data: Data Collection / Cases The GSS data were collected between 1972 and 2012. The population (universe) from which the datawere collected (individuals) are all non-institutionalized, English and Spanish speaking persons 18 yearsof age or older, living in the United States. Until 2006, only the English speaking persons were surveyed.Starting in 2006, the GSS sampled Spanish speakers in addition to English speakers.These individuals were surveyed using standardized questionnaire through computer-assisted personalinterview (CAPI), face-to-face interview, and telephone interview. The individuals were selected usingmodified probability design (from 1972 through 1974) and full probability design (from 1973 onward).Each case (each respondent’s answers to the survey) is one row of the data-set (one filled questionnaire). Selected Variables The following GSS variables were used for the subsequent statistical analyses: First variable = “satjob”:  The cases were asked the following question: “On the whole, how satisfied are you with the work you do?”  . The answers were collected on an ordinal scale (from 1 to 4 - ordinalcategorical variable) labelled as follows: Very satisfied (1), Moderately satisfied (2), A little dissatisfied (3),Very dissatisfied (4). Second variable = “coninc”:  The family income in constant dollars (inflation-adjusted). The answersvaries between a minimum of 383 dollars and a maximum of 180386 dollars. It is thus quantitative / numerical variable Type of Study   25/10/14 01:11ob satisfaction versus family income (GSS)Page 2 of 10file:///Users/paulacookimac/Documents/MATHIEU/MOOC/Statistics/Dat…0and%20Stat%20Inference%20Duke/Project/dasi_project_template.html The General Social Survey (GSS) is an observational study. The data were collected using a surveyquestionnaire. As it is a survey, respondents were not randomly assigned to treatment or control groups.Therefore, and even if the authors did a great job at selecting a representative sample of the population,this study cannot be considered as an experiment. Indeed, it would be both unethical and unpractical toassign an individual to a group to that is “Very satisfied”, or “Moderately satisfied”, or “A littledissatisfied”, or “Very dissatisfied” of his/her job. Scope of Inference - generalizability  The findings of this analysis can be generalized to all all non-institutionalized, English and Spanishspeaking persons 18 years of age or older, living in the United States. This is because the cases wereselected using a modified or full probability design (the investigators took care to develop a samplingdesign that gives equal probability of selection to any individual belonging to the population). Since it is asurvey, there is potential for response and non-response bias. Since liking his/her job is a sociallydesirable trait, a social desirability bias (a type of response bias) cannot be excluded. Also, since onlyEnglish and Spanish speakers were sampled, the results of the inference cannot be generalized to otherlanguages speakers (less than 1% of the USA household population according to the authors of thestudy). Observations collected before 2006 did not include Spanish speakers (less than 2% of thepopulation). Scope of Inference - causality  The inference intends to explore relationship between job satisfaction and family income. The datacannot be used to establish a causal relationship between these two variables since it is an observationalstudy (and thus not an experiment). It would be di cult to blindly administer job satisfaction (ordissatisfaction) to someone and then see the impact on the family income ;-). In other words, correlationis not causation, there could exist a confounding variable not included in the analysis. Exploratory data analysis:Exploratory data analysis: Exploratory data analysis for the first variable: = “satjob” The GSS survey questionnaire asked respondents to rate their job satisfaction on an ordinal scale asfollows: “Very Satisfied”, “Moderately Satisfied”, “A Little Dissatisfied”, or “Very Dissatisfied”. If (for anyreason) the respondent did not rate his/her job satisfaction, an “NA” was recorded in the data-set. The R-ready data-set has 57061 rows (respondents / cases). Out of these, 19717 (35 %) declared they were“Very Satisfied” with their job, 15736 (28 %) “Moderately Satisfied”, 4109 (7 %) “A Little Dissatisfied”,1715 (3 %) “Very Dissatisfied”, and 15784 (28 %) did not rate their job satisfaction (“NA”). These datashow that the surveyed Americans are on average satisfied of their jobs, with approximately 63 % beingvery or moderately satisfied, and only 10 % confessing some form of dissatisfaction. Nevertheless, thesehigh rates of job satisfaction could suggest some social desirability bias since being satisfied with his/her job is a socially desirable trait. Also a striking 28 % did not rate their job satisfaction (“NAs”). Thisimportant non-response rate could impact the generalizability of the subsequent statistical analyzes(non-response bias). To get a better overview of the job satisfaction results and investigate this importantnon-response rate, the results were broken down per year and displayed using a mosaicplot (where theoutermost bar on the left summarizes the whole GSS data-set):  25/10/14 01:11ob satisfaction versus family income (GSS)Page 3 of 10file:///Users/paulacookimac/Documents/MATHIEU/MOOC/Statistics/Dat…0and%20Stat%20Inference%20Duke/Project/dasi_project_template.html The mosaicplot shows non-response rates for job satisfaction around 20% for most surveyed years withthe exception of 1972, 2002, 2004, and 2006 when high non-response rates were recorded (41%, 62%,50%, and 52% respectively).For the remaining of the project, non-responses (NAs) were omitted. The mosaic plot showing annual jobsatisfaction results without non-response can be found below along with a summary() of the 2-waycontingency table (i.e. years versus job satisfaction levels). Although job satisfaction rates seems toremain fairly constant throughout the surveyed years (e.g. high level of “Very Satisfied”), the summary()function applied to the contingency table displays a chi-squared statistics of 228 for 84 degrees offreedom(= (29 years - 1) x (4 levels - 1) = 84), which yield a very tiny p-value. Therefore, the data dosupport the rejection of the null hypothesis and, consequently we conclude that it exists small butstatistically significant di ! erences between annual job satisfaction results. Using a Pareto chart analysis(not show), the major contributors to the chi-squared statistic of 228 were identified as years 1984 (14%of the chi-squared statistic), 1987 (11%), 1975 (8.7%), 2000 (8.3%), and 1980 (5.7%). Nevertheless, forthe inference part of the project, annual results for job satisfaction were pooled (in order to keep only 2variables, as requested). ## Number of cases in table: 41277 ## Number of factors: 2 ## Test for independence of all factors:## Chisq = 228, df = 84, p-value = 3e-15  25/10/14 01:11ob satisfaction versus family income (GSS)Page 4 of 10file:///Users/paulacookimac/Documents/MATHIEU/MOOC/Statistics/Dat…0and%20Stat%20Inference%20Duke/Project/dasi_project_template.html Exploratory data analysis for the second variable: = “satjob” To start exploring respondents’ family income, basic summary statistics were produced using the wholeGSS data-set:  Min. 1st Qu. Median Mean 3rd Qu. Max. NA's 383 18400 35600 44500 59500 180000 5829 Excluding NA, the family income of the sample ranges from $383 to $180,400. The median family incomeis $35,600 and the mean family income is $44,500. This suggest a right skewed distribution of familyincome. The histogram for family income confirms that the distribution is right skewed (with a few largefamily incomes that pull the mean to the right of the median):To explore how distribution of inflation-adjusted family income has evolved from 1972 to 2012, a boxplot(using years as factors) was produced. The plot shows that while the mean (red dots) family income hasincreases from $38,418 in 1972 to $48,385 in 2012, the median family income (blue dots) has remain
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