Foundations of Sociological Inquiry Statistical Analysis

Today’s Objectives  Why use Statistics?  Descriptive Statistics  Inferential Statistics  Multivariate Techniques  Questions?

The formula Y = f(X) tells us that 1. X is the dependent variable. 2. Y is the dependent variable. 3. f is the dependent variable. 4. need to know what Y, f, and X represent to determine the dependent variable. 5. None of these choices is correct.

Why Use Statistics? Statistics enable us to construct simplified representations of a complex social world.

Why Use Statistics? Statistics enable us to construct simplified representations of a complex social world.  Begin with a sociological question  Identify data to answer the question (collect, observe, record)  Analyze data (statistics help)  Present your findings (statistics help)  Situate your findings in relation to what we think we already know (statistics help)

Recommended Salary for Job Candidates: $4000 $70,000 $40,000 $80,000 $120,000 $135,000 $70,000 $50,000 $67, $500,000 $50,000 $75, $60,000 $150,000 $20,000 $ $70,000 $80,000 $62,000 $200,000 95,000 $75000 $70,000 $80000 $75,000 $45,000 a year $100,000 $250,000 $65, $ $75,000 $88,000 $80, $150,000 $55,000 $130,000 $60,000 $78,000 $150,000 $50, $45,000-60,000 $80,000 $75,000 $55000 $40,000 95,000 $80,000 $30, $80000 $30000 $70,000 $50,000 $50,000 $65000 $80,000 $? $80,000 $50000 $50000 (I have no idea how much Marketing Executive gets paid usually) 150,000 $74,000 $60,000 $60,000 $65,00 $80,000 $65,000 $90,000 $70,000 $90,000 $80,000 $45000 $45000 $35000 $100,000 $85,000 $50,000 $ $85,000 $58000 $60000 $70,000 $80,000 $70,000 $40000 $70,000 $80,000 $60,000 $200,000 $80,000 $50000 $60,000 - $75,000 $80,000 $60,000 $45,000 $50,000 $90,000 $30,000 $60, $200, $ $60000 $50,000 $75,000 $60000 $ $120,000 $80000 $55,000 $50, $145,000 $ $85,000 $55,000 $70000 $75, 000 $60, $ 10,000 $ $65000 $85,000 $80,000 $60,000 $ 70,000 $80,000 $75, $100,000 $ $70,000 $95,000 $92,000 $70,000 $50,000 $68,000 $80,000 $40,000 $30,000 $50,000 $60,000 $40,000 $80,000 $65,000 $i dont know $90,000 $60,000 $70,000 $80,000 $65000 $70,000 $ $100,000 $72000 $70,000 $50,000 $110, $80000 $18,000 $110,000 $200,000 $100,000 $80000

Descriptive Statistics (summary)  Statistical computations describing either the characteristics of a sample or the relationship among variables in a sample  Data reduction  Measures of association  Regression analysis  Other forms of multivariate analysis

Recommended Salary for Job Candidates Male Respondents Female Respondents Mean$77,277$78,837 Std. Dev$37,904$56,897 N9665 Source: Data were collected from students enrolled in Sociology 300.

Recommended Salary for Job Candidates

Difference in Means  Is the difference in mean salary recommended by men and women statistically significant?

Difference in Means  Is the difference in mean salary recommended by men and women statistically significant?  Conduct a t-test t = 0.20, df = 154, p-value = percent confidence interval (-13392, 16512)

Difference in Means  Is the difference in mean salary recommended by men and women statistically significant?  Conduct a t-test t = 0.20, df = 154, p-value = percent confidence interval (-13392, 16512)  We should not reject the null hypothesis that the true difference in means is equal to zero

Recommended Salary for Job Candidates

Multivariate Analysis  Is the difference in mean salary recommended by men and women statistically significant, controlling for parental status of applicant?

Multivariate Analysis  Is the difference in mean salary recommended by men and women statistically significant, controlling for parental status of applicant?  Conduct a regression analysis of recommended salary Variable Estimate t-valueP-value Male Respondent Parent Applicant Intercept <.001***

Multivariate Analysis  Is the difference in mean salary recommended by men and women statistically significant, controlling for parental status of applicant?  Conduct a regression analysis of recommended salary Variable Estimate t-valueP-value Male Respondent Parent Applicant Intercept <.001***  We should not reject the null hypothesis that the true difference in recommended salaries, controlling for parental status of applicant, is equal to zero

Inferential Statistics  The body of statistical computations relevant to making inferences from findings based on sample observations to some larger population.  Sampling error  Non-sampling error

_____ indicate the likelihood that the relationship observed between variables in a sample can be attributed to sampling error only. 1. Ex-post facto hypothesizing 2. Tests of statistical significance 3. Disconfirmation 4. Disambiguation

Statistical Significance  Statistical Significance is a general term referring to the likelihood that the relationship observed in a sample could be attributed to sampling error alone.

Statistical Significance  Statistical Significance is a general term referring to the likelihood that the relationship observed in a sample could be attributed to sampling error alone.  Tests of Statistical Significance are a class of statistical computations that indicate the likelihood that the relationship observed between variables in a sample can be attributed to sampling error alone.

Statistical Significance  Statistical Significance is a general term referring to the likelihood that the relationship observed in a sample could be attributed to sampling error alone.  Tests of Statistical Significance are a class of statistical computations that indicate the likelihood that the relationship observed between variables in a sample can be attributed to sampling error alone.  Level of Significance, in the context of tests of statistical significance, the degree of likelihood that an observed, empirical relationship could be attributed to sampling error.

_____ are statistical measures used for making inferences from findings based on sample observations to a larger population. 1. Descriptive statistics 2. Inferential statistics 3. Both of the above 4. Neither of the above

A statistical significance level of.05 means that 1. the probability that a relationship as strong as the observed one can be attributed to sampling error alone is 5 percent. 2. we can be 5 percent sure that the relationship is real and not due to sampling error. 3. there is an.05 percent chance that a relationship as strong as the observed one can be attributed to sampling error. 4. the difference we observed in the table is 5 percent different. 5. there is a 5 percent standard error in the observations.

Questions?