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Sociology 4880 Quantitative Methods of Social Research

Sociology 4880 Quantitative Methods of Social Research. University of North Texas Spring 2009 Kevin Yoder. Statistics. Statistics : Techniques for collecting, analyzing, and drawing conclusions from data Data : Information represented by numbers Why Study Statistics?

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Sociology 4880 Quantitative Methods of Social Research

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  1. Sociology 4880Quantitative Methods of Social Research University of North Texas Spring 2009 Kevin Yoder

  2. Statistics • Statistics: Techniques for collecting, analyzing, and drawing conclusions from data • Data: Information represented by numbers • Why Study Statistics? • Make sense of data collected from many people • Understand quantitative social science research • Evaluate statistical information that you see every day

  3. Signing Away Your (Sex) Life:Virginity Pledgers and Non-Pledgers • Study of 289 virginity pledgers and 645 non-pledgers* • In 1996, asked a random sample of U.S. youths ages 15-18 whether they had “ever signed a pledge to abstain from sex until they were married” • In 2001, asked the same youths (now ages 20-23) about various sexual-related outcomes *Rosenbaum, J.E. (2009). Patient teenagers? A comparison of the sexual behavior of virginity pledgers and matched nonpledgers. Pediatrics, 123, 110-120.

  4. Virginity Pledgers and Non-Pledgers: A Match Made in Heaven? • Non-pledgers and pledgers were matched (in a 3:1 ratio) on 128 characteristics • Based on information provided in 1995 • Characteristics included • Sociodemographics (e.g., gender, age, race/ethnicity) • Parental (e.g., education, income) • Family (e.g., family structure, relationship with youths) • School (e.g., participation in sports) • Sexuality (e.g., attitudes toward sex and birth control) • Religiosity (e.g., beliefs, church attendanace • Delinquency (self and friend)

  5. Study Results *Yes means that there is evidence for a difference between pledgers and non-pledgers among all youths in the population; no means that there is not enough evidence.

  6. Study Conclusions • “Pledgers were not less sexually active than matched nonpledgers despite prepledge similarities on 128 factors.” (p. 114) • “Despite having similar birth control attitudes 1 year before pledging, virginity pledgers were substantially less likely than match nonpledgers to protect themselves against STDs and pregnancy….” (p. 114) • “Clinicians should provide birth control information to all adolescents, especially AOSE [abstinence-only sex education] participants.” (p. 115)

  7. Is Statistics “Objective”? • Statistical Techniques: Objective in that they have fixed definitions • Example: The mean is always computed in the same way • Application of Statistics: Introduces subjectivity • Researchers choose which variables to use • A statistical technique might be used incorrectly • Sample might be biased • Survey questions might be poorly worded

  8. Population and Sample • Population: The entire collection of individuals, groups, or social artifacts in which a researcher is interested in studying • Sample: A portion of a population • Relevance to Statistics: We usually study a sample from a population

  9. Population and Sample: Examples

  10. Descriptive Statistics • Descriptive Statistics: Statistical methods used to describe data collected from a sample or population • Example 1: Average GPA of all sociology majors at UNT • Example 2: Average weekly offerings in 50 churches in the Metroplex • Example 3: Percent of all African cities with populations above 1 million • Example 4: Average length of 20 songs from an iPod

  11. Inferential Statistics • Inferential Statistics: Statistical methods used on data from a sample to make estimates or generalizations about a population • Example 1: Estimate the average happiness level of all married people in the U.S. • Example 2: Percentage of all voters in Iowa who support Rudy Giuliani, estimated from a sample of 1,500 voters • Example 3: Estimated number of people in the U.S. who have the flu

  12. Variable • Variable: A characteristic that takes on two or more values among different people or things • Examples of Variables • Age (many values: 0 to 100+) • Sex (two values: male and female) • Number of songs on iPods (many values: 0 to 20,000+) • Example of a Constant (not a Variable) • Sex of students in an all-girls school (one value: female)

  13. Level of Measurement • Definition: Numeric specification of, and relationships among, values of a variable • Three Levels of Measurement • Interval-Ratio • Ordinal • Nominal (categorical, qualitative)

  14. Interval-Ratio: Examples • Examples • Temperature • Age • Income • Extended Example: Number of alien abduction experiences (AAEs) • Values: 0, 1, 2, 3, 4, … • Numeric? Naturally numeric

  15. Interval-Ratio: Examples • Extended Example (Number of AAEs): Relationships among values of the variable • Different: A person who experienced 2 AAEs is different from a person who experienced 0 AAEs • Higher/Lower: A person who experienced 5 AAEs has more AAEs than does a person who experienced 4 AAEs • How Much More/Less: A person who experienced 6 AAEs has four more AAEs than does a person who experienced 2 AAEs

  16. Interval-Ratio: Summary of Properties • Numeric? Values of the variable are naturally numeric • Values of the variable do not exist if the numbers are removed • 3 Types of Relationships • Different: One value of a variable is different from another value • Higher/Lower: One value of a variable is higher or lower than another value • How Much More/Less: It can be said how much more or less one value is of another value

  17. Ordinal: Examples • Social class • 1 = working class • 2 = middle class • 3 = upper class • Ranking of your favorite classes: 1st, 2nd, 3rd, … • Likert Scales • 1 = strongly disagree • 2 = disagree • 3 = neither agree nor disagree • 4 = agree • 5 = strongly agree

  18. Ordinal: Examples • Extended Example: Level of Education • Values • 1 = less than high school • 2 = high school • 3 = some college • 4 = college graduate • Numeric? Not naturally numeric

  19. Ordinal: Examples • Extended Example (Level of Education): Relationships among values of the variable • Different: Someone who is a college graduate (4) is different from someone who has a high school education (2) • Higher/Lower: Someone with less than high school (1) has a lower level of education than does someone with some college (3)

  20. Ordinal: Examples • Extended Example (Level of Education): Choice of numbers • The choice is arbitrary as long as the numbers indicate difference and higher/lower • As an example, we could assign: • -99 = less than high school • 3,555 = high school • 5,890 = some college • 1,000,000 = college graduate

  21. Ordinal: Examples • Extended Example (Level of Education): It cannot be said how much more or less one value is of another value • Why? The choice of numbers is arbitrary • Also: We don’t know how many years of education someone in the “less than high school” category has versus someone in the “college graduate” category

  22. Ordinal: Summary of Properties • Numeric? Generally not naturally numeric • Exception 1: Rank ordering (1st, 2nd, 3rd, …) • Exception 2: Numbers as part of the values • Fujita Scale for tornado intensity (F1, F2, F3, F4, F5) • Saffir-Simpson hurricane intensity (Category 1, Category 2, Category 3, Category 4, Category 5)

  23. Ordinal: Summary of Properties • 2 Types of Relationships • Different: One value of a variable is different from another value • Higher/Lower: One value of a variable is higher or lower than another value

  24. Nominal: Examples • Examples • Race/ethnicity • Religion • Extended Example: Political party affiliation (PPA) • Values • 1 = Democrat • 2 = Republican • 3 = Independent • Numeric? Not Naturally numeric

  25. Nominal: Examples • Extended Example (PPA): Relationships among values of the variable • Different: Someone who is a Republican is different from someone who is an Independent

  26. Nominal: Examples • Extended Example (PPA): Choice of numbers • The choice of numbers is arbitrary as long as they indicate that one value is different from another • For example, we could assign • 5 = Democrat • 86 = Republican • 69 = Independent • Extended Example (PPA): It cannot be said how much more or less one value is of another value • Why? The choice of numbers is arbitrary

  27. Nominal: Summary of Properties • Numeric? Generally not naturally numeric • Some Exceptions • Telephone numbers • Zip codes • 1 Type of Relationship • Different: One value of a variable is different from another value

  28. Determining the Level of Measurement

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