General Slides for SOC120 Fall 2005 Week 9,10: C11, C12 (edited Study Guide edited 3/06/07)

1. General Slides for SOC120Fall 2005Week 9,10: C11, C12(edited Study Guide edited 3/06/07)

2. Diffirence:Informal and Formal Inductive Arguments • (1) “first level” Everyday life (340b) • (2) “second level” Scientific (340b) • Obvious diff in 1 and 2 (340b) • EXAMPLE OF FORMAL: Political Poll (341) • RANDOM VARIATION: PROBLEMS AND SOLUTIONS • (1) Error margin (p343t)(population variability) • (2) Confidence level/SIGNIFICANCE (p343m) • Rule IV (p343m) • G. SAMPLE SIZE (p343) • (1) Short Answer (p343b) • (2) Approximate error Margins (BOX 345) • (3) Typical confidencelevel Significance(p345b) • H. INFORMAL INDUCTIVE ARGUMENTS • (1) Cautious conclusions EX (p346) • (2) Summing Up Steps 1, 2, 3 (p347) • I. FALLACIES (p349t) • (1) hasty generalization/overgeneralization • (a) Anecdotal evidence • (2) Biased generalizing • (b) weak or poor analogy • (3) Also “Apples and Oranges” BOXp355) • POLLS: Problems and Pitfalls (p351m) • (1) Self selected samples (p351b) • “Dear Abby”, “Mike Royko” (p352bm) • (2) Person on the streets • (3) Slanted questions (& question order) • Playing by the numbers(p353b) • (1) Law of large numbers • RULE V (p352b) • (2) Random sampling errors (p355b) • (3) Gamblers Fallacy (p356t) Study Guide C10 8th Ed • Inductive Argument Def/Ex (p334t) Probability (p334t) Strong ---Weak Ex need for prediction (p334m) 3 examples (p335t) Motorcycles Barking dog Peaches • Analogical Arguments (p335b) Def/Ex terms target target/feature/propety in question Rule 1 (p336t) Peculiarity of arguments (p336b) • Inductive Generalizations 1. Def/Ex (p337b) 2. Sample (p338t) 4. Target Class or Population (p338t) 5. Representative (p338t)[EPSM] 6. Rule II (p338 m) 7. Biased sample 8. Is larger sample always better? 9p338b) 9. Rule III (p339t) 10. single item as predictor (p339 box p340) 11. Homogenous vs heterogeneous and sample

3. Guess your weight within 3 lbs Confidence/Significance and Error margin Hypothesis: People’s Attitude about abortion is related to their religious identity. The guess for my weight • His/her guess will be X (151 lbs) • The guess will have to be ±3 lbs of X, 148-154 lbs [Error margin] • He/she can do this 95 out of 100 guesses [Confidence/significance] Chisq(P) =61.63(p= 0.00 The researcher knows that: • His/her guess is the hypothesis • The significance was decided before the study at 95 out of 100 guesses, .05. [Confidence/significance] • RELIG and ABANY are related since the Chisq was .00 and the significance at the .05 level is accepted

4. BOX: Couch Potato… p382 • (d) Size of group and statistical significance chart [p383t] • (e) Do not assume always check 1,2 [p382b] • (f) Even if significant first, second, … 3 [p383b] • BOX: Cigarettes, Cancer…[p384t] • (2) Nonexperimental cause to effect def/ex • [p384] [slide] • (a) Do not assume…1-4 [p385t] • BOX: HPV vaccine found [p386t] • Self Select vs Match [p385b] • (3) Non Experimental Effect to Cause[slide][p386b] • [D] Appeal to Anecdotal Evidence Def/Ex [p387b] • (1) To establish X as causal…[p388m] • BOX: The Farside [p389t] • BOX: The Wrong Initials…[p388t] • [E] Doubtful Causal…1-5 p388-390 • [F] Causal explanation& Arguments p390-391 • (1) The moral is obvious..[p391t] • The lesson is simple..[p391b] • (2) Explanations and excuses [p391-393] • BOX: Study Backs Old Idea [p392] • FALLACY: confusing explanations and excuses [p393] C11 Terms: Study Outline 8th Ed [A] (1) Intro Causal Arguments Common example Def/Ex Post hoc fallacy Def/Ex [Correlation ~=Cause p371] (2) Informal causal reasoning Causal claim/cause-and effect-claim/Hypothesis def/ex [p372m] (3) Two Basic patterns of causal reasoning (a) Relevant –difference[p373m]Example [BOX p374t], Sum [p376m] (b) Common-thread reasoning[p374] Sum [p397b] (c) CT best for?, RD best for? [376b] [B] Common Mistakes In Informal Causal Reasoning 1-5 [p377] BOX: Converting [BOX p374t] BOX: The Great 9/11…[p375t BOX: Televisions effect…[p377] [C] General Causal Claims Def/Ex [p379b] • (a) Controlled cause to effect Def/Ex p380 [slide] (b) Concepts [p 381m] (c)Statistical significance p381b-Me

5. Table Reading for A05 Part A • Reading a table involves the following: • (1) Big Picture: General characteristicsof the table. What is the: • (a) Population and sample (size for sample, acceptable?) • (b) Independent variable categories and distribution (acceptable?) • (b) Dependent variable categories and distribution (acceptable?) • (2) Specifics: distributions of interest within the table • (3) Conclusion: hypothesis—support, reject, suspend judgment (see Part B) • (4) Problems: needs for further study, variable categories, size and distributions of categories… Table conventions: Creating and Reading a Table

6. Table Reading for A05 Part B Table conventions: Creating and Reading a Table (interpreting the distributions in a table for your hypothesis) If your independent variable has two many categories to run across a page, simply make it the row variable and calculate(row%) and compare percents across column --> next page

7. Simple Table Hypothesis: males in the US have a higher average income then females If the hypothesis is supported which cells A, B, C, D, should have the highest percents and which the lowest? (A and D)

8. V3 AGE by V46 CONCERNED ABOUT CONTRACTING AIDS V46 Page 1 of 1 Count | Row Pct |very somewhat not very not conc | erned Row | 1 | 2 | 3 | 4 | Total V3 --------+--------+--------+--------+--------+ 1 | 283 | 207 | 115 | 62 | 667 19 or younger | 42.4 | 31.0 | 17.2 | 9.3 | 33.2 +--------+--------+--------+--------+ 2 | 216 | 119 | 83 | 34 | 452 20- 21 | 47.8 | 26.3 | 18.4 | 7.5 | 22.5 +--------+--------+--------+--------+ 3 | 209 | 163 | 91 | 41 | 504 22- 24 | 41.5 | 32.3 | 18.1 | 8.1 | 25.1 +--------+--------+--------+--------+ 4 | 89 | 63 | 28 | 26 | 206 25- 29 | 43.2 | 30.6 | 13.6 | 12.6 | 10.3 +--------+--------+--------+--------+ 5 | 39 | 30 | 32 | 13 | 114 30- 39 | 34.2 | 26.3 | 28.1 | 11.4 | 5.7 +--------+--------+--------+--------+ 6 | 14 | 14 | 13 | 10 | 51 40- 49 | 27.5 | 27.5 | 25.5 | 19.6 | 2.5 +--------+--------+--------+--------+ 7 | 4 | 3 | 4 | 2 | 13 50 or over | 30.8 | 23.1 | 30.8 | 15.4 | .6 +--------+--------+--------+--------+ Column 854 599 366 188 2007 Total 42.6 29.8 18.2 9.4 100.0 % Across Read down if reverse Indep and Dep Can have Row as Independent Col.. as Dependant % by row Read by comparing across dependant (col.. this ex) Tables Chi-Square = 35.10558 DF = 18 significance= .00917

9. Ideal Experimental Model“controlled cause to effect” How much difference? See “significance” Experimental group: factor X is introduced all else stays same Control group: all stays the same C--- the suspected causal agent (c in the graphic above) E ---the effect being investigated C11 Concepts

10. Difference from experimental cause to effect is the experimental agent is not induced by researcher due to ethics, laws, money, etc. Non experimental cause to effect: two ex A. Find a group “I” who have been exposed to causal agent rather then investigator exposing to causal agent and create a matching group "II". Examine change over time for both groups. (e.g. Study of nurses and HMO patients life, diet, etc. patterns B. Find a group “I”who have been exposed to causal agent rather then investigator exposing to causal agent and examine change after a period of time I to II. (e.g. Study of Beagles that over time naturally develop skin cancer. It would seem as if the "causal agent" caused the cancer shows importance of group III and IV.) C11 Concepts

11. Non experimental effect to cause Find group “II” that has condition and compare with a group, "IV" that doesn’t have the condition find difference (e.g. Mouth cancer determine difference is smoking cigars Many possible differences in II and IV would limit strength of conclusions C11 Concepts

12. C10, C11 Chapter 10 • Representativeness and Bias, • Sample size, • Error Margin • Confidence Level • Criteria and Fallacies of Inductive Generalization Chapter 12 • Classifies research in terms of pattern of causal reasoning, models of research and fallacies.

13. Significance/Confidence • Probability/possibility that an event could have occurred by chance • For tables use the ChiSq value • .05means this distribution could have occurred by chance in only 5 of 100 times if the study were repeated 100 times. This means you are confident that you are confident of the accuracy at the .05 level. • Typically social science operates at the .05 level. If the number were larger .06, .08, .1… it is not considered significant, we do not have confidence that a relationship found in the table distribution is real Polls and surveys S6

14. Ambiguous Student Research Statements from A05 • A hypothesis concerning this theory is whether gender is affected by the opinion of stronger gun laws • I chose to research the topic of marital status in regards to the abortion decision because I am interested in how a women’s companionship with others may affect her belief that abortion is an individual matter. • I believe that illegal drug use is spread among all ages but, I am concerned in the amounts among teenagers and adults using them. • This paper focuses on the effects of education, as they pertain to the satisfaction level of an individual with respect to state government performance. • Based on a mother’s ethnicity, we can accumulate an idea on what level of education she possesses or will possess. • For this research project I would like to determine the relationship between Capital Punishment and the right to choice about abortion • The topic I have formulated is Gender Roles and their views regarding Capital Punishment.