EXAMPLE: TECHNOLOGICAL DEVELOPMENTS HAVE CAUSED THE GENDER GAP TO SHRINK IN THE PREVIOUS YEARS.

2. GIVEN WHAT YOU KNOW ABOUT THE GENDER PAY GAP, WHAT ARE SOME TESTABLE HYPOTHESES THAT YOU WOULD FORM IN FUTURE RESEARCH OF THE GENDER PAY GAP? EXAMPLE: TECHNOLOGICAL DEVELOPMENTS HAVE CAUSED THE GENDER GAP TO SHRINK IN THE PREVIOUS YEARS. (Cause and Effect Statement) CONCEPTS: Technological Development (independent) Gender Gap (dependent) POTENTIAL VARIABLES: # of cell phone users (independent) Income differences between men and women (dependent)

3. WHAT IS THE FORMULA FOR Q? Q = (B x C) – (A x D) (B x C) – (A x D)

4. HOW DO YOU SIMPLIFY A VARIABLE? IF YOU WANTED TO RECODE TO A SIMPLE LEVEL OF MEASUREMENT, WHAT PROCESS WOULD YOU UNDERTAKE? HOW DOES THIS ALTER YOUR HYPOTHESIS? WHENEVER YOU HAVE A VARIABLE THAT YOU WANT TO RECODE INTO A LOWER LEVEL OF MEASUREMENT (INTERVAL TO ORDINAL), YOU SIMPLIFY THE VARIABLE INTO SIMPLER CATEGORIES. (AGE IS CONVERTED TO AGE GROUPS.) THIS ACTUALLY ALTERS YOUR HYPOTHESIS IN THE SENSE THAT YOU’RE LOSING INFORMATION, SO THE TESTS FOR EXISTENCE/STRENGTH/DIRECTION AREN’T AS ACCURATE.

5. NAME A TESTABLE HYPOTHESES THAT YOU CAN DEVELOP EITHER FROM THE CHOLERA EXPERIMENT WITH JOHN SNOWE OR THE NASA O-RING MALFUNCTION. CHOLERA: A CONTAMINATED WATER SUPPLY CAUSES CHOLERA TO SPREAD IN THE POPULATION. (WATER PUMP LOCATION – independent – WAS PLOTTED IN A MAP ALONGSIDE INCIDENCE OF CHOLERA – dependent) O-RING: COLD TEMPERATURES CAUSE THE O-RING TO CONTRACT, WHICH CAUSES A SHUTTLE EXPLOSION. (TEMPERATURE OF LAUNCHES – independent – WAS PLOTTED AGAINST O-RING DAMAGE – dependent)

6. IF Q IS .24, HOW MUCH DOES THIS REDUCE OUR ERROR? WE KNOW 50% OF THE ERROR (IT OCCURS BY CHANCE). WE ARE REDUCING THE REMAINING 50% OF ERROR BY 24%. (50*.24=12%) REMAINING ERROR = 50% 12% WE CAN REDUCE THE REMAINING ERROR BY 24% WE CAN PREDICT 50% OF THE ERROR BY CHANCE

7. WHAT ARE THE TWO TYPES OF COEFFICIENTS? CORRELATION: HOW MUCH OF THE VARIATION OF YOUR DEPENDENT VARIABLE IS BEING EXPAINED BY YOUR INDEPENDENT VARIABLE. HOW WELL IS YOUR INDEPENDENT VARIABLE PREDICTING YOUR DEPENDENT VARIABLE? “GOODNESS OF FIT” EFFECT-DESCRIPTIVE: HOW MUCH DOES YOUR DEPENDENT VARIABLE CHANGE IN RESPONSE TO YOUR INDEPENDENT VARIABLE?

8. WHAT DOES A NEGATIVE RELATIONSHIP LOOK LIKE ON A SCATTERPLOT? WHAT DOES A POSITIVE RELATIONSHIP LOOK LIKE ON A SCATTERPLOT? NEGATIVE POSITIVE

9. WHAT INFORMATION DOES A LINE ON THE SCATTERPLOT TELL YOU? . . THE LINE ON THE SCATTERPLOT GIVES YOU THE DIRECTION OF THE DATA. (WHETHER OR NOT THE RELATIONSHIP IS POSITIVE OR NEGATIVE.) YOU CAN ALSO GENERALLY ESTIMATE THE SLOPE, BUT THE ACTUAL SLOPE IS GIVEN BY A REGRESSION.

10. WHAT DOES A NORMAL CURVE LOOK LIKE AND WHAT DOES IT TELL US ABOUT OUR DATA IF WE HAVE IT? A NORMAL CURVE TELLS US THAT WE HAVE A LARGE NUMBER OF OUR CASES SURROUNDED AROUND THE MEAN AND AN EQUAL AMOUNT OF CASES DISTRIBUTED AROUND THE MEAN. MEAN -2 SD -1 SD 1 SD 2 SD

11. WHAT DOES DEGREES OF FREEDOM TELL YOU? . . DEGREES OF FREEDOM GIVES YOU INFORMATION ABOUT THE SAMPLE SIZE (THE NUMBER OF OBSERVATIONS OR CASES IN AN ACTUAL DATASET.)

12. WHY IS Q SUCH AN IMPROVEMENT OVER DELTA? . . DELTA IS BOUNDED. THERE IS NO UPPER LIMIT. DELTA IS ALSO SENSITIVE TO SAMPLE SIZE. Q IS MEASURED FROM -1 TO 1. Q ISN’T SENSITIVE TO SAMPLE SIZE SINCE WE ACCOUNT FOR THE TOTAL NUMBER OF PAIRS.

13. WHY IS THE PERCENTAGE-DIFFERENCE COEFFICENT SUCH AN IMPROVEMENT OVER Q? . . THE PERCENTAGE-DIFFERENCE COEFFICIENT TELLS YOU HOW MUCH OF A CHANGE OCCURS IN BETWEEN THE VARIABLES THAT YOU’RE LOOKING AT IN YOUR RELATIONSHIP.

14. WHAT DID SARAH STUDY IN HER LECTURE? WHAT DID SHE END UP FINDING? . . SARAH WAS LOOKING AT WHETHER OR NOT WOMEN’S SENSE OF SELF-OBJECTIFICATION AFFECTED THEIR POLITICAL EFFICACY. THE RESULTS, WHICH SHE RAN IN A CROSSTAB AND A GAMMA, TURNED OUT NOT TO BE SIGNIFICANT.

15. I AM MEASURING CAMPAIGN INTEREST AS IT EFFECTS VOTER TURNOUT. I RUN A REGRESSION AND GET A BETA COEFFICIENT OF 34 WITH A SIGNIFICANCE VALUE OF .03. VOTER TURNOUT HAS A RANGE OF 74. HOW DO YOU INTERPRET EXISTENCE/STRENGTH/DIRECTION OF THIS RELATIONSHIP? . . EXISTENCE: THE SIGNIFICANCE VALUE IS .03. THIS IS SIGNIFICANT AT THE P<.05 LEVEL. THIS MEANS THAT THE PROBABILITY THAT THIS RELATIONSHIP IS DUE TO CHANCE IS 3%. STRENGTH: FOR EVERY UNIT CHANGE IN CAMPAIGN INTEREST, I GET A 74 UNIT INCREASE IN VOTER TURNOUT. CONSIDERING VOTER TURNOUT IS MEASURED FROM O TO 100%, THIS IS A VERY STRONG EFFECT. DIRECTION: MY COEFFICIENT IS 74 AND THIS IS POSITIVE. THIS MEANS AS I GET POSITIVE VALUES ON CAMPAIGN INTEREST, I GET POSITIVE VALUES ON VOTER TURNOUT.

16. I RAN A REGRESSION FOR ELECTIONS OVER THE PAST 30 YEARS AND GOT AN EQUATION THAT LOOKS LIKE THIS: y=3x+2 WHERE Y IS VOTE SHARE AND X IS APPROVAL RATING. IF BARACK OBAMA HAS AN APPROVAL RATING IN JUNE 2012 OF 32%, WHAT IS THE PREDICTED VOTE SHARE? . . JUST PLUG IN THE OBSERVED VALUE OF APPROVAL RATING (X) BACK INTO THE EQUATION TO GET THE PREDICTED VALUE OF VOTE SHARE (Y). Y=3(32)+2 Y=96+2 Y=98 OBAMA’S PREDICTED VOTE SHARE IS 98%.

17. WHAT IS CONTENT ANALYSIS? . . CONTENT ANALYSIS IS SYSTEMATICALLY LOOKING AT TEXTS TO COME TO A CONCLUSION ABOUT THE VARIOUS DIFFERENT COUNTS. (ie. COUNTING THE NUMBER OF USES OF THE WORD “TERRORISM” IN BUSH’S STATE OF THE UNION ADDRESS. MORE COUNTS MEANS THAT THE BUSH ADMINISTRATION IS RESPONSIBLE FOR FRAMING THE DEBATE ON TERRORISM.)

18. WHAT IS THE FORMULA FOR THE PERCENTAGE-DIFFERENCE COEFFICIENT? . . DYX = (B x C) – (A x D)_ _ (B x C) + (A x D) + (A x C) + (B + D)

19. WHAT IS THE DIFFERENCE BETWEEN DIRECT AND INDIRECT OBSERVATION? . . DIRECT OBSERVATION IS WHEN THE RESEARCHER OBSERVES THE ACTUAL BEHAVIOR. THIS IS A PROBLEM FOR VALIDITY IN REACTIVITY. INDIRECT OBSERVATION IS WHEN RESEARCHERS ARE MAKING INFERENCES BASED ON TRACES LEFT BEHIND. REACTIVITY ISN’T A PROBLEM.

20. WHAT IS THE DIFFERENCE BETWEEN WHAT WE HAVE BEEN DOING IN OUR PAPERS AND OBSERVATION/CONTENT ANALYSIS? . . THE MAIN DIFFERENCE IS THAT WE’RE NOT COLLECTING DATA IN OBSERVATION OR WE’RE NOT OBSERVING ANY DATA. WHAT WE’RE DOING IS WORKING WITH AGGREGATE DATA WITH SURVEYS. THEY ACT AS A SECONDARY SOURCE AND WE’RE INDIRECTLY OBSERVING DATA.

21. WHAT IS SYMMETRICAL/ASYMMETRICAL? (WITH COEFFICIENTS) . . SYMMETRICAL MEANS THAT ONE SIDE LOOKS LIKE THE OTHER SIDE. ASYMMETRICAL MEANS THAT ONE SIDE IS DIFFERENT THAN THE OTHER SIDE. FOR COEFFICIENTS, A COEFFICENT IS SYMMETRICAL WHEN THE VALUE IS THE SAME WHETHER IT PREDICTS Y FROM X OR X FROM Y. (Q IS SYMMETRICAL). A COEFFICENT IS ASYMMETRICAL BECAUSE IT’S DIFFERENT WHEN IT PREDICTS Y FROM X AND X FROM Y. (Dyx IS ASYMMETRICAL)

22. WHAT IS SELECTING ON THE DEPENDENT VARIABLE? . . SELECTING ON THE DEPENDENT VARIABLE HAS TO DO WITH SAMPLING. YOU’RE SELECTING CASES FOR YOUR SAMPLE BASED ON THE DEPENDENT VARIABLE. FOR EXAMPLE, IF YOU’RE INTERESTED IN ANSWERING THE QUESTION IF WHETHER OR NOT UCSB STUDENTS LIKE BURRITOS AND YOU DO YOUR SAMPLING AT FREEBIRDS, YOU’RE AUTOMATICALLY BIASING YOUR SURVEY.

23. I WANT TO INVESTIGATE WHY DIFFERENT COUNTRIES SPEND MORE OR LESS ON EDUCATION SPENDING. CANADA ANAD MEXICO DIFFER ON EXONOMIC DEVELOPMENT AND THEIR RESPECTIVE POLITICAL SYSTEMS. BUT THEY ARE THEY SPEND ROUGHLY THE SAME ON EDUCATION SPENDING. I LOOK TO SEE WHAT WAS COMMON AMONGST THESE DIFFERENT COUNTRIES TO SEE WHY THEY HAVE THE AME OUTCOME. WHAT TYPE OF DESIGN IS THIS? . . THIS IS MOST DISSIMILAR DESIGN. THE TWO COUNTRIES HAVE THE SAME AMOUNT ON THE DEPENDENT VARIABLE, BUT MAY DIFFER ON THEIR INDEPENDENT VARIABLES, SO I SELECT THEM BECAUSE I WANT TO SEE HOW THEY DIFFER.

24. WHAT IS CENSORED DATA? . . ANYTIME THAT YOU HAVE DATA THAT DOESN’T RANDOMLY SAMPLE AN ENTIRE POPULATION (USUALLY YOU’RE CHOOSING A SPECIFIC SEGMENT), YOU HAVE CENSORED DATA. YOU WILL BE MAKING ASSUMPTIONS ABOUT GENERALIZING YOUR DATA WHEN IT’S CENSORED.

25. WHAT IS RANDOM SAMPLING? WHAT IS QUASI-RANDOM SAMPLING? WHAT IS PURPOSIVE SAMPLING? . . RANDOM: A COMPLETELY RANDOM SAMPLE OF THE NATIONAL POPULATION. QUASI-RANDOM: YOU DON’T HAVE THE RESOURCES TO DO A COMPLETELY RANDOM SAMPLE, SO YOU’RE CHOOSING TO DO A SAMPLE THAT IS SOMEWHAT RANDOM, BUT YOU’RE CHOOSIN SPECIFIC LOCATIONS THAT YOU CAN GET TO. I’M RANDOMLY SURVEYING THE LOS ANGELES POPULATION INSTEAD OF NATIONAL POPULATION. PURPOSIVE: IF YOU’RE INTERESTED IN A SPECIFIC POPULATION, YOU WOULD JUST SURVEY THAT POPULATION. FOR EXAMPLE, IF I’M INTERESTED IN SEEING WHY UCSB STUDENTS LIKE BURRITOS, MY SAMPLE WILL PURPOSIVELY BE UCSB STUDENTS.

26. I'M TRYING TO FIND OUT IF PEOPLE'S VIEWS ON RACE AFFECTS THEIR PERSPECTIVE ON CRIMES. I SURVEY PEOPLE IN SEVERAL LOS ANGELES MALLS BY SHOWING THEM A NEWS STORY ON CRIME, ONLY ALTERING THE ALLEGED ASSAILANT'S RACE. I THEN CONDUCT THE SAME QUESTIONNAIRE, BUT COMPARE THE GROUPS BASED ON WHICH RACE THEY SAW IN THE NEWS STORY. WHAT IS SUPERIOR ABOUT THIS RESEARCH DESIGN? . . THIS IS A QUASI-RANDOM SAMPLE. IT’S ALSO AN EXPERIMENT. IT’S A GOOD DESIGN BECAUSE ALTHOUGH IT’S AN EXPERIMENT, IT AVOIDS THE ARTIFICIALITY OF A LABORATORY SETTING. WE’RE OBSERVING HOW THE RESPONDENTS DIFFER ON THEIR REACTIONS TO RACE (WHICH WE SKILLFULLY MANIPULATE IN THE NEWS STORY WHICH THEY DIDN’T KNOW ANYWAY.), SO THIS IS AS NATURAL OF A SETTING THAT WE CAN GET WHILE STILL HAVING SOME CONTROL OVER THE EXPERIMENT.