1. TYPES OF RESEARCH

2. Descriptive research Violent crime has been falling since the early 1990’s. Imprisonment is still increasing, but at a slower rate Use data to describe situations and trends Blacks and Hispanics are far more likely to be poor than Asians and Whites.

3. Explanatory research Collect data to explain why things change Ceasefire  less youth violence? Between 1996-1999 police, probation, Federal agents and social agencies in Boston applied a variety of strategies to reduce youth gun violence. These included meeting with at-risk youth, monitoring their behavior and invoking stiff Federal sanctions against armed criminals. Mean monthly gun deaths dropped 30 percent, from 3.5 pre- Ceasefire to 1.3 during Ceasefire. But gun deaths were also dropping elsewhere. Once that was taken into account the improvement was 14 percent. More imprisonment  less violence? Some researchers believe that falling crime rates were caused by harsher sentencing. A skeptical but well-known criminologist estimates that increased incarceration accounted for 20 percent of the “Great Crime Drop”. Between 1993-2005 the average time served in State prison (all offenses) rose 38 percent, from 21 months to 29 months. In 1991 the violent crime rate was 758.2 per 100,000. By 2000 it was 506.5, one-third lower.

4. Association and causation Association means that two or more variables change together – During the 70s and 80s, as the imprisonment rate (# incarcerated per 100,000 population) increased, violence decreased – After Ceasefire the mean number of youths slain by gunfire dropped Causation goes further – it means that changes in one variable caused changes in another variable. The causal variable is called the “independent” variable (in the Boston project it’s whether Ceasefire was in effect – Y or N) The effect variable is called the “dependent” variable (in Boston it’s the number of youth homicides each month) To test causation we begin with a hypothesis

5. Hypothesis A prediction of cause and effect – Must be based on literature and prior studies – If not, extreme risk of finding a non-existing relationship Hypothesis: Ceasefire reduced the number of youth gun deaths Independent variable on the left, dependent variable on the right – Ceasefire  Fewer youth gun deaths “One-tailed” hypotheses predict an effect and specify its direction (fewer gun deaths) “Two tailed” hypotheses predict an effect but do not specify its direction – Ceasefire affected the number of youths murdered (silent as to increase/decrease) One-tailed hypotheses are the most common. They predict either a positive or negative relationship between the independent and dependent variables. – If the scores of the independent and dependent variables rise and fall in unison, the relationship between the variables is “positive” – If the scores of the independent and dependent variables move in opposite directions, their relationship is “negative” – Whether the predicted relationship is “positive” or “negative” depends on how variables are measured – Here the relationship is negative – when Ceasefire is “yes,” gun deaths go down; when “no,” they go up – “Negative” relationships are just as much a relationship as “positive” relationships

6. CauseEffect measured with a speedometer Independent variableDependent variable measured with a pressure gauge or by changes in angle of pedal Get in the car and drive! Hypothesis 1: As pressure on accelerator increases, speed increases (positive relationship) Hypothesis 2: As pressure on brake increases, speed decreases (negative relationship)

7. Important concepts Population – Entire group to which we can project the findings of a study – Effects of imprisonment: U.S. population – Ceasefire: All youth in Boston Sample – Any subgroup – To use it to describe a population, the sample must be “representative” as to all vital characteristics Unit of analysis – “Persons, places, things or events” under study – The “container” for the variables – Incarceration study: Incidents of violent crime – Boston: youth homicides Contains the independent (causal) variable time period (before/after Ceasefire) and the dependent (effect) variable youth homicides (monthly total) Case – A single occurrence of a unit of analysis – Incarceration study: Each violent crime – Boston: Each homicide Population Sample

8. MEASUREMENT

9. What gets measured? “Variables” A variable is any characteristic of a person, thing or event that can take on different values – Number of youth gun deaths – Age – Gender – Attitude – Etc. etc. Characteristic must be measurable – Category (e.g., M/F) or – Scale (e.g., 1-100) Coding – Process of assigning a categorical value or numerical score to a variable

10. Categorical variables Nominal – Mutually exclusive groups or categories Period studied in Boston: pre-Ceasefire or post- Ceasefire? Other common categorical variables are gender and color Ordinal – Low/medium/high – Rank implied

11. Continuous variables Can be placed on a scale – Length, height, weight, temperature, etc. – Differences between adjacent points are equal (distance between 2 and 3 same as between 6 and 7) – Number of violent incidents – Number of Boston youth shot dead each month Ordinal categorical variables are sometimes “transformed” into continuous variables – Low  1, medium  2, high  3 Continuous variables are sometimes transformed into ordinal variables – 1-10  low, 11-20  medium, 21- 30  high

12. Validity and reliability Is the measurement VALID? – Are we measuring what we say we are measuring? – Do the results reflect something real? Measuring how much one weighs is far simpler and more straightforward than measuring their “attitude” – Some things can’t be directly measured Use “surrogate” measures (e.g., income for poverty) Is the measurement process RELIABLE? – Is it reproducible? Does it consistently yield the same values? Are we throwing away information? – Collapsing continuous variables into categories Are surrogate measures giving us what we need? – Does number of arrests adequately measure police effectiveness? Click here for more on validity and reliabilityhere

13. Class assignment Researchers ride along with cops to observe whether youths’ demeanor affects how officers respond What is the research question? Is this descriptive or explanatory research? What is the unit of analysis? What is the independent variable? What kind is it? What is the dependent variable? What kind is it? How would you measure each variable? Are there concerns about reliability and validity? Formulate a hypothesis that predicts the direction of change in what you feel is the most likely direction

14. ISSUES IN MEASUREMENT

15. “Intervening” variables Poverty  Crime Poverty is strongly associated with crime – So is it simply Poverty  Crime? – Or is there something else at work? Poor people tend to get poor educations Maybe education is a more powerful predictor of crime than income If this is the actual causal order, education is an “intervening” variable Poverty (as measured by income) is still a factor, but its influence is mediated by education Education is a more proximate (closer) “cause” of crime Bottom line -- we must study all variables (a) that could affect the dependent variable and (b) are related to our independent variable of interest But sometimes what seemed to be a cause turns out not to be a factor at all… Income Education Crime

16. Spurious Relationship Age  Height? Given that by 18 one has usually stopped growing, how could changes in age in this sample really affect changes in height? 24 students: 12 males, 12 females, age range 18-25 r statistic - Correlation

17. Spurious Relationship Age  Height? The apparent relationship between age and height is spurious. There only seemed to be a relationship because in this sample, males, who tend to be taller, also happened to be older. It’s still Gender  Height M M M M M M M M M M M M M M 24 students: 12 males, 12 females, age range 18-25 r statistic - Correlation

18. Former chief Bratton was repeatedly credited with reducing crime in L.A. Hypothesis: Bratton  less crime (negative relationship) Bratton (yes/no) is the independent, causal variable; crime the dependent (effect) variable But could the apparent relationship between Bratton and crime reduction be spurious? Spurious means that an apparent relationship can be explained away by other factors While Bratton was chief crime was falling around the U.S. for various reasons These real, underlying causes (each would be an independent variable, on the left of the arrow) may be why crime fell in L.A., not Bratton Spurious relationship Bratton Crime