1. Types of research
Descriptive
Explanatory

2. Descriptive research
Using data to describe situations and trends - no attempt to explain why
How many people are murdered each year?
Murder rate per 100,000 pop.
Trend over time - up, down?
How many students of each gender?
Proportion of students (%) by gender
Mean (arithmetic average) age and height

3. Explanatory research More imprisonment  less violence?
Using data to explain why things change
More imprisonment  less violence?
During 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 per 100,000. By 2000 it was 506.5, one-third lower.
Some researchers believe that falling crime rates were caused by harsher sentencing. One criminologist estimated that increased incarceration accounted for 20 percent of the “Great Crime Drop” of the 90’s.

4. Explanatory research Ceasefire  less youth violence?
Using data to explain why things change
Ceasefire  less youth violence?
During 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 additional benefit of Ceasefire was estimated at 14 percent.
Ceasefire = N
(OFF)
Ceasefire = Y
(ON)

5. RESEARCH PROCESS: measuring VARIABLES

6. What gets measured? “Variables”
To describe or explain, we must measure
What gets measured? Variables.
Variable: any characteristic or aspect of a person, thing or event that can be “measured”, that is, given a score or assigned a value
Age, height, gender

7. What gets measured? “Variables”
To describe or explain, we must measure
What gets measured? Variables.
Variable: any characteristic or aspect of a person, thing or event that can be “measured”, that is, given a score or assigned a value
Ceasefire status: Off or On
3.5
1.3
Ceasefire = N (OFF)
Ceasefire = Y (ON)

8. Types of variables
Categorical
Continuous

9. Categorical variables
Nominal
Mutually exclusive groups or categories
Gender: M/F
Color
Boston Ceasefire status (Y/N or OFF/ON)
To use certain statistical techniques nominal variables are sometimes recoded as “dummy” (0/1) variables
Gender 0=male, 1=female
Ceasefire 0=off, 1=on
Ordinal
Plus an implied ranking
Low/medium/high
Poor/fair/ good
This table displays two categorical/ordinal variables
Ceasefire = N (OFF)
Ceasefire = Y (ON)
Car value
Low
Medium
High
Low income 5 8 3
High income 1 4
Income

10. Continuous variables Measured on a scale (e.g., 1-100)
Differences between adjacent points must be equal (distance between 2 and 3 same as between 6 and 7)
Examples of continuous variables
Length, height, weight, temperature
Number of youth shot dead each month (varies month-to-month)
Mean (average number) youth shot dead each month
3.5
1.3

11. Going between categorical and continuous variables
Car values
Low
Medium
High
Low income 5 8 3
High income 1 4
Categorical/ordinal variables are sometimes “transformed” into continuous variables
Example:
Low  1
Medium  2
High  3
Car values 1 2 3
Low income 5 8
High income 4
Car values 1 2 3 4 5 6
Low income
High income
Continuous variables are sometimes “transformed” into categorical/ordinal variables
Example:
1-2  low
3-4  medium
5-6  high
Car values
Low
Medium
High
Low income 5 8 3
High income 1 4

12. RESEARCH PROCESS
Research question
Hypothesis

13. Research process: formulate a research question
Researchers begin with a “research question” relating to their area of interest
Could taking guns off the street reduce violence?
They review the literature review to find prior research into their topic of interest and related issues
“Hmm. A couple articles mention an anti-gun violence program in Boston called ‘ceasefire.’ Did it seem to reduce gun homicides among youths?”
This article and others are used to identify relevant variables and figure out how they should be measured
Ceasefire program (categorical/ordinal = on / off)
Monthly youth gun homicides (continuous = no. of homicides or monthly mean [average])

14. Ceasefire  fewer youth gun deaths
Hypothesis
If researchers feel there is causation, they formulate a hypothesis - a prediction of cause and effect. Every hypothesis has at least one “independent” variable and at least one “dependent” variable.
Independent variable: The causal or predictor variable. Its change is predicted to cause (therefore, precede) change in the other variable. Here it’s Ceasefire status (on/off).
Dependent variable: The effect variable, whose change is being predicted. Here it’s number of youth gun homicides each month.
Why it called this? Because its value or level is predicted to “depend” on the value or score of the independent variable.
Normally the independent variable is on the left side of the hypothesis, and the dependent variable is on the right
Ceasefire  fewer youth gun deaths

15. Get in the car and drive!
Hypothesis 1: More pressure on accelerator  higher speed (positive relationship) Hypothesis 2: More pressure on brake  lower speed (negative relationship)
Independent variable
Dependent variable
Cause
Effect
measured with a speedometer
measured with a pressure gauge or by changes in angle of pedal

16. More about hypotheses
Variables given in hypotheses are often simplified, summarized versions of the real variables
In hypothesis “college education improves police job performance,” dependent variable police job performance reflects several measures, including disciplinary actions (Y/N, categorical) and ratings by supervisors (scale, continuous.) Each of these measures is a variable in its own right.
A quick tip-off is that a variable cannot be directly measured. For example, in “poverty causes crime,” what does “poverty” mean? Usually it means income, the actual, measured variable.
Direction of effect of independent variable on dependent variable
“One-tailed” hypotheses specify the direction of the effect of the independent on the dependent variable
Ceasefire leads to fewer youth gun deaths
“Two tailed” hypotheses do not specify the direction of the effect
Ceasefire affects the number of youth gun deaths (silent as to increase or decrease of dependent variable as the independent variable changes) ?

17. More about hypotheses
Positive or negative relationship between variables?
Positive: Scores of the independent and dependent variables rise and fall together
Negative: Scores of the independent and dependent variables move in opposite directions.
Whether the predicted relationship is “positive” or “negative” depends on how the variables are measured
Our ceasefire hypothesis predicts a negative relationship.
When Ceasefire is off, gun deaths are high; when it’s on, gun deaths are low
Negative does NOT mean “no” relationship - it is just as much a relationship as a “positive” relationship

18. Recap: research process through hypothesis
STEP ONE: RESEARCH QUESTION
Is there a way to reduce youth gun deaths?
Can targeting problem youths reduce gun homicide?
STEP TWO: LITERATURE REVIEW
Check prior studies about reducing youth gun deaths.
Where did the data come from?
What programs or approaches were used?
Did they prove effective?
Identify the relevant variables and determine how to measure them
STEP THREE: HYPOTHESIS
Brief, declarative statement of cause and effect, with a measurable independent variable on the left (Ceasefire, on/off) and a measurable dependent variable on the right (number of youth gun deaths each month). No extraneous words!
Ceasefire leads to fewer youth gun deaths

19. GATHERING DATA TO TEST HYPOTHESES
Unit of analysis and “case”
“Coding” variables

20. Two important terms: “unit of analysis” and “case”
“Persons, places, things or events” under study
A container for all the variables
Youth gun homicide
Contains the independent (causal/predictor) variable Ceasefire (yes/no) and the dependent (effect) variable youth homicides (number)
Sometimes defining the unit of analysis is tricky (we’ll have an example later)
Case
A single occurrence of a unit of analysis
Each youth gun homicide
Ceasefire OFF
Ceasefire ON

21. Coding Assigning a value or score to a variable
by the subjects of a study
through “observation” by researchers
Accurately assigning values requires they be “operationalized,” meaning specifically defined

22. CLASSROOM EXERCISE - hypothesis building

23. Hypothesis building
Make up a hypothesis with one independent (causal) and one dependent (effect) variable.
Hypothesis must predict that changes in the independent variable cause corresponding changes in the dependent variable
Variables must be accurately measurable
1. State your hypothesis. Do so in the conventional manner, with the independent (causal/predictor) variable on the left, and the dependent (effect) variable on the right:
2. Identify the independent variable
3. How is it measured (categorical or continuous)?
4. Identify the dependent variable
5. How is it measured (categorical or continuous)?
6. Is the predicted relationship between variables positive or negative?

24. ISSUES Validity Reliability Intervening variables
Association and causation
Spurious relationships

25. Validity Is a measurement VALID?
Are we measuring what we claim to be measuring?
Do the results reflect a real-world characteristic?
Age, gender and weight definitely exist in the real world
Age, gender and weight can be accurately measured
What about “attitude”?

26. Reliability Does our measurement process yield RELIABLE results?
Is the process reproducible? Regardless of who measures, does the method arrive at the same values?
Is it accurate? Are we throwing away information?
Needlessly collapsing continuous variables into categories
Some variables are valid (exist in the real world) but can’t be directly measured
Attitude
Poverty
When this is so, does our system accurately measure the variable of interest?
Can where a car is parked (faculty/student lot) reliably reflect its driver’s income?

27. “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 that is true, education may be 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 seems to be a cause turns out not to be a factor at all…
Income Education Crime

28. Association and causation
Association means that two or more variables seem to 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
Association does not necessarily mean causation
Causation takes it a big step further. It means that changes in one variable cause changes in one or more other variables.
Imprisonment reduces violent crime
Ceasefire reduces youth gun deaths
Ceasefire OFF
Ceasefire ON

29. Spurious Relationships
When it looks like there is a causal relationship between variables
But there isn’t!
24 CSUF students: 12 males, 12 females, age range 18-25
Age  Height?
Given that by 18 one has usually stopped growing, how could changes in age in this sample really affect changes in height?

30. Spurious Relationship
24 CSUF students: 12 males, 12 females, age range 18-25
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, for no particular reason also happened to be older.
It’s still Gender  Height M M M M M M M M M M M M M M

31. A spurious relationship?
Bratton: NO
Bratton: YES
Bratton  less crime
Bratton Crime rates
ON High
OFF Low
Former chief Bratton was repeatedly credited with reducing crime in L.A.
Presence of Bratton (yes/no, categorical/nominal) is the independent (causal) variable
Level of crime, expressed as a rate, a continuous measure, is the dependent (effect) variable
Negative relationship (Bratton goes from off to on, crime rates drop; goes from on to off, rates increase)
Could the apparent relationship be spurious?
Could it be caused by the effect(s) of other independent variables?
While Bratton was chief, crime was falling around the U.S.
These underlying causes (each an independent variable, left side of the arrow) - not Bratton - may explain why crime also fell in L.A.