Research Designs

REVIEW

Review -- research General types of research – Descriptive (“what”) – Exploratory (find out enough to ask “why”) – Explanatory (“why”) Unit of analysis: “object, entity or process” under study – Contains the variables being measured – Case: A single instance of a unit of analysis

Review - variable Any characteristic that is (a) measurable (b) can take on different values in the population or over time – Incarceration rate (no. of persons sent to prison/100,000 population) – Mean number of Boston youth shot dead each month – Period when Boston youth were shot dead (pre- or post-Ceasefire) – Height, weight, gender Types of variables – Categorical Nominal: mutually exclusive categories (e.g., M/F) Ordinal: implied ranking (low/medium/high) – Continuous (e.g., height, weight, scale 1-20) – Can “transform” ordinal into continuous, and continuous into ordinal Low/medium/high  1-3 scale 1-20 scale  low (1-5)/medium (6-10)/high (11+) Coding – Process of assigning a measurement to a variable – To use certain statistical techniques, nominal variables are sometimes recoded as “dummy” variables M/F recoded 0 for male, 1 for female

Review - distributions An arrangement of cases in a sample or population according to their values or scores on one or more variables Statistics – mean, median, mode, range, standard deviation – summarize distributions

Review - association and causation Association means that the values of two or more variables change together – In Boston, the number of youth shot dead appears to be associated with the study period – After invoking Ceasefire the mean number of youths slain by gunfire drops Causation means that changes in one variable cause corresponding changes in another variable. – The causal variable is called the “independent” variable (here it’s the time period) – The effect variable is called the “dependent” variable (here it’s the mean number of monthly deaths) – So, did Ceasefire cause the reduction?

RESEARCH DESIGNS – NON-EXPERIMENTAL

Principles of non-experimental designs Begin with a hypothesis – Changes in independent variables(s)  changes in dependent variable(s) – Lower income  more crime Assess the hypothesis by collecting data on variables of interest. – Data usually reflects the values of variables at one point in time – Data can also be collected in “waves,” meaning at succeeding points in time – In non-experimental designs investigators only collect data - they do nothing that might affect the values of the variables Data sources – Field observations Field observations – Surveys Surveys – Official sources (public records, census, etc.) Official sources Non-experimental designs

Data source: field observations Research question: do police officers take harsher legal measures if youths display a bad attitude? Hypothesis: worse demeanor  harsher disposition Researchers rode along with cops to observe their interactions with youths Researchers did NOT intervene -- they let things be Researchers coded... – Independent variable: youth’s demeanor (2 values) – Dependent variable: officer disposition (4 values) At a later time they used statistical techniques to assess whether youth’s demeanor was associated with officer disposition in the hypothesized direction (the worse the demeanor, the harsher the disposition) Depending on the strength of this association they might conclude: – There is a cause-and-effect relationship between the variables: hypothesis confirmed – The association does not go beyond what could be obtained by chance: hypothesis rejected Non-experimental designs

Data source: official sources Panel 5 Panel 9 Panel 10

Non-experimental designs Data source: surveys Panel 1 Panel 6 Panel 4

Non-experimental designs Data source: surveys Panel 8 Panel 12

Data sources: surveys + official sources Non-experimental designs Panel 3 Panel 7

Data sources: surveys + official sources Non-experimental designs Panel 11

Causal order: Did the change in the independent variable precede (come before) the change in the dependent variable? Poverty  crime OR Crime  poverty Intervening variables: Could lack of education or living in a violent area be the more proximate (closer) cause of crime? Poverty  poor education  crime Here poverty is still the cause, but it affects crime through intervening variable education, which is the more proximate cause Spurious relationship: What seems to be a relationship isn’t - it’s bogus! ―Often caused by a strong association between the independent variable of interest (e.g., poverty) and another independent variable (e.g., poor social controls) which turn out to be the real cause Poor social controls  crime Poverty Issues in non-experimental designs Non-experimental designs

RESEARCH DESIGNS – EXPERIMENTAL

Principles of experimental designs Purposes – Eliminate other possible “causes” (e.g., that it’s education, not poverty) – Set the causal order (e.g., know you are testing crime  poverty) Method 1.Randomly assign cases to two or more groups. Designate one or more groups as “experimental” and one as “control” 2.Measure the dependent variable (time 1) for each group. Random assignment insures that the mean values of the independent variable(s) should be about the same for each group. 3.Intervene in the experimental group by adjusting the level of the independent variable of interest 4.Post-measure dependent variable (time 2) for each group. If the differences between experimental and control groups are “statistically significant” they can be attributed to the intervention. Simple experiment intervention (adjust level of independent variable) Experimental group ( X ) DV t1 ……….IV……….DV t2 Control group ( C ) DV t1 …..………………DV t2 (no intervention) Experimental designs

population: 200 patrol officers 150 males (75%)50 females (25%) Apply the intervention (apply the value of the independent variable – the program.) NO YES YES NO CONTROL GROUP Randomly Assign 25 Officers CONTROL GROUP Randomly Assign 25 Officers EXPERIMENTAL GROUP Randomly Assign 25 Officers EXPERIMENTAL GROUP Randomly Assign 25 Officers For each group, pre-measure dependent variable officer cynicism Hypothesis: officers who complete a special training program will be less cynical For each group, post-measure dependent variable officer cynicism Also compare within-group changes – what do they tell us? Experimental designs

Hypothesis: SOCP reduces recidivism 1.Independent (causal) variable: SOCP (yes/no) (categorical/nominal) 2.Dependent (effect) variable: recidivism (rearrest rate, continuous) 3.Randomly assign youths being released to either X or C – Random assignment makes them equal overall for background factors such as age, criminal record, etc. 4.X (experimental group) gets intensive supervision (SOCP yes) 5.C (control group) remains with regular supervision (SOCP no) 6.Wait two years, compare recidivism – Does the X group have a significantly lower rearrest rate? – Does the X group have significantly lower rates of drug & alcohol use? Population: youths cited during an 18-month period Apply the intervention YES NO CONTROL GROUP Randomly Assign 265 youths EXPERIMENTAL GROUP Randomly Assign 264 youths pre-measure arrest record, drug and alcohol use, etc. For each group, post-measure dependent variable measures: arrest record, drug and alcohol use, etc.

1973 Kansas City Patrol Experiment Research question: Does routine patrol deter crime? Hypothesis: Routine patrol reduces crime 1.Independent (causal) variable: Patrol (categorical/ordinal - three levels) 2.Dependent (effect) variable: crime rate (continuous) 3.Randomly divide an area into 15 beats 4.Measure crime in each beat 5.Randomly assign each a different value of the independent variable – Five C (control) beats: same patrol as usual – Five X 1 (experimental) beats: no patrol (“R” - reactive - only answer calls for service) – Five X 2 experimental beats: more patrol than usual (“P” - proactive - more cars cruising, looking for trouble) 6.After one year compare crime rates Experimental designs

Some issues with experimental designs According to the Kansas City experimenters, there was no significant difference in crime rates between the experimental and control groups. – Since neither increasing nor decreasing patrol made a difference, the hypothesis that random patrol can reduce crime was rejected. However, the experiment was later criticized: – Level of the independent variable (amount of patrol) was not sufficiently increased in the proactive beats to be able to demonstrate a statistically significant effect – Due to contamination by other units, level of patrol was not sufficiently reduced in the reactive beats to be able to demonstrate a statistically significant effect Other constraints – Practicality Could we experimentally test poverty  crime? – Ethics Should we experimentally test poverty  crime? Can we make some people poor, then see what happens! Experimental designs

RESEARCH DESIGNS – QUASI-EXPERIMENTAL

Quasi-experimental designs & issues Experiment that lacks random assignment to groups – Groups might differ along a key independent variable (“matching” often used to try to make up for this) Experiment without a control group – An extraneous event might be the true cause of the change in the dependent variable A non-experimental design that mimics an experiment – A known intervention did take place (e.g., it’s known that the level of the independent variable did change at a certain time) – Measures of the dependent variable are available for the periods before and after the intervention Quasi-experimental designs

Data source: Vignette Panel 2 “Vignettes” are brief descriptions of actual or realistic events that are administered to elicit responses by test subjects to key issues of interest to researchers In this example a vignette is used to test the hypothesis that police officers with military experience are more likely, in domestic violence situations, to show leniency to other veterans – Officer’s veteran status  Officer’s disposition Since everyone was administered the vignette (there was no equivalent “control group”) the possibility exists that independent variables other than those tested could explain why officers acted as they did Quasi-experimental designs