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Repeated cross-sectional designs

(long) Sample drawn from population at Time 1 (T1), data collected Time passes, some people leave population, others enter At Time 2 (T2), different sample drawn from population & data collected

time order

A criterion for establishing a causal relation between two variables; the variation in the independent variable must come before variation in the dependent variable We must also ensure that the variation in the independent variable came before variation in the dependent variable—the cause must come before the presumed effect

association

A criterion for establishing a causal relationship between two variables; variation in one variable is related to variation in another variable as a condition to determine causality A change in the independent variable is associated with—correlated with—a change in the dependent variable. By contrast, if there is no association between two variables, there cannot be a causal relationship.

What is treatment misidentification? What are its sources?

A problem that occurs in an experiment when the treatment itself is not what causes the outcome, but rather the outcome is caused by some intervening process that the researcher has not identified and is not aware of Expectancies of experimental staff. Placebo effect. Hawthorne effect

Differential attrition

A problem that occurs in experiments when comparison groups become different because subjects are more likely to drop out of one of the groups for various reasons

Random assignment (randomization)

A procedure by which experimental and control group subjects are placed in groups randomly does not help at all to ensure that the research subjects are representative of some larger population; instead, representativeness is the goal of random sampling. What random assignment does—create two (or more) equivalent groups—is useful for ensuring internal validity, not generalizability.

Nonequivalent control group designs

A quasi-experimental design in which there are experimental and comparison groups that are designated before the treatment occurs but are not created by random assignment n this type of quasi-experimental design, a comparison group is selected to be as comparable as possible to the treatment group. Two selection methods can be used: Individual Matching Agregate matching

Nonspuriousness

A relationship that exists between two variables that is not due to variation in a third variable "Correlation does not prove causation"? It is meant to remind us that an association between two variables might be caused by something else

Quasi-Experimental Design

A research design in which there is a comparison group that is comparable to the experimental group in critical ways, but subjects are not randomly assigned to the comparison and experimental groups Used when true experiment not possible (feasible) Comparison group is predetermined to be comparable to the treatment group

Endogenous change

A source of causal invalidity that occurs when natural developments or changes in the subjects (independent of the experimental treatment itself) account for some or all of the observed change from the pretest to the posttest

External Events/history effect

A source of causal invalidity that occurs when something other than the treatment influences outcome scores; also called an effect of external events

Regression effect

A source of causal invalidity that occurs when subjects who are chosen for a study because of their extreme scores on the dependent variable become less extreme on the posttest due to natural cyclical or episodic change in the variable

Contamination

A source of causal invalidity that occurs when the experimental and/or the comparison group is aware of the other group and is influenced in the posttest as a result

placebo effect

A source of treatment misidentification that can occur when subjects receive a treatment that they consider likely to be beneficial and improve because of that expectation rather than because of the treatment itself

Compensatory rivalry/John Henry effec

A type of contamination in experimental and quasi-experimental designs that occurs when control group members are aware that they are being denied some advantage and increase their efforts by way of compensation

Hawthorne effect

A type of contamination in experimental and quasi-experimental designs that occurs when members of the treatment group change in terms of the dependent variable because their participation in the study makes them feel special

Solomon four-group design

An experimental design in which there are four groups. Two of the groups represent a classic experimental design in which there is an experimental and a control group that each receives a pretest. The final two groups represent an experimental and a control group but neither receives a pretest. This design helps to identify the interaction of testing and treatment participants randomly assigned to at least two experimental groups and at least two comparison groups One experimental group and one comparison group have pretest, other two will not

Field experiment

An experimental study conducted in a real-world setting social experiments are not always conducted in a laboratory or controlled environment. In fact, many experiments are conducted out in the real world. Whenever studies utilize the conditions of an experimental method in a real-world setting, t

What are the criteria for establishing a causal relationship?

Association Time Order Relation to IV and DV Nonspuriousness and spurious

Expectancies of experimenter

Change among experimental subjects may be due by it rather than the treatment itself

Experimental & Control Groups

Control groups are not exposed to the stimulus or IV Best way to address experimental effects You can have more than one experimental or control group

correlation

Correlation is a statistical technique which tells us how strongly the pair of variables are linearly related and change together. It does not tell us why and how behind the relationship but it just says the relationship exists.

True Experiments

Experimental research provides the most powerful design for testing causal hypotheses because it allows us to confidently establish the first three criteria for causality—association, time order, and nonspuriousness. True experiments have at least three features that help us meet these criteria: Experiment in which subjects are assigned randomly to an experimental group that receives a treatment or other manipulation of the independent variable and a comparison group that does not receive the treatment or receives some other manipulation; outcomes are measured in a posttest doesnt require a pretest A systematic attempt to test a causal hypothesis about the effect of variations in one factor on another" Three major components: Independent and dependent variables Pretesting and post-testing Experimental and control groups Fourth component? Randomization

experimental group

In an experiment, the group of subjects that receives the treatment or experimental manipulation

aggregate matching

In most situations when random assignment is not possible, this second method of matching makes more sense: identifying a comparison group that matches the treatment group in the aggregate rather than trying to match individual cases. This means finding a comparison group that has similar distributions on key variables: the same average age, the same percentage female, and so on.

individual matching

Individual cases in the treatment group are matched with similar individuals in the comparison group. This can sometimes create a comparison group that is very similar to the experimental group.

Posttest

Measurement of an outcome (dependent) variable after an experimental intervention or after a presumed independent variable has changed for some other reason —that is, a measurement of the outcome in both groups after the experimental group has received the treatment.

pretest

Measurement of an outcome (dependent) variable prior to an experimental intervention or change in a presumed independent variable that measure the dependent variable before the experimental intervention. A pretest is exactly the same as a posttest, just administered at a different time.

Before-and-After Designs

Most common feature of before-and-after designs - NO comparison group Fixed-sample panel design (panel study)Repeated measures panel design -A type of longitudinal study in which data are collected from the same individuals—the panel—at two or more points in time; in another type of panel design, panel members who leave are replaced with new members

what "correlation does not prove causation" means

No. Two things are correlated doesn't mean one causes other. Correlation does not mean causality

Types of Quasi-Experimental Designs

Nonequivalent control group designs Before-and-after designs Ex post facto control group designs

Fixed-sample panel designs Event-based designs

Sample (called "panel") drawn from population at T1, data collected Time passes, some panel members unavailable for follow-up, population changes At T2, data collected from same people at T1 (the "panel"}, except those people who cannot be located

Variations in the Classical Experimental Design

The design can vary in terms of these four building blocks Posttest only Multiple experimental groups Number of experimental and control groups Number of and variation of independent variable (IV) Number of pretest and posttest measurements Procedures used to select subjects and assign them to a group (random assignment)

control or comparison group

The group of subjects who are either exposed to a different treatment than the experimental group or who receive no treatment at all one receiving the experimental condition (e.g., treatment or intervention), termed the experimental group, and the other receiving no treatment/intervention or another form thereof, termed the control group.

Sample generalizability

ability to apply findings to clearly defined, larger population

cause

an explanation for some characteristic, attitude, or behavior of groups, individuals, or other entities (such as families, organizations, or cities) or for events.

Selection bias

characteristics of experimental group subjects differ

Cross-sectional research design

data collected at one point in time

Longitudinal research designs

data collected at two or more points in time

spurious

meaning apparently but not actually valid—that is, false A relationship between two variables that is due to variation in a third variable

DV

potential effect

IV

presumed cause An experiment examines the effect of an independent variable (IV) on a dependent variable (DV) Typically, the IV is either present or not present IV is a stimulus or "treatment IV affects change in the Dv

Double-Blind Studies

researchers can get overly excited about their research Our own biases can prompt us to indulge in prejudgments or give cues that prompt desired behaviors Neither participants nor researchers know who is in which group Experimental Control An experimental method in which neither subjects nor the staff delivering experimental treatments know which subjects are getting the treatment and which are receiving a placebo

Causation

takes a step further than correlation. It says any change in the value of one variable will cause a change in the value of another variable, which means one variable makes other to happen. It is also referred as cause and effect.

event-Based Designs also cohort

the follow-up samples (at one or more times) are selected from the same cohort, people who all have experienced a similar event or a common starting point.

Causal (internal) validity

the truthfulness of an assertion that A causes B

true experiments have at least three features

two comparison groups—one receiving the experimental condition (e.g., treatment or intervention), termed the experimental group, and the other receiving no treatment/intervention or another form thereof, termed the control group. Random assignment to the two (or more) comparison groups. Assessment of change in the dependent variable for both groups after the experimental condition has been applied. This is usually called a posttest. We can determine whether an association exists between the independent and dependent variables in a true experiment because two or more groups differ in terms of their value on the independent variable. One group, the experimental group, receives some "treatment" that is a manipulation of the value of the independent variable. In a simple experiment, there may be one other group that does not receive the treatment; it is termed the control or comparison group.