adv 309r exam

types of experiment's external validity

- population-related external validity - setting-related - measurement-related

pretest to post-test with control

- two groups with four measures - makes certain there is equivalency between the treatment and control groups before the start of the research - An alternative to the post-test only with control design - Both designs control for premeasurement, history, maturation, and instrumentation threats - Controls selection threats - Weakness: doesn't eliminate mortality threats - *true experimental*

pearson's correlation coefficient range

-1, 1

t-test process

1. check assumptions 2. calculate t-statistics 3. conclusions

requirements for causality

1. events must take place in the proper order 2. events must take place at the same time and show an explicit relationship 3. alternative explanations must be reduced and eliminated whenever possible 4. strength of association (p value)

characteristics of experiments

1. identify what you need to learn 2. take the relevant actions (conduct the experiment by manipulating one or more variables) 3. observe the effects and consequences of those actions on other variables, and then 4. determine the extent to which the observed effects can be attributed to actions taken

anova assumptions

1. samples are random and independent of each other 2. the populations are normally distributed 3. the populations all have the same variance

two variable design (i.e., factorial design)

1. study two or more factors 2. crossed factors are arranged in a factorial design

a researcher wants to compare media use among four different ethnic groups. What statistical test should be applied?

ANOVA

chi square

an examination of a pattern of responses to see the difference between the expected and the observed result - when data represents frequency or percentages (nominal level data) - subjects in each category are independent - when there is only one independent variable

mortality threats

arise when respondents drop out of a study btwn the pretest and the post-test and as a result, it cannot be determined whether differences between the two tests are the results of the experimental treatment or are the result of different group characteristics at the two stages of measurement

t-test

assignment to one of no more than two groups (independent variable)

sources of variability in an anova

between group and within group

solomon four-group design

both the most powerful and the most-resource intensive experimental design - Controls for all major threats to an experiment's internal validity - Combines a pretest to post-test with control design with the post-test only with control design - Weakness: complex, costly, takes longer - *true experimental*

f-ratio

btwn group variability/within group variability

experiments

can be used to bypass recall and actually observe behaviors

testing threats

can result from repeated administrations of the same questionnaire or survey, when it is administered once before the treatment, and once again after the treatment - a consumer may know how to answer the test questions "correctly" on the second try

in a five group comparison study, greater within group variance will __________, your f-ratio by definition

decrease

internal validity

determines the extent to which we have confidence that observed results are due to the experimental manipulations

anova is restricted to 3 or 4 group comparisons

false

r=1 is stronger than r=-1

false

f-ratio score tells you what means are different

false: it tells us that they are different not which are different

R-squared

gives you the variance explained in the dependent variable - always expressed as a percentage

two group post-test with control

has two groups, one with pretest and posttest and one just with a post-test - The main problem with this is that there was no true random assignment so there is no way to tell or even assume any equivalence in pretest scores - *quasi*

true experimental designs

have a control group and use random assignment to form test and control groups 5 most common types: 1. simulated pretest to post-test 2. post-test only with control 3. pretest to post-test with control 4. solomon four-group design 5. factorial designs

in a factorial design, the two factors are considered the

independent variables

external validity

influences the extent to which the experimental results are generalizable to the "real world" - population related - measurement related - setting-related external validity

quasi-experimental design

not true experiments; fail to eliminate a large number of threats to internal validity common types: 1. one group post test only 2. one group pretest to post-test 3. two group post-test with control

factorial design

observes the effects of two or more independent variables at the same time where each of these variables has two or more levels or aspects - Most appropriate if you want to see the effect of two independent variables working together

interaction threats

occur whenever an interview administered before the start of the experiment affects a respondent's sensitivity or responsiveness to the independent variable - occur whenever the independent variable is more likely to be noticed and reacted to then it would be without exposure to the initial measurement instrument

selection threats

occur whenever, at the start of the experiment, the tests and control groups differ in terms of relevant demographics, attitudes, or behaviors

researcher bias threats

occurs whenever the actions of the experimenter affects the outcome of an experiment

a company wants to compare their latest commercial against a pool or prior commercials. what statistical test should be run?

one sample t-test

inferential statistics

provides the basis for objectively determining the amount of confidence you can have in drawing conclusions about differences and relationships observed in a set of data

history threats

refers to any events or influences beyond those intentionally manipulated by the researcher that occur during the experiment and that have the potential to affect the experimental outcome as measured by the dependent variables - ex. weather

instrumentation threats

refers to changes made to the measurement instrument or data recording techniques during the experiment

surveys

respondents are asked to recall and report on their behaviors

maturation

respondents attitudes, behaviors, and physiology change during an experiment - all of these have the potential to affect and distort the levels of the dependent variable

one group pretest to post-test

similar to the one group post-test only except there's a pretest - assessed by comparing the levels of the dependent measure in the post-treatment to the levels in the pretreatment measures - no control for any historical threats to internal validity - premeasurement threats -*quasi*

one group post-test only

takes a group of individuals, exposes them to the treatment or experimental manipulation (the independent variable), and then measures the dependent variable(s) as part of the post test - you have to use judgment since there is no control or reference group - fails to control for several additional threats to internal validity, specifically maturation, selection, and mortality - *quasi*

two variable main effect

the change in response produced by a change in the level of the factor

interaction

the combinations of main effects on the dependent variables

main effects

the separate influence of each independent variable on the dependent variable

goal of an experiment

to determine causality- the effect of changes in one area on one or more other areas. This is important because its more accurate than descriptive research in making decisions on advertising

"R" assumes that one variable predicts the other

true

"r" simply assumes the two continuous variables are related

true

correlation and simple regression are about looking at relationships between two variables

true

multiple regression is appropriate when there is more than one independent variable and one dependent variable

true

pearson's correlation coefficient: "r" shows linear correlation between two continuous variables

true

hi and low media use is though to affect attitude toward advertising. to test this what statistical test should be applied?

two sample t-test

paired t-test

used to compare the means of two dependent samples

two-sample t-test

used to determine whether the mean of one group is equal to, larger than or smaller than the mean of another group

one-way anova

used to determine whether three or more populations have different distributions

one sample t-test

used to test whether the population mean is different from a specified value

simulated pretest-post-test

uses one group of randomly assigned respondents for the pretest measurement and a second group of randomly assigned respondents for the treatment and post-test measurement - controls for pre measurement and interaction threats - The simulated pretest and post-test design estimates treatment effects by comparing one group's pretest to a different groups post test - Weaknesses: doesn't control for any of the other threats like history, maturation, instrumentation, and selection - *true experimental*

post-test only with control

utilizes two groups of respondents and two measures; compares exclusively post-test measures, one measure obtained from the treatment group and one from the control - Controls for a greater number of threats to internal validity - Controls for history, maturation, and instrumentation, as well as premeasurement, and interaction threats - Weakness: doesn't eliminate mortality threats - *true experimental*

equal variance

variances are approximately the same across the samples - you have two different means: if the variation is relatively equal, that is one sample variance is no larger twice the size of the size of the other, then you can assume this

independent variable

what the experiment manipulates

dependent variable

what the researcher is interested in explaining, and as a result, is the measure used to evaluate the influence of the independent variable

premeasurement threats

whenever an interview administered before the start of an experiment has a direct effect on a respondent's attitudes, actions, or behaviors during the experiment - occurs without exposure to the independent variable - all observed attitudinal or behavioral changes are the result of exposure to the initial measurement instrument

difference between r and R

with r, the two variables are treated as equals. in regression (R), one variable is considered the independent (predictor) variable and the other the dependent (outcome) variable