final exam! Flashcards
(28 cards)
basic steps of scientific method
Making an observation.
Forming a hypothesis.
Making a prediction.
Experimenting to test the hypothesis.
quasi experimental research used when?
does not involve control over the assignment of participants to conditions. It tries to
isolate a casual influence by selection rather than manipulation. Whereas it is possible to randomly assign
participants to conditions in a true experiment, in a quasi-experiment it is only possible to select
participants for the different conditions from naturally occurring groups
types of quasi experimental groups
-nonequivalent groups design
-pretest posttest design
-combination designs
-person by treatment design
-nonequivalent groups design
-a basic between-subjects design in which participants have
not been randomly assigned to conditions.
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-pretest posttest design
-the dependent variable is measured once before the treatment is implemented and once after it is implemented.
combination designs
-combines elements of both nonequivalent and pretest postest design
-There is a treatment group that is given a pretest, receives a treatment, and then is given a posttest. However, at the same time, there is some sort of comparison group that is given a pretest and subsequently a posttest
-person by treatment design
-includes a manipulated independent variable (e.g., treatment), but also a nonmanipulated independent
variable (e.g., participant characteristic). Although the manipulated variable involves randomly assigning
participants to conditions, the nonmanipulated variable does not. Because of this, the person by treatment
design is technically categorized as a type of quasi-experimental design
single case research
- type of experimental design that involves studying in detail the
behavior of each of a small number of participants.
-usually between 2 and 10 participants
-most basic single case design is the reversal design
reversal design
-Reversal (A-B-A-B) Design. The hallmark of this design is conducting an initial baseline phase (A) and then introducing (B), removing (A), and reimplementing intervention (B). Control is revealed when behavior “reverses” during phase changes
within subjects
-each participant is tested under all conditions
-controlling extraneous participant variables, which generally reduces noise in the data and makes it easier to detect a
relationship between the independent and dependent variables
between subjects
-each participant is tested in only one condition
-conceptually simpler and requiring less testing time per participant. They also avoid carryover effects without the need for counterbalancing
matched subjects
match a subject in each group based off of similarities. ex. person with same IQ in group A and B
factorial design
-approach to including multiple independent variables
-each level of one independent variable is combined with each level of the other(s) to produce all possible combinations
main effects in factorial designs
-the impact that one independent variable has on the dependent variable—averaging across the levels of the other independent variable
-independent of eachother
interactions in factorial designs
-when the effect of one independent variable depends on the level of
another
-ex. subjects that receive psychotherapy and were more motivated to change have greater effects than those who aren’t as motivated
simple effects in factorial designs
provides a means for researchers to break down interactions by examining the effect of each independent variable at each level of the other independent variable.
3 levels APA
There is the organization of a research article, the high-level style that includes writing in a formal and straightforward way, and the low-level style that consists of many specific rules of grammar, spelling, formatting of references
apa citing journal format
Author, A., Author, B. B., & Author, C. (Year of publication). Title of article. Title of Journal, xx, pp–pp
ANOVA test
-more than 2 group means to be compared
-multiple group comparisons
type 1 error
reject the null hypothesis and it was actually true
type 2 error
fail to reject the null hypothesis and it was actually false
p-value
-probability value
-how likely your data could have occurred under the null hypothesis
alpha level
-significance level
- probability of making the wrong decision when the null hypothesis is true
ttest
comparison of one group and another
