RMP Exam 2

  • Experiment

    •  a study where there are only independent variables

  • Conditions/levels

    • the groups formed by the independent variables manipulation

  • Treatment group

    • Level of independent variable that has the key active variable

  • Control group

    • The group that remains unaffected by manipulation

  • Placebo group

    • Group that remains unaffected by manipulation but they think they are

  • Constant

    • Something that does not vary in the study

  • Covariation

    • The two variables must go together

  • Temporal precedence

    • The predictor variable must come before the dependent variable 

  • Third variable criterion

    • The ability to eliminate other possible alternative explanations for the results

      • Random assignment 

      • Confounds→ a possible alternative explanation for the results but it varies with the manipulation

  • Confounds ex. Brian vs. Karen 

  • Manipulation Check

    • Designed to see how well the manipulation worked 

  • Pilot study 

    • Just tests a new manipulation to decide whether it is appropriate or not

  • Main effect

    • The effect of a predictor variable on a DV and IV ignoring other predictor variables (each predictor variable has its own main effect)

  • Interaction

    • The effect of one predictor variable on the DV which depends on the level of another predictor variable (two predictors together produce an interaction)

  • Correlation

    • a statistical analysis which tests the association between two variable (pearsons’ r)

    • THIS IS ABT STATISTICAL ANALYSIS

  • Correlational study

    • a study which all predictors are measured

    • THIS IS ABT METHODOLOGICAL APPROACH

  • Statistical significance

    • The likelihood of getting our data by chance ASSUMING there is no effect in our population

  • Effect size

    • the strength of magnitude of a relationship

    • Larger effect sizes are often but not always more important

    • In general, larger effects are more likely to be statistically significant 

  • Restriction of range→ having a limited range on one variable can make relationships look weaker than they are

  • Internal validity

    • The strength of the claim that the predictor variable (vs. something else) is causing change in the dependent variable

  • Covariation

    • Two variables must be related

  • Temporal precedence

    • The predictor variable must come before the dependent variable

  • Third variable criterion

    • The ability to eliminate other possible alternative explanations for the results

  • Matched Design/Matched Groups

    • participants who are similar to some important measure are grouped into sets and then members of each set are randomly assigned to different conditions

    • potential disadvantages are…

  • Pretest/Posttest Design

    • participants are tested on the key dependent variable twice, once before and after manipulation

    • potential disadvantages are…

  • Within-Groups

    • each participant experiences all levels of the independent variable

    • potential disadvantages are…

  • Order Effects

    • one experience or level of the IV can effect the others

  • Demand Effects

    • cues that lead participants to guess what the study’s purpose

  • Counter Balancing

    • presenting levels of IV to participants in different orders to control order effects

  • Quasi Experiment

    • one measured and one manipulated predictor variable

  • statistics

    • math procedures to interpret information

  • parameter

    • a numerical value we use to describe a population- does not vary

  • A statistic

    • a numerical value used when we describe a sample

  • sampling error

    • differences that exist from one sample to another, natural

  • descriptive statistics

    • numerical and graphical ways to analyze data including mean and mode, used to simplify and organize data

    • graphs, correlation and effect size, z-score percentile

    • central tendency

  • inferential statistics

    • uses sample info to make estimates, approx, conclusions and predictions about population

    • Null hypothesis significance testing, Type I and Type II error, Power, Types of inferential tests and when to conduct them

  • central tendency

    • descriptive statistics that provide an average, typical, or representative score that defines the center of a distribution

    • mean, median, mode

  • variability

    • Describes the distribution

    • Estimates distance we expect between scores

    • Provides a sense of how well an individual score will represent the population

    • How much error to expect if you’re using a sample to represent the population

  • variance

    • average deviation ² distance from the mean

  • standard deviation

    • average distance or deviance score from the mean

  • P<0.05

    • The data is statistically significant

  • P>0.05

    • the data is not statistically significant

  • Type I error

    • false positive

  • Type II error

    • false negative

  • Power

    • the probability that the test will correctly reject the false null

  • Inferential tests

    • chi squared

    • Correlational

    • independent t test

    • ANOVA

    • factorial ANOVA

  • replication

    • conducting the study again and getting the same results

  • replication plus extension

    • has new variables

  • conceptual replication

    • has same conceptual variables but new operational

  • meta-analysis

    • quantitative review of effect

  • floor effects

    • data is clustered at the bottom of the scale

  • ceiling effects

    • data is clustered at the top of the scale

  • noise

    • too much variability associated with predictors