Research Methods 2 Midterm

variance

variance

much each score in a given set of responses varies on average from the mean

standard variance

the square root of variance;  a measure of how spread out the data is in relation to the mean

Steps to find variance

Step 1: calculate mean

Step 2: subtract mean from each score deviation Step 3: square each deviation score

Step 4: add the squared deviation scores

Step 5: divide by degrees pf freedom: N – 1

total variance = _______+__________

systematic + error

types of error variance

Phenomena are multiply determined, Individual differences, Transient states, Environmental factors, Differential treatment, Measurement error

a good experiment (that can predict causality) can:

Vary (manipulate) at least one independent variable (IV)

Control extraneous variables (eliminate confounds!)

Have the power to assign participants to conditions

Systematic variance

portion of total variance that reflects differences among the experimental groups

Treatment variance

portion of systematic variance due to the IV

Confound variance

portion of systematic variance due to error variance that differs systematically between conditions

types of experiment manipulation

Environmental manipulations, Instructional manipulations, Invasive manipulations, Real world interventions

Environmental manipulations

Vary participants’ physical or social environment

Instructional manipulations

Vary participants’ verbal instructions

Invasive manipulations

Create physical changes through surgery or drugs

Real world interventions

Programs, policy changes, practice changes

Control condition

Participants receive zero level of IV

Experimental condition

Participants receive a nonzero level of the IV

Between subjects (between-groups) design

Each participant participates in only one condition in the experiment

Within subjects (repeated measures) design

Each participant participates in every condition in the experiment

Random assignment is essential to experiments because…?

Biased assignment to conditions

Miscellaneous design confounds

Problems with the design, History effects, Pre-test sensitization, Differential attrition, Maturation

order effects

Effects of an experimental condition are contaminated by the order they occurred in

ex: Practice effects, Fatigue effects, Sensitization effects, Carry-over effects

Can fix this through conterbalancing

counterbalancing

presenting the conditions in different orders to different participants

researcher’s dilemma

as internal validity increases, external validity decreases

Inferential statistics

Can estimate how much the means would differ just by chance if the IV has no effect

statistically significant

it exceeds our estimate of how much the means would differ due to chance

t-test

used to test difference in means between 2 conditions

ANOVA

variance of analysis; used to test differences in means across multiple conditions

If T ______ critical value (p < .001) Its unlikely to be due to chance. This means that we ____ the null hypothesis

exceeds; reject

Problem Definition

What is psychology’s central bias when it comes to researching social problems? What is the researcher’s approach?

Person-centered problem definition

focuses on the individual rather than the whole problem; can often person-blame

Philosophy of science

the conceptual roots or fundamental beliefs of a researcher on how to properly answer their research question

ontology

Concerns the nature of reality and being; What is the form and nature of reality and what can be known about that reality

epistemology

Concerned with the relationship between the “knower” (participant) and “would be knower” (researcher)'; Are the participant and researcher and topic independent of each other? and Can the participant and topic be studied without bias?

axiology

Concerns the role of researcher values in the scientific process; Is there a place for values in research?

rhetorical structure

Concerns the role of researcher values in the scientific process; Is there a place for values in research?

Methodology

Refers to the processes and procedures of research; What kinds of designs are preferred? Ones that resemble the “hard” sciences or more naturalistic ones?

philosophy of science

Quantitative approaches to operationalization

Observational approaches, Archival data Physiological / neuroscientific approaches, Standardized assessments, and Self-report questions and scales

validity

is this measure an accurate representation of our construct? Is it measuring what we think it is?

reliability

How consistently does it represent our results over time?

multiple item scale

Consists of a set of items all thought to measure the underlying dimension in slightly different ways; average or sum across multiple items better captures the full dimensionality of the construct

Inter-item reliability

the degree of consistency among items thought to make up a scale; Commonly assessed by Cronbach’s alpha (α)

factor analysis

Statistical method that looks at the correlations among a set of items and determines how many constructs could most simply account for the patterns seen

Exploratory factor analysis

when you don’t have any expectations about how many factors there are or what items might represent which factor

Confirmatory factor analysis

when you expect from theory or research that items will represent constructs in a specific way

Orthogonal rotation

used in factor analysis when factors are uncorrelated

Oblique rotation

used in factor analysis when factors are correlated

higher eigenvalues correspond to _____ variance explained by each factor. A “good” eigenvalue should be ___ or higher

more; 1.0

In a factor analysis you should keep items above the ______ on a scree plot

elbow

Factor structure matrix

an output used to interpret the nature of underlying factors in a factor analysis

Factor loadings

the correlations between each item and the factor on a factor structure matrix

For a “high loading” item, it should be at/above _____ to keep the item on the construct.

0.4

One-way design

Only one IV (factor) is manipulated and there are 2 + levels

ANOVA

Test whether means from 3 or more conditions are statistically different from each other

One-way ANOVA

Use for 1 IV with 3+ conditions

Two-way ANOVA

has 2 IVs, each with 2+ conditions

To use an ANOVA, you must….

the DV must be continuous (interval or ratio) and normally distributed

If, and only if, the F-test is significant, then you determine which means are different from each other with ____________

post-hoc comparisons

F-test

finds whether the means from more than 2 conditions (groups) are significantly different, using a single test; divides the average variance between groups by the average variance within groups

Factorial Experimental Design

More than one (IV) (factor) is manipulated and each IV can have 2 or more levels (e.g. conditions)

interaction effects

the effect of one IV differs across the levels of other IVs

Randomized groups factorial design

Participants are randomly assigned to each of the possible combinations of the factors

Within subjects (repeated measures) factorial design

Each participant participates in every condition

Mixed factorial design

One factor is between groups randomly assigned, and the other factor is within-subjects

Independent variable

the variable(s) manipulated in a study

Subject variable

a personal characteristic of participants

There is no interaction in a design if the lines on a graph are ______

parallel

There is no interaction in a design if the bars on a graph __________

show the same pattern in each condition

Factorial nomenclature examples

•A “2 x 2 factorial” (read “2-by-2”) is a design with two independent variables (factors), eachwith two levels

A “3 x 3 factorial” has two independent variables, and each IV has three levels

A “2 x 2 x 4 factorial” has three independent variables, the first and second IVs have two levels, the third IV has four levels

Number of numbers = number of factors Value of each number = number of levels in each factor

Analyzing factorial design

In a factorial ANOVA, the average variance between groups (SSbg) is broken into components representing the main effects and interactions

Each main effect and interaction are compared to the same estimate of error, MSwg

Each main effect and interaction gets its own F-ratio

Median-split procedure

divide participants into groups based on 50th percentile (to create high vs. low motivation groups)

Extreme groups procedure

pretest a large number of potential participants and then select those with extreme scores (top and bottom 25%) for the study

quasi-experimental design

a study which it’s goal is also to see if one variable is causing change in another, but can not be as sure about the cause-and-effect relationship

Weaknesses in quasi-experimental designs

Less or no control over the independent variable

No control over participant assignment to conditions (no random assignment)

More difficult to eliminate confounds

Reasons to use quasi-experimental design

Answer real-world questions

Policy and legislation

External validity of a laboratory finding

Intervention in a natural setting was effective

When random assignment not practical or ethical

More cost-efficient

one group pretest-posttest design

Tests a group before and after an intervention

Because there are so many threats to validity, it is often rejected as a quasiexperimental design and considered a “preexperimental” or correlational design

Nonequivalent control group designs

Researcher includes one or more groups of participants who are similar to the group that receives the intervention, but do not get it

Nonequivalent control group posttest only

a nonequivalent control groups design that only records results after an intervention

can have selection bias and Local history effect

Nonequivalent control group pretestposttest

a nonequivalent control groups design that records results before and after an intervention

Can have local history effect