# Unit 4 and Final Study Guide

- StudyBlue
- Nebraska
- Creighton University
- Psychology
- Psychology 315
- Skovran
- Unit 4 and Final Study Guide

**Created:**2011-12-12

**Last Modified:**2011-12-13

*at least one*of the groups is different from the others

**quantitative variable**, from each participant, who is in

**all**of the conditions if the qualitative variable

- You are investigating
__differences over time__of the same group

- Both a t-test and a repeated measures ANOVA can be used when collecting data from a
**group of participants that were in two conditions**

- A
**t-test is only for 2 conditions**, while an**ANOVA is for 2 or more conditions/ levels**

- Both a repeated measures ANOVA and a one-way ANOVA can be used
**in a study with 2 or more conditions/ levels**

- A
**RM ANOVA is for participants in all conditions** - A
**one-way ANOVA is for participants in only one of the conditions**

- Perform the Omnibus
*F*test - Compute all Pairwise comparisons - if your
*F*test was significant - Reject the null: mean differences > minimum mean
- Check that the significant mean difference is in the hypothesized condition
- SPSS computes the math for you

- Significant mean difference or not
- APA Results Ex.:
*F*(2, 412) - 15.62,*p*= .00 - Information revealed brought the LSD comparisons
- (
*M = ____, SD = _____*)

*multiple causes*and can be better studied using

*multivariate*research designs

- The DV
- One IV
- The other IV

- Interaction
- The main effect of one IV
- The main effect of another IV

__between simple effects__

__between marginal means__

- Cell means
- Marginal means
- Main effects
- Simple effects
- Interaction

__with a particular combination of IV treatments__

__in a particular condition of the specified IV__

__comparison of cell means__

**The main effect may be misleading**

*have an interaction*:

- = vs. <
- < vs. >
- < vs.
_{<}

*no interaction:*

- < vs. <
- = vs. =

- One null simple effect and one simple effect

< vs. >

- Simple effects in opposite directions

- Involves
**a single IV** - Tells how one IV is expected to relate to the DV ignoring the other IV

- Involves
**both IVs** - Tells the expected differences in how one IV relates to the DV for different conditions of the other IV

- Telling the expected pattern of this RH, this involves a single IV
- This suggests that the effects of that IV are the same for all levels of the other IV

- While telling the expected pattern of this RH, it is essentially the same as describing an interaction
- This suggests that important differences between the effects of that IV are at different levels of the other IV

- Type of research hypothesis that can always be tested
- States whether or not there is a statistical relationship between one IV and the DV

- Requires that the conditions of that IV were randomly assigned, properly manipulated and there was good experimental control
- States that the DV value is a direct result of the value of that IV

- States whether or not there is a statistical relationship between the combination of the IVS and the DV

- States that the DV value is a direct result of the value of the combination of IV conditions

- Tell the IVs and the DV
- Present data in table
- Determine if the interaction is significant
- Determine whether the first main effect is significant
- Determine whether the second main effect is significant

*F*tests

*relationship of each IV to the DV*

*marginal means are significantly different*

- Check and make sure if two marginal means are significantly different that they are different in the
*expedited direction*

*F*tests

*statistically significant*interaction, but

*not where*it is significantly significant

- Does not tell us the pattern and which simple effects (cell means) are different from each other
- Does not tell us
*how much difference is necessary*to conclude that they are significantly different

*how large of a cell mean is required*to treat it as a

*statistically significant mean difference*

- # of conditions
- 2x2 is always going to have
**4 conditions** - df
_{error} **N - 4**- MS
_{error} - Look on the spss printout
- n
- N / 4

*F*tests

- Between-groups factorial design
- Within-groups factorial design
- Mixed factorial design

- Each IV uses a
*between-groups comparison* - Each participant complete
*only one condition*of the design

- Each IV uses a
*within-groups comparison* - Each participant completes
*all conditions*of the design

- One IV uses a between-groups comparison
- The other IV uses a within-groups comparison
- Each participants completes
*both conditions*of the within-groups IV,*but*completes*only one condition*of the between-groups IV

- Three parts of a story

A main effect is the difference between marginal means

- Simple effects are only relevant
*when referring to an interaction*

- Enter cells means & marginal means
- Main effect of one IV?
*F*test sig.- Descrip. or mislead.?
- Main effect of the other IV?
*F*test sig.- Descrip. or mislead.?
- Significant interaction?
*F*test sig.- Calculate LSDmmd
- Enter < = > for SE

*F*(1, 28) - 21.19, P < .05. Further analyses from LSD follow ups of the cell means (min. mean diff. = ) revealed ...

*F*(1, 28) - 31.62, p < .05. As hypothesized, ... (if this is qualified by simple effects state that here)

Contrary to the RH, there was not main effect for the other IV,

*F*(1, 28) = 6.54, p = .16

- Both main effects must be causally interpretable to have a causally interpretable interaction
- Can we causally interpret the results?
- Main effect of one IV?
- Main effect of the other IV?
- Interaction effect?

**direction and strength of a linear relationship**between

*two quantitative*variables

^{2})

**relationship**(pattern) between

*two qualitative*variables

**contingency table**

*frequencies or counts*, and not the means of those in each cell

- No relationship
- Linear
- Non-linear

- Positive
- Negative

- Strong
- Moderate
- Weak

- Variables
- Direction of the expected linear relationship
- Population of interest
- In generic form
- There is a positive or negative relationship between X and Y in the population represented by the sample

- Variables
- No linear relationship is expected
- Population of interest
- Generic form
- There is
**no linear relationship**between X and Y in the population represented by the sample

**is not strong enough**to conclude there is a linear relationship between the population represented by the sample

**is strong enough**to conclude that there is a linear relationship between them in the population represented by the sample

- Variables
- Specific pattern of the expedited relationship
- Population of interest
- Generic form
- There
**is a pattern of relationship**between X and Y in the population represented by the sample

- Variables
- No pattern of relationship is expedited
- Population of interest
- Generic form
- There
**is not pattern of relationship**between X and Y in the population represented by the sample

**is not strong enough**to conclude there is a relationship between them in the population represented by the sample

**is strong enough**to conclude there is a relationship between them in the population represented by the sample

*by the participants*

- Usually can't be because it is normally a natural groups design...
- No random assignment
- No manipulation of IV
- No procedural control

- Everything we have covered so far is parametric statistics

- Chi square is non-parametric
- Used with
**categorical**data - Considered distribution free tests
- Not robust
- Can be
*misleading*but gives us*something*to do with nominal/ ordinal data

*X*(1) = 8.44, p < .05.

^{2}- 0.00 absolutely - no pattern of relationship
- "smaller" X
^{2 }- weaker pattern of relationship - "larger" X
^{2}- stronger pattern of relationship

# of levels of IVs: 1

Types of IVs: qualitative

Independent or related: independent

# of DVs: 1

Types of DV: quantitative

Comparison being made: whether or not that sample likely belongs to the population

# of levels of IVs: 2

Types of IVs: qualitative

Independent or related: independent

# of DVs: 1

Types of DV: 1

Comparison being made: whether 2 samples likely belong to the same population - i.e. 2 samples are significantly different

# of levels of IVs:

Types of IVs:

Independent or related:

# of DVs:

Types of DV:

Comparison being made:

# of levels of IVs: 2+

Types of IVs: qualitative

Independent or related: independent

# of DVs: 1

Types of DV: quantitative

Comparison being made: whether 2+ samples belong to the same population - i.e. 2+ samples significantly differ

# of levels of IVs: 2+

Types of IVs: qualitative

Independent or related: relation

# of DVs: 1

Types of DV: quantiative

Comparison being made: difference scores of 1 sample over 2+ measurements (levels of the IV)

# of levels of IVs: 2+

Types of IVs: qualitative

Independent or related: independent and/ or related

# of DVs: 1

Types of DV: quantitative

Comparison being made: testing for the combined effect of 2+ IVs on the DV

# of levels of IVs: 1

Types of IVs: quantitative

Independent or related: independent and/ or related

# of DVs: -

Types of DV: -

Comparison being made: whether two quantitative variables are linearly related - does not prove causation

# of levels of IVs: 1

Types of IVs: qualitative

Independent or related: independent and/ or related

# of DVs: -

Types of DV: -

Comparison being made: whether there is a pattern between the frequency counts of 2 qualitative variables

**a behavior exists**, can be measures, and distinguished from similar other behavior

Ex. Flying saucers have been seen in our skies

- Knowing the amount or kind of one behavior helps you
**predict**the amount or kind of another behavior

*is related*to how many hours you work each week

Ex. IQ is a better predictor of graduate school performance than college GPA

- Not always feasible
- First step of the sampling procedures
- Target population: defining people/ animals we want to study

- Second stage of selection/ sampling

Ex. Students enlisted with the registrar

- Third stage of selection/ sampling

Ex. 100 students from the registrar's list

- Fourth stage of selection/ sampling

- May be qualitative (in most cases) or quantitative (r)

- Changing a physical aspect of stimuli or changing the meaning of stimuli
- Manipulate the environment
- Change attributes of the stimuli or task

*expected to change*as a result of the manipulation of the IV

- The one in which scores are presumably caused or influences by the iV
- Also called the
**dependent measure**

- Employ a more precise operational definition of each component
- Eliminate the extraneous variable
- Keep the extraneous variable constant
- Balance out the extraneous variable

**take control of the potential confound**so that they become controls and

*not confounds*

- ...if we are going to causally interpret our results

*causal research hypothesis*

**Random assignment**of participants*before*IV manipulation- by experimenter
**Treatment/ manipulation**of IV by the*experimenter*- Provides temporal precedence
**Good control**of procedure during task completion

- Includes both quasi-experiment and natural groups design

- No random assignment of individuals
- No treatment/ manipulation of the IV performed by the researcher
- Poor/ no control over procedural variable during task

May have one or two of these, but not all

- No random assignment of individuals
- No treatment manipulation by the researcher
- No procedural control over variables during task

- Also called
*between subjects*experiment - Each participant is only in
**one of the conditions** - Typically used to study "differences"

- Has a ‘control’ or ‘comparison group that helps control for extraneous variables
- Can randomly assign participants to groups or conditions
- Can be used when other designs cannot
- Ex. Married vs. not married

- Cannot always assign individuals to groups or conditions
- SES, gender, personality, etc.
- Need more participants

- Also called
*within subjects, repeated measure, or longitudinal* - Participants complete
**all conditions**of the experiment - Typically used to study "changes" within a participant

- Takes fewer participants
- Participants serve as their own control group
- More powerful statistical tests available

- Attrition
- Practice/ carryover effects
- Solution - counterbalancing

- Self report
- Naturalistic observation
- Undisguised participant observation
- Disguised participant observation

- Mail questionnaire
- Group administered interview
- Computer administered interview
- Personal interview
- Phone interview

- Requires "camouflage" or distance
- Researchers can be very creative

- The researcher is in plain view
- Participant is likely to know they are collecting data

- Laboratory setting
- Structured setting
- Field setting

- An attempt to blend the best attributes of field (external) and laboratory (internal)

- Helps external validity, but can make control (internal validity) more difficult
- Random assignment and manipulation are possible with some creativity

- A "false alarm"
- Reject the null when you should have retained it
- p = alpha

- A "miss"
- Retained the null when you should have rejected it
- p = 1 - alpha
- Power

- A "mus-specification"
- The results show that there is a significant relationship, but in the opposite direction than originally hypothesized

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