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Social Desirability
How to prevent this in the future.
A socially desirable response which has nothing to do with the question.
Prevention: Control privacy & confidentiality

Simplicity wording
Avoid jargon terms and technical terms; define complex terms before asking about them.

Negative wording
 Avoid negative wording.
 Ex: Do u think that they should not.....


Questionnaires & Interviews are part of _________ research.
Pros & Cons
 Surveys Pros: Easy, cheap, fast, Lg groups
 Survey Cons: Hard 2 identify age/gender & poor response rate.

Double barreled Question
 Two questions with one response.
 Ex: should you forgive a friends gay rights or life choices?

Loaded Question
A question that might have strong wording and forces people to answer in only one way.
Ex: do you beat ur kids?

Closed Ended Question
 Fixed number of responses
 Rating scales are often used 57 points
 (strongly agree to strongly disagree)

Open Ended Questions
Free response

Interviews can be done.......
Pros & Cons
face to face, on telephone, or focus groups.
 Pros: Unexpected / Unthinkable answers
 Cons: Lose anonymity & Interviewer Bias

Questionnaires:
Pros / Cons
Pros: Can mail, use on internet, not very costly, respondents = anonymous.

It is often impossible to include all of the population that's why a ________ is used to represent it.
sample

How to measure confidence interval.

What is probability sampling?
All population is included and it is rare & hard to conduct research this way.
Ex; Simple random sampling: in this way every member of the population has an equal chance to be selected.
Often times hard to include everyone so that's why non probability sampling is more commonly used.

Even if random sampling is used, what can be a two ways for bias to occur?
 The sampling frame used (ex white pages)
 Poor response rates.

Non probability sampling:
Sampling in which one cannot specify the probability that any member of the population will be included in the sample.

Internal Validity:
Certainty that IV manipulation caused the changes in the DV. AND NOT confounding variables

External Validity:
Ability 2 generalize results

What is confidence interval?
range that tells you how accurate you think you are.

Confounding
variable =
a variable that is not controlled in a research investigation.
 e.g.,
 If I had married couples complete a difficult puzzle in either a hot room (experimental cond.) or a normal temperature room (control cond.), but did not
 control the size of the room,
 confounding may occur

Basic Simple Experimental design
2 Variables ( IV & DV)
& IV only has two levels

Posttest  Only Design =
_{a true experimental design in which the DV (posttest) is measured only once, after manipulation of the IV.}
 _{e.g.,}
 _{an experimental group that receives the treatment and a control group that does not}
 _{ e.g., use two different amounts of the IV}
 _{or}

PretestPosttest Design =
Disadvantage?
a true experimental design in which the DV is measured both before (pretest) and after (posttest) the manipulation of the IV.
Disadvantage:
*Disadvantages: time consuming & pretests can sensitize participants to what you are studying

Solomon fourgroup design
combines both the posttestonly and the pretest posttest design. (This lets you assess the impact of the pretest.)

Three ways of assigning participants to experimental conditions:
 ^{Independent Groups Design (AKA between subjects deign)}
 ^{Repeated Measures Design (AKA within subjects design)}
 ^{Matched Pairs Design (match participants 1st then randomly assign)}

^{Independent Groups Design (AKA between subjects deign)
Repeated Measures Design (AKA within subjects design)
Matched Pairs Design (match participants 1st then randomly assign)}
 ^{Independent Groups Design (AKA between subjects deign)}
 Researchers randomly assign participants to ONE level of IV
 Repeated Measures Design (AKA within subjects design)
 Researchers assign participants to ALL levels of the IV.
 ADV: Fewer research participants needed because running twice.
 DISadv: Practice effect, fatigue effect, or carryover effect
 Matched Pairs Design (match participants 1st then randomly assign)
 Ex: B4 experiment give IQ test  List in order  pair accordingly with one in exp group and one in control group.

To deal with problems, researchers can employ ____________ ___________ or increase the time interval between treatments.
Counterbalancing techniques

Setting the stage involves:
 Informed consent
 Explain why experiment is being conducted.
 Must introduce IV & DV within context of the experimental setting.

Straightforward manipulation:
Staged Manipulations:
 Straightforward manipulation:
 ^{Variables are manipulated with instructions & stimulus presentations}
 _{(Most manipulations of IVS in research are like this)}
 Staged Manipulations:
 ^{necessary to stage events in order to manipulate the IV successfully}

 *They often employ a confederate

Best way to get the strength of the manipulation?
Past research while keeping ethics in mind.

_{DV in most experiments is one of three general types:}
 Self Report Measures
 Behavioral Measures
 Physiological Measures

1) SelfReport Measures
Advantages/Disadvantages
^{Ppl provide info about their own attitudes, beliefs, and behaviors}
_{Advantages: you get info it straight from }_{them.}
^{Disadvantages: They may lie, social }^{desirability (make themselves appear in + light)}

Behavioral Measures
Advantages / Disadvantages
measures in which direct observation of behavior occur
Advantages:
 Disadvantages: May not always catch
 everything. Need to use IOA reliabilty.

3) Physiological Measures
_{ … measures of people’s body responses (e.g.,}_{heart rate, temperature, muscle tension)}
Advantages:
Disadvantages:

*The DV should be __________
enough to detect differences between
groups
Sensitive
 Ex:
 Men's measurement of how attractive they view their wife. Watch 30 min pornography,
 and control group does nothing, then ask the men after video if wife is
 attractive.
Is this sensitive enough? No!

Ceiling Effect =
 Ceiling effect = failure of a measure to detect a
 difference because it was too easy

Floor Effect =
failure of a measure to detect a difference because it was too hard
So in depth that there is no correlation

Demand Characteristics =
How can we eliminate demand characteristics?
 cues
 that inform the subject how he or she is expected to behave
 Ex: asking them 50x after taking a
 caffeine pill…… if they are nervous.
 We want to eliminate demand
 characteristics, you can use deception to control for this, use filler
 questions, use a control group a group that doesn't get the treatment.

Expectancy effects =
any intentional or unintentional influence that the experimenter exerts on subjects to confirm the hypothesis under investigation

Blind & Double Blind Study
Blind Study: Participants do not know which group they are in
Double Blind Study: The experimenter also do not know which group participants are in.

To control for expectancy effects, researchers can:
 
 train extensively & practice behaving consistently
  run all conditions simultaneously or
 make the procedures automated
  make the experiment double blind (hire
 uninformed people)

Pilot studies:
Purpose?
SiZe?
 You
 run your study doing a trial run with a smaller group.
 Point
 is to see if there is any flaws in the study and then can fix them before runing
 actual experiment.
 Ask
 questions after trial run to see if they knew what your hypothesis was….. If
 so…. U messed up somewhere.

What are factorial designs?
Designs with more than one IV

5x5 factorial design
How many:
Groups
Levels
IVS
Main Effects
 Groups: 25 groups
 Levels: 10 Levels
 IVS: 2 IVS
 Main Effects: 5 Main effects

Factorial design yields two things:
 Simple main effects: effects of 1 IV at a particular level of another IV.
 Main effects = Effects of IV by itself.5IV = 5main efects


Factorial designs yield two kinds of information
 The main effect of an IV
 Whether there is an interaction between variables

You run a nova to if the information is ______ ________.
This measures if it was just a fluke or is it significant.
statistically significant

What experimental design is run to see the effect on ONE participant.
Also has no control group.
Reversal Design: Treatment is introduced after a baseline (control) period. ABA
 A = Baseline B= Treatment
 Baseline acts as control group / Comparison
Single case design is when there is no control group present.

Multiple Baseline design
Why might this be used?
Observing behavior before & after a manipulation under multiple circumstances.
Used in certain situations, it may be unethical to remove a treatment in order to test its effect
Ex: instead of using 1 person to test drug. Use 3 people to see if yield same results. Gets rid of extraneous variables and flukes.
 P1 Drug Day: 10
 P2 Drug Day: 20
 P3 Drug Day: 30

Are Quasi experiments true experiments?
What are quasi Experiments & why are they not true experiments?
No
Quasiexperimental designs allow us to study the effect of an IV on a DV, but lack the control features of true experimental designs

OneGroup Post testOnly Design
^{OneGroup PosttestOnly Design (Partici > }^{IV > DV)}
 Ex:
 park bench, ur accomplice sits next to stranger and you time how long until they get up &
 leave.
 ^Problem:
Problems? … no control group to aid in interpreting the results.

What do you need for an experiment to be a true experiment?
 Comparison group
 &
 Random assignment!

OneGroup PretestPosttest Design^{
(participants > DV > IV (treatment) > DV)}
Problem:
Ex?
 ^{Problem:}
 ^{no control group to rule out history effects, maturation effects, instrument decay,}^{etc.}
 ^{EX:}
 ^{Program}: Trying to help ppl quit smoking
 ^{Pretest measures how much they smoke (self report)}
 ^{Treatment Given (program)}
 ^{Post test measures again. (self report)}

History Effects:
Ex:
 any event that occurs between 1st
 & 2nd measure & Is not part of the manipulation
 If Obama diagnosed with stage 4 terminal lung cancer between measures it may
 effect data.

Maturation Effect:
Ex:
Maturation Effect: Change due to time and not the independent variable.
 Ex:
 Jealousy (longitudinal study) comparing year 1 to year 10. IT can be naturally occurring.

Instrumental Decay:
Your measurement doesn't measure accurately over time.

Nonequivalent control group design
Problem?
Ex:
Like a true experiment ( has control group) BUT no random assignment is used.
Problem: selection differences may affect the results.
 Ex:
 smoking ex: Get 100 volunteers à
 creates bias > two groups but not = groups

Nonequivalent Control Group PretestPosttest Design
How is it similar to true experiment?
*This is one of the most useful quasiexperimental designs.
NO RANDOM ASSIGNMENT but... pretest scores act as a comparison group. Can compare with post test.
 *No
 control group but because of pretest you do have a comparison group.

Crosssectional design:
Usage?
Pros:
Problem?
crosssectional designs, compare people at different stages or ages in a developmental process)
 Pros: yields immediate results
 Problem: May cause cohort effects & confound with age differences.

Longitudinal Designs
Pros:
Cons:
^{With longitudinal designs, participants of the same age are followed with repeated measurements over a period of time.}
 Pros: yields valuable info
 Cons: expensive, many years, & ppl dropping out of study (participant attriation)

participant attrition
Participants dropping out of the study

Retrospective designs
Problem:^{}
^{This design relies on participants recall of past events.}
Peoples memories can be inaccurate (bias, forgetting)

Sequential Designs
_{Cross sectional & longitudinal methods are combined}.
 Compare two sections of population & then when they are older they are measured again & again & again and see how they
 change over time.

When analyzing results when is it best to compare group percentages?
Ex:
If I’m studying whether males and females differ in their interest in sex, and ask males and females whether they like or dislike (yes or no) sex (a nominal
variable), I can compare group percentages
When studying NOMINAL VARIABLES.

When is it useful to correlate individual scores?
Ex:
when individuals are measured on 2 variables that have a range of numerical values
ex:
 e.g.,
 If I’m studying location in classroom
 and grades in the class, I can determine
 if there’s a relationship between the two
 variables.
 or
 Ex: height & income level

When is it useful to compare group means?
Ex:
Useful when studying participants in 2 or more groups.
Ex:
 If I’m studying the effect of exposure to
 violent video games, I can compare the average # of aggressive acts demonstrated in my experimental and control conditions after the manipulation

What is frequency distribution?
Ex:
Frequency Distribution = an arrangement of a set of scores that indicates the number of times each score was obtained
Ex: grades (ABCDF) It will show us how many of each was earned in the class can be charted using graphs, pie charts, and more.

What are descriptive statistics?
 Descriptive Statistics = statistical measures that describe the results of a study; descriptive
 statistics include measures of central tendency(mean, median, mode), variability, and correlation.

Central Tendency Statistic:
Tells us?
What are they?
Central Tendency Statistic tells us what the sample as a whole is like.
Mean, Median, Mode.

Variability
What is it?
What are the 2 kinds of variability?
Is a number that characterizes the amount of spread in a distribution of scores.
 _{2 kinds:}
 Standard Deviation (avg dev. from mean) & Range(Highest  lowest)

The SD is small when....
most ppl have similar scores close to the mean.

SD is large when most ppl......
have scores that lie further from the mean.

Pearson productmoment correlation
coefficient
Represented by?
Tells us?
Can range from?
How to read the scale.
 Represented by: r
 ^{Tells us}: how strongly 2 variables r related & direction of relationship.
Can range from: 0  +1 & 0  1
_{ A correlation of 0.00 means that the two variables are not related}
 ^{When the correlation coefficient is positive, that means that there is a positive linear relationship}
 ^{ When the correlation coefficient is}
 ^{negative, that means that there is a negative linear relationship}


How to determine (from number) the strength of Pearson correlation coefficient.
_{r = +.70 or Very strong positive relationship}
_{r = +.40  +.69 Strong Positive Relationship}
_{r = +.30 }_{ +.39 Moderate }_{positive Relationship}
_{r = +.20 }_{ +.29 Weak Positive Relationship}
_{r = +.01 }_{ +.19 No / Little Relationship}

What are inferential statistics?
Ex of inferential statistics
 statistics designed to determine whether results based on sample data are generalizable
 to a population.
Ex: ttest & F tests

Statistical inference begins with.......
Two goals of an experiment?
 ^{statement of the null hypothesis and a research hypothesis.}
 
 The Null Hypothesis states that the observed difference in group means is caused by chance, and not the IV (that the IV had no effect)
 The Research Hypothesis states that the observed difference in group means is due to the IV (that the IV did have an effect)
*The goal of an experiment is to be able to reject the null and accept the research hypothesis!

When is a result considered statistically significant?
What is it for psych?
What is alpha level?
If there is a low probability that the difference between the obtained sample means was due to random error.
 Psych .05
 Alpha level: probability required for significance.

What are t tests and F tests
When are they used?
What do they determine?
t test = a statistical significance test used to compare differences between 2 group means
 … the t value is a ratio of two aspects of the
 data, the difference between the group
 means & the variability within groups
 Will tell u if difference is statistically difference or not.
 F test (AKA analysis of variance or ANOVA) =
 a statistical significance test for determining whether two or more means
 are
 significantly different
… F is the ratio of systematic variance to error variance

What are Type I and Type II errors?
Type I error = an incorrect decision to reject the null hypothesis when it is true
 The wrong decision of rejecting the null.
 (ex: u are saying drug works when in reality it doesn't
Determined by Alpha Level (.05)
 *The probability of making a Type I error is
 determined by the alpha level chosen
 e.g., If the alpha level chosen is .05 and the
 null hypothesis is rejected,
 there are 5 chances out of 100 that the
 decision is wrong
Type II error = an incorrect decision to accept the null hypothesis when it is false
 (ex: that the drug did not work but the
 drug actually did work)
 *Making a Type II error is related to 3
 factors:
 
 alpha level ( if alpha level is set too low you
 become more likely to make type II eror)
  sample size (if
 the sample size is too low )
  effect size (strength
 of relation ship between variables – Ex: half pill – future research full pill)
 Type II error is worse because if a drug does actually work and we indicate it does
 not work it wont get published.

Non significant results can occur for a number of reasons:
Non significant results do not necessarily indicate that the null hypothesis is correct.
 1) A true relationship between the variables
 does not exist
2) A true relationship exists, but wasn’t detected because:
  the study was poorly designed
 (incomprehensible instructions, a weak manipulation of the IV, a dependent measure that is unreliable)
 the alpha level was set too low
 the sample size was too small
  the relationship is weak so the effect size
 (difference between groups) is too small

Convenience Sampling:
Representative samples:
******************************
An incorrect representation of the population affects the external validity.

Volunteer & Gender & Locale Bias
 *Volunteers:
 are more highly educated, wealthier, more in need of approval, and more social than nonvolunteers
 *Males and females sometimes respond
 differently too; therefore, it’s a good idea to include a proportionate ratio of males to females
*Locale bias can affect external validity too
 There should be cultural considerations as
 well

Two types of Replication
 1) Exact Replication – replication of research
 using the precise procedures of a study to see whether the same results are obtained
 2) Conceptual Replication –
 replication of research using different procedures for manipulating or measuring the
 variables

Possible reasons for repeated failures:
^{the original results reflect a Type I error}
^{the original researcher misrepresented the }^{results}
 ^{the original results could only be obtained}
 ^{under certain limited circumstances}
 ^{ the characteristics of the original}
 ^{experimenter biased the results}

