Psyc 2020 #Exam2

A socially desirable response which has nothing to do with the question.

Prevention: Control privacy & confidentiality

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

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

• Surveys Pros: Easy, cheap, fast, Lg groups
• Survey Cons: Hard 2 identify age/gender & poor response rate.

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

A question that might have strong wording and forces people to answer in only one way.

Ex: do you beat ur kids?

• Fixed number of responses
• Rating scales are often used 5-7 points
• (strongly agree to strongly disagree)

Free response

face to face, on telephone, or focus groups.

• Pros: Unexpected / Unthinkable answers
• Cons: Lose anonymity & Interviewer Bias

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

sample

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.

• The sampling frame used (ex white pages)
• Poor response rates.

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

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

Ability 2 generalize results

range that tells you how accurate you think you are.

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

2 Variables ( IV & DV)

& IV only has two levels

_{^{a true experimental design in which the DV (post-test) 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

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

combines both the posttest-only and the pretest-  posttest design.  (This lets you assess the impact of the pretest.)

• ^{_{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.

Counterbalancing techniques

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

• 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

Past research while keeping ethics in mind.

• Self Report Measures
• Behavioral Measures
• Physiological Measures

<sub>^{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)}</sub>

measures in which direct observation of behavior occur

Advantages:

• Disadvantages: May not always catch
• everything. Need to use IOA reliabilty.

  _{^{… measures of people’s body responses (e.g.,heart rate, temperature, muscle tension)}}

Advantages:

Disadvantages:

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 = failure of a measure to detect a
• difference because it was too easy

failure of a measure to detect a difference because it was too hard

So in depth that there is no correlation

• 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.

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

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.

• -
• train extensively & practice behaving consistently

•   - run all conditions simultaneously or
• make the procedures   automated

•   - make the experiment double blind (hire
• uninformed people)

• 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.

Designs with more than one IV

• Groups: 25 groups
• Levels: 10 Levels
• IVS: 2 IVS
• Main Effects: 5 Main effects

• Simple main effects: effects of 1 IV at a particular level of another IV.
• Main effects = Effects of IV by itself.5IV = 5main efects

• The main effect of an IV
• Whether there is an interaction between variables

statistically significant

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.

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

No



Quasi-experimental designs allow us to study the effect of an IV on a DV, but lack the control features of true experimental designs

^{_{One-Group Posttest-Only 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.

• Comparison group
• &
• Random assignment!

• ^_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)}}

• 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: Change due to time and not the independent variable.

• Ex:
• Jealousy (longitudinal study) comparing year 1 to year 10.  IT can be naturally occurring.

Your measurement doesn't measure accurately over time.

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

• AKA
• selection differences

*This is one of the most useful quasi-experimental 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.

cross-sectional 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.

^{_{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)

Participants dropping out of the study

^{This design relies on participants recall of past events.}

Peoples memories can be inaccurate (bias, forgetting)

_{^{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 studying NOMINAL VARIABLES.

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

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

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.

• 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 the sample as a whole is like.

Mean, Median, Mode.

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)

most ppl have similar scores close to the mean.

have scores that lie further from the mean.

• 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}}

<sub><sup>—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</sup></sub>

• statistics designed to determine whether results based on sample data are generalizable
• to a population.

Ex: t-test & F tests

• ^{_{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!

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.

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

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.

• 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

******************************

An incorrect representation of the population affects the external validity.

• *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

• 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

<sup><sub>-the original results reflect a Type I error

-the original researcher misrepresented the results</sub></sup>

• ^{_{-the original results could only be obtained}}
• ^{_{under certain limited circumstances}}

•  ^{_{-the characteristics of the original}}
• ^{_{experimenter biased the results}}