Exam 1 Study Guide 3600

8/10/2019 Exam 1 Study Guide 3600

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Exam 1 Study Guide

Chapter 1

Empiricism

Idea that knowledge is based on observation. Data allows us to form conclusions

Goals of Science

Description of behavior

Careful observation of behavior

Can be done through simple observation (watching behavior), surveys, tests, etc.

Often goal is to see if one thing causes another, or one thing is related to another

Example: How does self-esteem change across the lifespan?

Prediction of behavior

Allows us to anticipate events

Example: You conduct a study, and find that students with high self-esteem get

better grades than students with low self-esteem. If a student has low self-esteem,

there is a high probability that they will do poorly in their classes.

Determining causes of behavior

Why does a behavior happen?

To change behavior, we need to know why it happens

Remember, CORRELATION DOES NOT EQUAL CAUSATION!

Determining causes of behavior

3 things required to determine causality

Temporal precedence: cause always precedes the effect

Covariation of cause and effect: when cause is present, effect occurs.

When cause isnt present, effect doesnt occur Elimination of alternative explanations: nothing other than causal variable

could be responsible for effect occurring

Explanation of behavior

Explain why event occurs

Example: College students who watch a lot of TV have lower grades, because

they spend less time studying than students who dont watch TV.

Description, prediction, determination of cause, and explanation all closely intertwined

These 4 components often need to be revised or discarded as new information comes out Behavioral science is constantly evolving and moving forward!


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Basic research Tries to answer fundamental questions about nature of behavior

Common topics: cognition, emotion, motivation, learning, neuropsychology, personality,social behavior

Example research question: Do adults over 65 have less working memory than adults

under 30?

Applied research

Conducted to address issues in which there are practical problems and potential solutions Note: basic research often informs applied research

Example research question: How can computer work stations be modified to account for

working memory deficits in older workers?

Program evaluation: assesses social reforms and innovations in education, government,health care, and other institutions

Example: Is new district program for students with learning disabilities improving

these students reading abilities?

Chapter 2

Hypothesis

Tentative idea or question waiting for evidence to support or refute it

Characteristics of Theories

1. Theories must be testable

must be able to falsify

2. Organize & Explain Knowledge

3. Generate New Knowledge

Self-perception theory (Lepper, Greene, & Nisbett, 1973)

We understand our behavior by observing it

If a person is paid very little for a dull and boring task, then they should find the task

enjoyable

4. Past Research

Every study raises new issues


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Use a different setting

Use a different participant population

Use a different methodology

Inconsistencies in results

5. Practical Problems

Should tuition be raised?

How high?

How can we help the students meet the increased cost?

All APA-style papers need to include:

A title

Authors and author information

An abstract An introduction

A method section

A results section A discussion section

References

Appendices (if applicable)

Tables (if applicable)

Figures (if applicable)

Title

Concise Gives information about the study

Good title: An examination of the effect of self-esteem on college student GPA

Bad title: Self-esteem and college students Author name

First, middle, last (no titles)

Institutional affiliation

Author note Complete departmental affiliation

Changes in affiliation (if applicable)

Acknowledgements (funding, etc.)

Special circumstances (conference presentation) Person to contact

APA style

Abstract: concise summary of article Usually between 50 and 200 words


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Should describe:

Problem under consideration

Participants Basics of study method

Basic findings

Conclusion

Introduction Presents problem under consideration

Explores importance of problem: why should reader care?

Describes relevant scholarship (previous research) State hypotheses

Methods: describes how study was conducted

Operational definition of variables

Participant characteristics Sampling procedures

Sample size and power

Measures

Research design

Experimental manipulations

Results: what did you find?

Recruitment of participants and results of this Statistics and data analysis

Additional analyses: exploratory, subgroup, etc.

Manipulation fidelity Adverse events

APA style

Discussion: evaluate results and draw conclusions

Were hypotheses supported?

Interpret results

Acknowledge limitations

Suggest areas for future research Brief overall conclusion

References

Should include all work cited in the paper Note: Within paper, literature cited parenthetically (Author, year). In the

reference section, the full citation (author, year, title, journal, volume,

page number, doi) is included

APA style guidelines are fairly strict on this: see p. 193-214 Appendices and supplemental materials: important information that would make the

paper itself too cumbersome to include within it

Scale items

Analysis code Technical formulations and proofs of statistical methods

Stimulus materials

Tables and figures: can be used to display results in an easier-to-interpret way Placed at end of manuscript for journal submission


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During editing, journal editor places them into the paper in the correct place

Tables and figures should be straightforward and easy to read

Document formatting requirements: Double-spaced

1-inch margins

Page number and header on top of each page

Chapter 3

Beneficence: need for research to maximize benefits and minimize any possible harmful

effects of participation

Informed consent provides participants with important study information so they canmake an informed decision about whether to participate (associated with autonomy

principle)

Generally includes:

Purpose of research

Procedures used, including time involved

But, dont need to tell participants everything if it would negatively affect

study: just a general idea Risks and benefits

Compensation (if any)

Confidentiality Assurance of voluntary participation and permission to withdraw

Contact information for questions

Should be written in straightforward, simple language

No jargon

8thgrade reading level usually recommended

Not written in first person

Non-English speaking participants should receive translated copy See Figure 3.1 on p. 45 for detailed checklist

Autonomy issues Some populations (children, patients in psychiatric hospitals, developmentally or

cognitively impaired adults) cant make informed decision

Usually require someone to give permission (such as parent) in addition to

participants consent Coercion: individuals may feel that they have no choice but to participate

Examples: prisoners, students, employees

Need to ensure that individuals are aware that they are not required to participate

Excessive compensation/benefits can be type of coercion Keep benefits in line with what research requires of participants

Debriefing


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Happens after completion of study

Researcher tells participant what purpose of research was

Researcher explains any deceptions that occurred, and why they occurred Researcher offers participant resources if they need to speak with someone about the

study

Justice

Historically, high-risk research conducted with marginalized groups who may not havehad power to refuse to participate

Example: Tuskegee Syphilis Study (1932-1972)

Participants: 399 poor African Americans in Alabama Not treated for syphilis in order to study long-term effects of disease

Decisions to include or exclude certain groups from research need to be based on

scientific grounds

Institutional review board Institutional review board (IRB): reviews research conducted within institution to ensure

ethical standards maintained

All universities with federal funding have an IRB

Must have at least 5 members, 1 of which must be from outside institution

All research conducted by faculty, students, and staff must be approved by IRB

Institutional review board Must submit application to IRB before beginning research

IRB can make several decisions:

Exempt: no risk to participants; study does not need further IRB review Examples: Studies with archival data where data cant be linked;

naturalistic observation in public place; survey research with anonymous

responses and no stress to participants

Exempt decision must be made by IRB: researcher cant decidethis ontheir own

Minimal risk research: risks of harm no greater than those encountered in daily

life Examples: routine physiological testing; moderate exercise; research

related to individual or group behavior or characteristics of individuals

No special safeguards required Quick, straightforward IRB review

May get expedited review instead of full board review

Greater than minimal risk: places participants at risk of physical or psychological

harm Examples: research involving physical or psychological stress, invasion of

privacy, collection of sensitive information

Requires full IRB board review

Must be reviewed at least annually

Fraud and plagiarism

Fraud: fabrication of data

Dont do this. Period.


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Plagiarism: misrepresenting someone elses work as your own

Always cite sources. If an idea wasnt yours, it needs to be cited.

If directly quoting, use quotation marks, and cite properly.

Chapter 4

Variables Some variables have numeric properties associated with them

Height Cognitive ability

Extraversion Some variables have categories

Gender

Geographic location Major

The type of variable (quantitative vs. categorical) determines what kind of statistical

analyses can be conducted

Operational definition: set of procedures used to measure or manipulate variable

Need operational definition to empirically study variable

Example: Is personality related to academic performance?

What do we mean by personality?

Variable: event, situation, behavior, or individual characteristic that varies

Has to have 2 or more levels

Variable examples:

Gender Job satisfaction

Reading skill Self-esteem Hair color

Height

Agreeableness

What do we mean by academic performance? Better question: Is conscientiousness, as measured by the IPIP 20-item

conscientiousness scale, related to college students GPA?

Construct validity: does the operational definition of a variable actually measure the

variable?

Important implications for scale and test construction

If operational definition doesnt capture the variable of interest, cant makemeaningful conclusions from study

Example: using GPA as a measure of cognitive ability. Is GPA a goodoperationalization of cognitive ability?

Independent variable (IV):variable being manipulated by experimenter; variable

believed to be causal variable


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Dependent variable (DV):measured but not manipulated. Hypothesized to change dueto manipulation of independent variable.

Example: Study comparing several remedial reading programs to see how they affect

student reading skill

IV: remedial reading program

DV: reading skill Relationships between variables

Relationships between variables

Positive linear relationship: If value of one variable increases, the value of the other

variable increases

Negative linear relationship: as value of one variable increases, the value of the othervariable decreases

No relationship: as value of one variable changes, value of other variable doesnt change

Curvilinear relationship: two variables are related, but not always in same direction

Nonexperimental methods

Relationships studied by making observations or measures of behaviors of interest

Variables observed as they naturally occur Example: As age increases, positive mood increases.

Direction of cause and effect cant be established

Example: People who exercise more have lower stress. Does exercise reduce stress, or do

people who are highly stressed have little time available to exercise?

Experimental method

Involves direct manipulation and control of independent variables Manipulate independent variable(s), and measure effect(s) on dependent variable(s)

Can evaluate cause and effect Experimental method Experimental control: extraneous variables kept constant

Example: Does increased caffeine consumption improve reaction time?

Could bring people into the lab first thing in the morning and tell them to avoid

caffeine prior to arrival

Can ensure experimental and control groups equivalent

Caffeine could be given only to experimental group

Randomization: Dont know which participant characteristics will affect dependentvariable

No way to guarantee groups are 100% the same Randomly assigning participants to groups ensures that potentially confounding variables

will likely be spread evenly between groups

Randomization helps to control effect of any variables that cant be held constant


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Third-variable problem: unmeasured variable may be creating relationship between 2

measured variables

Example: There is a significant positive correlation between ice cream consumption and

homicide rates.

Third variable: Heat. Heat leads to increased ice cream consumption, andincreased violent crime.

Confounding variable: third, uncontrolled variable that we know is affecting our results.

Example: Relationship between organizational level and pay satisfaction: Gender

could be confounding variable

Internal validity

Internal validity: ability to draw conclusions about causal relationships from results of

study

High internal validity: Can be reasonably certain that one variable caused changes in the

other

Strong internal validity requires: Temporal precedence

Covariation between variables Alternative explanations can be eliminated

External validity

External validity: ability to generalize results to other populations and settings Examples:

Do findings from a study using undergraduate students apply to adults in

the workforce?

Does the relationship between conscientiousness and academic

performance hold when GPA is used to measure academic performanceinstead of class rank?

Do work-family conflict theories based on studies conducted in the U.S.

apply to dual-income households in Japan?

External validity

In a single study, often a trade-off between internal and external validity

Artificiality of highly controlled experiments contrasts with real-world settings

But, nonexperimental methods often have poor internal validity

Why experimental methods arent always best Artificiality of experiments: carefully controlled laboratory experiments rarely mimic

real-world situations

Field experiments: trade-off between experimental and non-experimental

methods Independent variable manipulated in natural setting

Example: To examine whether new cashier training program improves job

performance, experimenter could randomly select half of the new cashiers

at a store to go through the new training program, and the other half to go


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through the old training program. Job performance could be compared

between the 2 groups

Ethical and practical concerns

Many things cant be ethically manipulated: drug use, childhood abuse, bullying,

etc. Many variables cant be reasonably manipulated: childhood memories, parental

relationships, etc.

Person-level attributes, such as ability and personality, cant be manipulated Why experimental methods arent always best

Sometimes goal is merely to describe behavior

In this case, experiment isnt needed Example: Why do individuals choose to be affiliated with a political party?

If you want to predict behavior, cause and effect may not be important Example: If there is a strong relationship between narcissism and workplace theft,

dont need to know whether cause-effect relationship holds

Using multiple methods No study is perfect

Best understanding of a phenomenon will happen when multiple studies using multipleresearch methods are used

Example: Does negative performance feedback cause stress?

Study 1: Conduct laboratory experiment with college students

Study 2: Conduct laboratory experiment with working adults Study 3: Conduct field study comparing stress levels of employees who

received negative performance appraisal to employees who received

positive performance appraisal

Chapter 5

Reliability Reliability: consistency or stability of a measure of behavior

Scores dont change much from one administration to the next

Classical test theory: Observed scores on a measure have 2 parts:

True score: actual score on the variable if we could measure it perfectly Measurement error: anything other than true score that affects the observed score

X=T+E

Reliability

Unreliable measure example: A measure of intelligence is given to Marie 3 times within1 week. Her score the first time is 50, her score the second time is 100, and her score the

third time is 15.

Reliable measure example: A measure of intelligence is given to John 3 times within one

week. His score the first time is 50, his score the second time is 52, and his score the

third time is 49.


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Test-retest reliability Measure the same individuals at 2 points in time Test-retest reliability measured by calculating correlation between scores at time 1 and

scores at time 2

Should be at least .80

Problems: Participants remember questions from time 1 to time 2, artificially inflating

correlation

Difficult to test same people twice

Some variables are expected to change over time: mood, stress, etc.

Internal consistency reliability

Requires only one administration of test Evaluates reliability by looking at degree to which answers to items correlate with one

another

Several ways to evaluate internal consistency Split-half reliability: correlation of total score on one half of test with total score on

second half of test Good way to do this: randomly select items for each half Bad way to do this: correlate the first half of items with the second half of items

More items=more reliability: have to use Spearman-Brown split-half reliabilitycoefficient to correct for loss of items

Internal consistency reliability

Cronbachs alpha: average of all possible split-half reliability coefficients

Scores on each item correlated with scores on every other item

Can be easily calculated with SPSS

Item-total correlations: correlation of each item score with total score based on all items

Good for screening items

Items with low item-total correlations can be removed from measure

Interrater reliability

Extent to which raters agree in their judgments Example: 3 clinicians rate an individual on level of psychopathy

Limitations of reliability

Reliability very important!

But, a measure can be reliable but still not tell us what we need to know Example: You develop a measure of personality which involves measuring the

circumference of someones head. This measurement will be the same every time its

administered (its reliable), but its still not telling us anything about personality

The measure is not valid

Construct validity

Construct validity related to measurement: is measure actually measuring what it issupposed to measure?

Example: Is intelligence test measuring intelligence, or something else?

Construct validity related to variables: does operational definition actually reflect truemeaning of variable?


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Example: Is GPA accurately reflecting academic ability? Multiple different types of evidence can be used to support construct validity

Face validity Measure seems to measure what its supposed to measure

Example: High face validity extraversion item: I enjoy meeting new people. Problem: Face validity not always tied to construct validity

Highly face-valid measures may not measure what theyre supposed to measure

Magazine personality tests

http://www.parents.com/parents/quiz.jsp?quizId=/templatedata/ab/quiz/data/BirthOrderQuiz_03052004.xml

Measures with low face validity can be measuring what theyre supposed to

measure

Implicit association tests https://implicit.harvard.edu/implicit/selectatest.html

Content validity

Does content of measure capture all of the content that defines the construct?

Example: Job performance measure includes only items related to teamwork (low

content validity)

Job performance measure includes items related to all major aspects of the job

(high content validity)

Useful, but still not sufficient

Predictive validity

Do scores on a measure predict an outcome?

Scores gathered on measure, and then outcome measured at a later time

Examples: A depression inventory should relate to future depression diagnosis, a jobknowledge test should relate to future job performance, SAT scores should relate to first-

year freshman GPA

Can analyze this using correlation and/or regression

Concurrent validity

Looks at scores on a measure and how they relate to an outcome

Can use correlation, regression, or a test of mean differences

Unlike predictive validity, measure and outcome scores collected at same time Two ways this is used:

Do 2 groups known to be different on a construct score differently on the measureof that construct?

Example: Do people with high GPA score higher on an academic

achievement scale than people with low GPA?

Do people who score differently on a measure of a construct behave differently ina situation?

Example: Do people with low scores on a job knowledge test have lower

job performance than people with high scores on a job knowledge test?


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Convergent validity

Extent to which scores on a measure are related to scores on measures of same or similar

constructs

Example: Scores on a new self-esteem questionnaire should be positively related

to scores on two older self-esteem questionnaires Example: Scores on a new intelligence test should be positively related to scores

on a problem-solving test

Can use correlation

Discriminant validity

Extent to which scores on a measure are not related to scores on unrelated measures

Example: Scores on an extraversion questionnaire should notbe positively related

to scores on an introversion questionnaire

Example: Scores on an intelligence test should notbe related to scores on an

agreeableness test

Can use correlation

Measurement scales

Nominal scales: no numerical properties

Categories used to differentiate responses

Examples

Gender

Major City

Very limited in types of statistical analyses that can be performed

Ordinal scales: levels of variable can be rank-ordered

But, intervals between items not consistent or known

Example:

Rank foods in order of spiciness The food ranked as first and the food ranked as second might be very close in

spiciness, but the third-ranked food might be much less spicy than the food ranked

second

Still very limited on types of analyses that can be used

Interval scales: difference between numbers on scales is meaningful

Difference between a 1 and a 2 is the same as the difference between a 2 and a 3

Have arbitrary 0

0 does not indicate complete absence of the trait Scoring a 0 on an intelligence test would not mean that a person had absolutely no

intelligence

Can do a wide variety of statistical analyses Measurement scales

Ratio scales: difference between numbers on scale is meaningful Scale has absolute 0


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0 indicates a total absence of the quality No psychological scales fit this requirement

Measurement scales When possible, use interval-level scales

More quantitative information

More statistical analysis options

Exam 1 Study Guide 3600

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