What is Science? Chp 1.

What is Science? Chp 1

Misunderstanding about Science #1
Science is a collection of proven facts. If you want to become a scientist you just need to memorize the facts. Chp 1

Science = Method of Inquiry Constantly re-examining current theories.
Chp 1

Nothing is ever proven!!!!! Proof Chp 1

Scientism or Pseudoscience Often makes claims of Proof!!!!
Make reference to Authorities but not to the evidence. Use undefined, meaningless or misleading terms. Chp 1

" % Pure: It Floats" This description of Ivory Soap is a classic example of junk science from the 19th century. Not only is the term "pure" meaningless when applied to an undefined mixture such as bath soap, but the implication that its ability to float is evidence of this purity is deceptive. The low density is achieved by beating air bubbles into it, actually reducing the "purity" of the product and in a sense cheating the consumer. Chp 1

Common mistake in interpreting Scientific Evidence #2
If A is a factor that causes the effect, than B cannot be a factor. Chp 1

Genetic Factors Serotonin (hormones)
Example: Depression Genetic Factors Serotonin (hormones) Coping Strategies Current Stressors Depression Chp 1

Science is EVIDENCE BASED.
Chp 1

Scientific studies provide evidence for understanding the universe
Scientific studies provide evidence for understanding the universe. The process of science is always ongoing. Chp 1

Scientific Methods The methods that are used to establish the valid and reliable knowledge that underlies science has developed and improved over time. Chp 1

Hypothesis Testing Observations (data)
Theory Inductive Deductive Prediction Empirical Hypothesis Evidence Observations (data) Advantage: Self correcting cycle. Chp 1

Scientific Method in Psychology
Does the data support the theory? If not, informs the revise of the theory. Refine theory Develop theory Light affects plant growth. Form hypothesis If light affects plant growth, then plants exposed to light should differ in growth from plants deprived of light. Test hypothesis Compare groups of plants that differ on nothing other than exposure to light. Chp 1

Theory - Current understanding of a phenomenon.
Is supported by evidence (based on a body of research – not just one study). Allow us to make testable predications (hypothesis). The theory Must be Falsifiable. Chp 1

1) Descriptive: No Hypothesis
Types of Research 1) Descriptive: No Hypothesis 2) Relational Studies: (Circumstantial Evidence) - predictive value 3) Experiments - evidence of cause and effect. - explanation and control Chp 1

Can a 4 year old keep themselves from eating a marshmallow now,
If they are told they will get two latter? Video Link 6 mins

Y Psychology: Scientific Study of Behavior and Mental Processes
Goals Describe Behavior Predict Behavior Explain Behavior Control Behavior

Scientific Vocabulary Variable: Anything that takes on different
values, at different times, places, or in different individuals. Constant: Anything that remains the same for all individuals, at all times, and all places during the study. Chp 1

Characteristics of the Scientific Approach
In order to study a variable we need to Defined what we mean be the Variable (i.e. how are we measuring it?) Operational Definitions – Detailed descriptions of measurement criterion. Chp 1

Does it measure what it claims to?
Construct Validity Is the measure Valid? Does it measure what it claims to? Chp 1

2. Is the measuring device reliable? - will the same value be obtained
- by different researcher - at different times Chp 1

Control: Are all other possible explanations
for the results eliminated? Chp 1

Replication The same results must be found if the study is repeated.
Replication with modifications allow us to learn about the limitations and generalizations of a finding. - different groups, different times, different settings, different operational definitions. Chp 1

Meta-analysis A statistical technique for combining the findings from independent studies. Accounts for variables that may influence outcomes. Chp 1

Example: A meta-analysis of 208 experiments found that the mere-exposure effect is robust and reliable, with an effect size of r = This analysis found that the effect is strongest when unfamiliar stimuli are presented briefly. Mere exposure typically reaches its maximum effect within 10–20 presentations, and some studies even show that liking may decline after a longer series of exposures. For example, people generally like a song more after they have heard it a few times, but many repetitions can reduce this preference. A delay between exposure and the measurement of liking actually tends to increase the strength of the effect. The effect is weaker on children, and for drawings and paintings as compared to other types of stimuli. One social psychology experiment showed that exposure to people we initially dislike makes us dislike them even more (Bornstein, 1989). Chp 1

a difference between conditions is produced by researcher
True Experiments objective measure a difference between conditions is produced by researcher all other variables held constant can make conclusions about cause and effect EI (2)

Independent Variable (IV): a variable that is presumed to cause changes to occur in another variable. In a true experiment we manipulate the IV to determine its effect on a Dependant Variable (DV). EI (2)

Extraneous or Confound Variables: anything other than the manipulated variable that is different between two conditions. Serves as an alternative explanation of the cause of differences between conditions. EI (2)

Mediating and Moderating Variables
A Mediating Variable occurs between two other variables in a causal chain (e.g., anxiety causes distraction (mediating variable) which affects memory). Moderating Variables qualify a causal relationship as dependent on another variable - e.g., the impact of anxiety on memory depends on level of fatigue (moderating variable).

Criteria for Identifying a Causal Relation
cause (IV) must be related to the effect (DV) (relationship condition) changes in IV must precede changes in DV (temporal order condition) no other explanation must exist for the effect (eliminate confounds)

Definition of an Experiment:
objective observation of phenomenon that are made to occur in a strictly controlled situation in which one or more factors are varied and others are kept constant

Limitations of the Experimental Approach
Does not test the effects of nonmanipulated variables (many potential independent variables cannot be directly manipulated) Artificiality - refers to potential problems in generalizing findings from laboratory settings to the “real world”. Inadequate method of scientific inquiry

Experimental Research Settings
Field experiments Advantage: may be easier to generalize findings. Disadvantage: less control of extraneous variables Laboratory experiments Advantage: more control than field experiments; Disadvantage: possibly more artificiality

Example: Eyewitness Testimony
EI (2)

Quantitative Research
Nonexperimental Quantitative Research Types correlational study cross-sectional and longitudinal studies natural manipulation research

Two (or more) variables measured on same person.
Relation Studies Two (or more) variables measured on same person. Measures how well we can predict one variable if we know the value of the other variable. EI (2)

The magnitude of the correlation defines the degree of relationship.
Pearson Correlation Statistic ( r ) measures the degree and the direction of the relationship. Ranges from -1 to +1 The magnitude of the correlation defines the degree of relationship. EI (2)

r = 1.0 - one variable is perfectly predicted from the other variable.
r = no relationship. r = one variable is perfectly predicted from the other variable. The closer to +/- 1 the stronger the relationship. The closer to 0 the weaker the relationship. EI (2)

The sign (- or +) indicates the direction of the relationship.
Positive - change in same direction. High on one variable predicts high on the other. Negative - change in opposite directions Low on one variable predicts high on the other. EI (2)

Correlations Do young hockey players take more penalties than
old hockey players? r - statistical relationship twixt 2 variables (age & penalties) Penalty Minutes Age Ÿ (-) r

* * * * * * * * * * * * * Scatter Diagrams Data point.
- point on diagram representing the co-occurrence of the two variables for each subject. Variable Y * * * * * * * * * * * * * Variable X EI (2)

* * * * * * * * * * * Line of Best Fit Straight line that
fits the data so the summed distance between the line and each data point is lower than for any other possible line. * * * * * * * * * * * Variable Y Variable X EI (2)

Correlation Diagram Guessing Game EI (2)

Predict the correlation between these variables
Concept Check Predict the correlation between these variables (High, Medium or Low? Negative or Positive) Weight and Height IQ and shoe size SAT scores and Grades in College Miles you have drive since a fill-up and amount of gas in your tank. Number of Storks and Birthrate in a town EI (2)

Correlation  Causation Directional Problem A could cause B
B could cause A Third Variable ( C) Problem C could be causing both A and B EI (2)

Warning: People often try to use correlations as evidence of cause.
This is bad science. EI (2)

Correlational Studies Two variables measured on same person.
Relationship between 2 variables measured. Results indicate level and direction of prediction – does not indicate cause. Why do Correlational Research? - preliminary studies (e.g., epidemiology) ethical concerns (e.g., smoking and cancer) non-manipulatable variables (e.g.,sex) EI (2)

Path Analysis Correlational method of testing relationships among variables by seeing how well they fit some theoretical model Direct effects – when a variable directly impacts another Indirect effects –effect occurs through mediating variable

Hypothesized causal ordering for how Socio-economic status (SES), Intelligence (IQ) and Achievement Motivation (AM) Are related to GPA SES AM GPA IQ SES GPA AM IQ

Natural Manipulation Research
- looks like experiments, but they are confounded. Variable is not manipulated by experimenter. e.g., comparisons of pre-existing or self-selecting groups. Males vs. Females Arts vs. Science Students EI (2)

If Males score better on Math tests than Females can I say being Male causes better math scores?
Other explanations? EI (2)

EI (2)

Developmental Change Studies
Longitudinal Studies - Measure Same Subjects repeatedly at different Ages. Cross-Sectional Studies - measures different subjects at different ages. EI (2)

Age-Cohort - group of people of the same
age, who share similar cultural and historic experiences. Cross-sectional - confounds Age and Age Cohort effects. EI (2)

Longitudinal - follow one Age cohort. - no Age X Cohort Confounds
But … Costly, time consuming and do not necessarily generalize to other cohorts. Cohort-Sequential Design - incorporate both Longitudinal and Cross Sectional. EI (2)

Reading Readiness Scores in 1970 and 1985
EI (2)

Qualitative Research Interpretive Multimethod (triangulation) - interviews, introspective analysis, personal experience, observations, photographs ect. . . Conducted in a natural setting (field)

Methods of Data Collection
Tests Questionaires Interviews Focus Groups

Descriptive Quantitative Studies Concerns - defining variables
- sampling (who, where and when) - Experimenter Bias - Reactive effect Observation can change behavior EI (2)

Naturalistic Observation Studies e.g., Ethnology
EI (2)

Complete Participant Observer Studies
e.g., Schouten & McAlexander, 1995

E.g., Gloria Steinem EI (2)

Steve Garasky and Brenda Lohman, pictured here in the observation room of Iowa State University’s Child Development Lab School, are authors of a new study finding increased levels of stress in adolescents are associated with a greater likelihood of them being overweight or obese. (Credit: Bob Elbert/ISU News Service) Marshmellow Study

Sampling

Who to include in your study!
Your Sample should represent the population you want to apply the findings to. ch12(1)

No Sample will represent everyone in the Universe.
ch12(1)

WHO is in the study greatly effects WHO the findings apply to.
Research has found that Ginko aids memory. Should You use it??? ch12(1)

Scientists at the New York Institute for Medical Research reported that one-third of the Alzheimer's patients taking ginkgo improved in tasks involving memory, such as remembering the date or the names of relatives. About half of the group didn't experience improved memory, but showed no signs of increased memory loss. ch12(1)

Sample Population Sampling Bias Population Sample
Collection of Ss used in a study Population Larger collection of people about which we want to generalize Sampling Bias When the sample is not representative of the larger population Sample Population

Population Sample Element

Small numbers of subjects can be used to estimate the
Who is in the study? Small numbers of subjects can be used to estimate the behavior of a larger group… Real Clear Polls But the results will be VALID only if the sample is a good mini-version of the population. EI (2)

Statistics Vs. Parameters
Statistics are estimates of characteristics of the population.

Sampling Error The difference between the value of a sample statistic and the value of the population estimate. Samples statistics are estimates and all estimates have some potential for error. The only sample that can perfectly estimate a population parameter is one that includes ALL members of the population (Census).

If I repeatedly take samples of the same size from a population, will I always get the same estimate?

No, if I repeatedly took samples I would get a distribution of estimates. Most would be pretty close to the population parameter. The more inaccurate the estimate the less likely I would be to get that estimate. Mean of the distribution of sample means is the mean of the parameter. Error is normally distributed. The standard deviation of this distribution is the Sampling error. Sample Statistic

The larger the sample size, the lower the sampling error
The larger the sample size, the lower the sampling error. Larger samples give better estimates!

Small N Designs Used to increase the power of a design because there are a small number of participants. E.g., A - B - A design Chp 1

Equal Probability of Selection Method
Each Member of the Population has an equal, unbiased chance of being included in the sample. Ex. The School board does a random sample of students in millitary Academy. There are 75% male students enrolled and 25% females. What percentage of students in their sample should be female?

If a sample is truly random than the sample should be a good approximation to the characteristics of the population. Population Sample

In order to have a true random sample there can be no systematic reason why one member of the population is included or excluded. Advantage: Even if you do not know the demographics of the population you are sampling, random sampling will give a non-biased sample.

Simple Random Sampling
1. Identify all members of the population. Use a random processes to select sample members.

Stratified Random Sampling
Population Divided into strata (mutually exclusive groups). i.e. Males and Females. Ex. The School board does a random sample of students in military Academy. There are 75% male students enrolled and 25% females.

Proportional Stratified Sampling
The proportion of members of each strata match the proportions of the population. From Military school example 25% Female and 75% Male Ensures that sampling error does not cause unequal sampling from each sex.

Disproportional Stratified Sampling
From Military Example: 50% Male and 50% Female Why might you want to use this method? Will it give an unbiased estimate of the population?

Cluster Random Sampling
Randomly Select from a set of groups of predefined units (classrooms, neighborhoods, teams). I.e., I may have 15 general psych classes I can choose among for a study. I could randomly choice 3. Each sample member is not independently selected, it is a random selection of classes.

Non-Random Sampling Techniques
Q. IF I do a telephone survey is this a random sample or convenience?

Systematic Sampling List all members of the population. N = pop. size Determine what size sample you want. n = sample size. N/n = k (Sample interval). E.g., N = 100 n = 10 K = 10. Randomly select a number from 1 to K. Select each 10th person from the population list.

Disadvantage: You have to be able to identify all members of the population (Sampling Frame) and all randomly selected elements must agree to be included in the sample. If I do a telephone survey do you think there are some people who would be more likely to agree to participate than others? Response Rate – percentage of people selected who actually participate.

Quota Samples Used when you are trying to produce a sample which match the demographics of a known population you wish to generalize to. e.g., opinion, attitude or political polls. Determine the numbers of people in specific demographics that you need. Then use convenience sampling to fill each quota.

The resulting sample is not random but it matches the demographics of the population you are trying to learn about.

Convenience Sampling: Use who is available
Convenience Sampling: Use who is available. Many Psychology studies use General Psychology Students. Action Research uses students/clients. Is this a problem? It depends on how similar the General Psych students are to the population I want to generalize to.

Replication in Different Populations

e.g., What is the average weight of these two rats?
Trying to average very diverse groups often ends up in measures that are not very representative of any individual in the group. e.g., What is the average weight of these two rats? ch12(1)

According to the book, a majority of Americans:
• Eats peanut butter at least once a week • Prefers smooth peanut butter over chunky • Can name all Three Stooges • Lives within a 20-minute drive of a Wal-Mart • Eats at McDonald's at least once a year • Takes a shower for approximately 10.4 minutes a day • Never sings in the shower • Lives in a house, not an apartment or condominium • Has a home valued between $100,000 and $300,000 • Has fired a gun • Is between 5 feet and 6 feet tall • Weighs 135 to 205 pounds • Is between the ages of 18 and 53 • Believes gambling is an acceptable entertainment option • Grew up within 50 miles of current home ch12(1)

Experimental Design Chp 1

Internal Validity - The extent to which all explanations for changes in the DV between conditions have been eliminated -- other than the IV. ie(7a)

Extraneous variable - any variable other than IV that effects DV.
Control Experimental Extraneous Variable ie(7a)

Control Confound Experimental
Confounding Extraneous Variable - any extraneous variable that affects one condition differently than it affects other conditions. ie(7a)

Between Subjects Designs
Two (or more) Groups of Participants compared to each other. Each group has a different level of the IV. Experimental (Treatment) Group Control Group ie(7a)

Major Potential Confound: Individual Differences (I. e
Major Potential Confound: Individual Differences (I.e., perhaps the participants in one group are not comparable in many ways to participants in the other group). ie(7a)

Within Subjects Design
One Group of participants measured under more than one condition of the IV. Experimental Condition Control Condition Potential Confounds Since subjects cannot be in more than one condition at one time, anything that is not the same at each of the times of measurement (other than the IV) is a potential confound. ie(7a)

Confounds with Pre-Post Designs
Other than the treatment (IV) what could cause difference in DV between conditions (I.e., what possible confounds could there be?) Confounds with Pre-Post Designs History - any changes that occurred between Pre and Post Test other than IV. ie(7a)

Maturation - biological/psychological changes between pre-post test.
ie(7a)

Instrumentation - changes in measurement device or operational definition between pre and post test.
ie(7a)

Attention Deficit Disorder?
Are rates of Diabetes really increasing, or are we simply diagnosing more cases??? Attention Deficit Disorder? DUIs? ie(7a)

Testing : Changes is a person’s score for the post conditions that results from having been tested in the pre-test. Sensitization Boredom Practice ie(7a)

Attrition - subjects dropping out of the study.
ANYTHING that is a difference between the before and after condition other than the IV is a confound. ie(7a)

Hokey Pokey ie(7a)

Artifact - Effect caused by the procedure rather than by the IV.
Statistical Regression Artifact. - problem when subjects are assigned to groups based on the pre-test scores. - group scores will be pulled towards the mean of the DV. - Pre-test high scorers will appear to do poorer. Pre-test low scorers will appear to do better. - problem with unreliable measures ie(7a)

Not a problem if the DV is very reliable.
ie(7a)

Confounds (in pre and post designs) History Maturation Instrumentation
Internal Validity? Confounds (in pre and post designs) History Maturation Instrumentation Testing Attrition (Mortality) Statistical Regression Artifact* ie(7a)

Controlling for these confounds. Use a pre-post control group.
Treated the same as treatment Group except IV is not manipulated between tests. Control group affected by same, history, Maturation and Regression effects. Any difference between the treatment and control groups are not due to confounds. ie(7a)

Pre-test Treatment Post-test plus confounds Control Group
Experimental Group Pre-test Treatment Post-test plus confounds Control Group Pre-test confounds Post-test ie(7a)

Compare Pre-test scores. Are they the same to begin with?
Selection – confound due to assignment of subjects to the Control and Treatment Groups. Compare Pre-test scores. Are they the same to begin with? But could be a difference that interacts with the treatment. - use random assignment!!!! ie(7a)

subjects assigned to treatment and control group
Selection Confound subjects assigned to treatment and control group biased on a criteria (bias). Selection X (interactions) Any of the five confounds effect the control group differently than the treatment group. i.e., Alcohol treatment study - compare volunteers to non-volunteers e.g., Differential History ie(7a)

Differential Attrition
Selection X (interactions) Any of the five confounds effect the control group differently than the treatment group. Differential History Differential Attrition i.e., Alcohol treatment study - compare volunteers to non-volunteers ie(7a)

Differential Attrition Weight loss Study (Diet and Exercise condition)
No-Treatment Control Group Type of people that drop put of the study might depend on which study they are in. ie(7a)

How can we ensure the subjects in each condition are comparable?
Random Assignment Extraneous variables still effect DV, but it should not be a confound. ie(7a)

Treatment group = size of treatment effect plus confounds
Pre-test Post test Treatment Control Treatment group = size of treatment effect plus confounds Control group = estimate of the size of pre-post confounds. ie(7a)

Pre-test Post test Treatment Control Is the pre-post test change significantly greater in the Treatment Condition than in the control condition? ie(7a)

WS design with counterbalancing for order.
Used when comparing conditions and the conditions can be measured in any order. Weakness: Sequencing Effects: Participation in one condition may effects participation in a subsequent condition(s). ch8(1)

testing and not to differences in the IV.
A) Order Effects Changes in performance due to practice, familiarity, boredom that are due to repeated testing and not to differences in the IV. ch8(1)

e.g., Learning, Expectation, attitude, physical
Carry-over Effects Long-lasting effects participation in one condition that carry-over to effect performance in subsequent conditions. e.g., Learning, Expectation, attitude, physical changes due to experimental condition. E.g., Reward Contrast effects in Rats ch8(1)

Example of Carry Over effects: Reward Contrast effects in Rats
ch8(1)

Randomized Counterbalancing Each Subject gets a different order. ABC
ACB BCA BAC CAB CBA Subjects are randomly assigned to order conditions. ch8(1)

Complete Randomized Counterbalancing
All possible orders of conditions are used and participants are randomly assigned so that there are equal numbers in each order condition. ch8(1)

Equal numbers of subjects are assigned to each order condition, thus order effects are controlled by equalling the effects of order for each condition. Condition A Condition B Condition C 1st 2nd 3rd ch8(1)

Incomplete Counterbalancing
With 4 conditions all orders would total 24. ABCD BACD CABD DABC ABDC BADC CADB DACB ACBD BCAD CBAD DBAC ACDB BCDA CBDA DBCA ADAB BDAC CDAB DCAB ADBA BDCA CDBA DCBA ch8(1)

Rules for Incomplete Counterbalancing
Each condition must appear an equal number of times in each ordinal position. Each condition must precede and be followed by every other condition equal numbers of times. A B D C ch8(1)

Intrasubject counterbalancing Each subject gets each possible order.
ABCCBA ch8(1)

Control of Individual Differences e. g
Control of Individual Differences e.g., IQ, personality, sex, race, age etc. Random Assignment - Each member of the sample has an equal chance of being assigned to any of the conditions. ch8(1)

Ethics

Research Misconduct Fabrication, falsification 2 or plagiarism.

Treatment of Research Participants
Institutional Review Board (IRB) Risk/Benefits assessment Ethical Principles of Psychologists and Code of Conduct (APA, 2002).

Autonomy – Informed Consent
Autonomy – Informed Consent. Beneficence and Nonmaleficence Three Categories of Review Exempt Status ?? Expedited Full Board Review Special populations

Informed Consent Purpose, Procedure Risks and Benefits Incentives With Special populations Consent and Assent Passive vs. Active Consent

Coercion and Right to Decline to Participate
Alternative Assignments Right to Withdraw without penalty

Privacy Anonymity Confidentiality

Deception Active Deception – deliberately misleading Passive Deception - omission

Debriefing Debriefing, Dehoaxing and Desensitizing Functions Ethical
- undo deception - undo stress Educational Function Post experimental Inquiry Satisfaction

What is Science? Chp 1.

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