Chapter 2: The Research

Understanding and Prediction

Hypothesis

=A tentative statement about two or more variables-a tentative statement about how things work (Students that eat breakfast perform better in school-what are the two variables here?) -

an educated guess

.

RESEARCH

Applied Research

=has clear, practical application; goal to control positive/negative situations • If it is found that students who eat breakfast perform better, schools will initiate a breakfast program

Basic Research =

questions of interest that may not have immediate, real world application • How does anxiety affect people’s desire to be with others (affiliation need)?

• Do different cultures react differently to stress?

THEORY

•

• If we observe there is a relationship between breakfast eaters and performance we formulate a theory

Theory

=

predicts behavior or events

-

only change as new information available

-

more permanent-have considerable facts to support it.

Unit 2 Review

longitudinal study

study same people over a long period disadvantage=expensive; drop out advantage=same cohort, less confounding variables

cross sectional study

different cohorts at the same time, less expensive and less time; disadvantage=different cohorts

Survey-

questionnaire (can study more people, study things not ethical through experiment)

case study-

in depth study of an individual (used for rare occurrences of events/illnesses

experimental study

-manipulation of Independent variable (experimental group receives the treatment/control group does not) to see it’s effect on the dependent variable

•

Experimental Research: Looking for Causes Experiment

= manipulation of one variable under controlled conditions so that resulting changes in another variable can be observed (feeding one group of students) –

Detection of cause-and-effect relationships Variable

=

any measurable conditions, controlled or observed in a study

• •

Independent variable (IV)

see its effect on = variable

manipulated

(food) to

Dependent variable (DV)

= variable

measured

manipulation of IV (school performance) – How does IV (food) affect DV(performance)? and affected by

Operational definitions

precisely define each variable(IV-breakfast, DV-school performance)-

required for good experiment

most needed aspect of a study-

so study can be

refuted

or

verified through REPLICATIOIN

(makes study scientific)

Independent and Dependent Variables Hypotheses

1. Riding the bus to school (IV) makes students more intelligent(DV) 2. Kids who view aggressive cartoons(IV) are more likely to act aggressively(DV) 3. AP Psychology students who eat chocolate(IV) perform better on vocabulary tests (DV)

Operational Definitions=clearly defining independent/independent variables for replication

Children (Male/female, ages 4 – 6) who

view aggressive cartoons

= , viewing all of Sponge Bob, episode 5, while alone in…..

are more likely to

act aggressively

= placed on the playground for 30 minutes with 10 children who did not view cartoon, five minutes after cartoon was shown and strikes another child

•

Experimental and Control Groups: The Logic of the Scientific Method Experimental group

(exposed to manipulation of independent variable-chocolate given) •

Control group

(similar subjects but does not receive IV manipulation given to the experimental group-no chocolate given) • EVERYTHING ELSE FOR THESE TWO GROUPS MUST BE THE SAME

and

Resulting differences in the two groups

must

be due to the independent variable •

Extraneous and confounding variables Levels of independent variable =

1.chocolate verses not getting chocolate; 2. also two independent variables=chocolate and breakfast verses none for Control Group

The Scientific Method: Terminology

Population

=animals or people from which a sample is drawn (all AP students in Broward) and researchers want to generalize about

Participants

or

subjects

=organisms whose behavior observed in a study

Sample

=subjects from the population (AP psych students selected from all schools in Broward County)

Random sampling

-all in population have equal chance of selection.

Representative Sample

is

only way to generalize results to population Random assignment

participants have equal chance of placement in control or experimental groups;

lessons confounding variables

Figure 2.16 The relationship between the population and the sample

Unit 2 Review

Descriptive statistics describes data –

mean, mode , median, standard deviation

Measures of central tendency

= typical or average score in a distribution • •

Mean

: arithmetic average of scores

Median

: score falling in the exact center, or the average of the two center scores •

Mode

: most frequently occurring score mean is most useful measure of central tendency except when

Outliers

= mean distorted by extreme scores

statistical inference-

conclusions drawn about the relationship between variables, from a sample to entire population

Figure 2.11 Measures of central tendency

Descriptive Statistics: Correlation

• When two variables are related to each other, they are

correlated

.

•

Correlation Coefficient

= relationship between two variables • How well does A predict B?

– Strength of the correlation -1.0 to +1.0

positive correlation

-as one variable increases, so does the other ; as one variable decreases, so does the other

negative correlation-

one variable increases the other decreases

Figure 2.14 Interpreting correlation coefficients

Correlation

Correlation

Correlation

Correlation

Correlation

Correlation

Scatter plot =best way to show relationship between variables

3 4 5 6 Hours Spent Watching Television per Day & GPA

PERSON HRS GPA

1 2 0.5 1 3.50

3.75

2 2.5 3 3.5 4.00

2.75

2.75

1.75

7 8 9 10 4.5 5 5 7 2.25

1.50

2.50

1.00

Which statistic approximates the relationship between the variables?

50% N=20 N=10 r=

-

.

90 r=.50

Unit 2 Review

In

normal curve

-

distribution of scores

-68% fall within 1 SD above/below the mean

percentile scores

takers – the same as or better than 72% of population/test

Describing Data

Measures of Variability-how scores vary from the center

• Normal Curve (bell shaped)

Descriptive Statistics: Variability

=

how much scores

vary

from each other

and

from mean

(see the normal curve or bell curve pp 63-64 or 66-Barrons) –

Standard deviation

= how far scores are from the mean/average; for the Normal curve with IQ, one standard deviation is 15 points from the mean If scores deviate 10 points, curve by 10 points, how far will scores deviate from mean???

In normal curve

-

distribution of scores

-68% fall within 1 SD above/below the mean

-

Range

= distance between highest and lowest scores in data set

Figure 2.12 Variability and the standard deviation

Z scores

measure the distance of a score from the mean (either - or +); a z score of -1 is 15 points below the mean, -2 is 30 points below mean

Percentile scores – the same as or better than 72% of population/test takers 38 th percentile =

you did the same or better than 38 percent of people who took a test

Distribution of Scores-Bell Curve

• • •

Symmetrical distribution -see

p. 65 in Barron’s

Positively skewed

(curve to

Left

)-more low scores than high

Negatively skewed

(curve to

Right

)-more high scores than low If Negative skewed due to test scores, can only assume????

Experimental Research:

•

Replication=

repeat a study to see if earlier results are duplicated (this is why the

operational definitions

are important) •

Reliable

when you can replicate or repeat it •

Valid

when it measures what the researcher set out to measure

Statistics and Research: Statistics Drawing Conclusions

– using mathematics to organize, summarize, and interpret numerical data –

Descriptive statistics

(the numbers) organizing and summarizing data (measures of central tendency, measures of variability, and the correlation coefficient)

to see if there is a relationship between variables

–

Inferential statistics

: interpreting data and drawing conclusions about the larger population

Statistical significance

= the relationship found between the IV and DV is not due to chance

(.05 level of significance

)=

less than 5 chances in 100

. It can never be 0 because we can never be 100% certain

Correlation Predicts Strength or Relationship Between Variables DOES NOT Say ONE CAUSES OTHER:

Correlation does not =Causation

– Foot size and vocabulary positively correlated – larger feet belong to older children

Strengths and Weaknesses of Experimental Research

• Strengths: –

conclusions about cause-and-effect

relationships can be drawn • Weaknesses: – artificial nature of experiments – ethical and practical issues

Figure 2.10 Comparison of major research methods

Advantages/Disadvantages of these (naturalistic observation, case studies, surveys) called Descriptive/ Correlation Methods : Advantage:

• explore questions that can not be examined with experimental methods (poor maternal nutrition and birth defects)

Disadvantage

:

Cannot control events

to isolate

cause and effect

Evaluating Research: Methodological Pitfalls

• • •

Sampling bias =

sample not representative of population- I CANNOT DRAW CONCLUSIONS

Placebo effects = participants’ expectations

lead them to experience some change (do better on a test, less headaches), regardless of the Independent Variable/Treatment

Placebo method

helps=give both groups a drug (one real and one a placebo)

Evaluating Research: Methodological Pitfalls

Distortions in self-report data

(survey, interview): –

Social desirability bias =

give socially approved answers to personal questions –

Response set =

respond to questions in a particular way that is unrelated to the content of the question (agreeing with almost everything on a questionnaire)

-Hawthorne Effect

=

changes

in subjects

behavior due to

the

attention of researcher

(having control and experimental groups help)

Evaluating Research: Methodological Pitfalls

•

Experimenter bias

= researchers expectations about outcome of study influences results; treats experimental and control groups differently to increase chance of confirming hypothesis

double-blind control/procedure

= neither subject or experimenter know which group is the control or experimental group

Single blind control/procedure =

the subject does not know if they are the control or experimental group

Ethics in Psychological Research: Do the Ends Justify the Means?

Ethical standards for research: the

American Psychological Association

academic research and the

IRB : ensures ethical treatment for animal and human research :

1. Informed consent

– participant’s permission, told potential risks, offered alternative activity

2. No harm to humans Psychological or physical 3. Minimal harm to animals Ethical Treatment 4. Debriefing to offset deception 5. Confidentiality-

cannot share names (includes test scores-UNLESS WRITTEN PERMISSION PROVIDE

Deception IS PART OF RESEARCH!!!

Figure 2.17 Ethics in research