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65 Cards in this Set

  • Front
  • Back
1) Intuition
2) Authority
3) Tenacity
1. Not systematic, limited settings and experience. Limited by morality and religious beliefs; personal exp. Ex. colds.
2. Trusted, shorthand.
3. Taken for granted; common knowledge Ex. bayer aspirin
Knowledge as a combination of logic and empirical inquiry.
A) Descartes Rule of Inquiry:
A) 1) Accept nothing as true that is not self-evident 2) Divide problems into their simplest parts 3) Solve problems by proceeding from simple to complex 4) Always recheck your reasoning and calc.
Knowledge as a combination of logic and empirical inquiry.
B) Scientific knowledge
B) 1) rationalism: expand on truths 2) empiricism: observations used to dev methods, test, interpret.
Buildign science (empirical and logic) A) Data
B) Induc. deduc processes
C) Models
- Factual data not often used (observed with no inferences)
A) Obserbable beh as data to infer (induc) B) Based on construct, look for evidence of construct (Hypoth from theories).
C) Relationship of components, induc and deduc processes to make a construct. (Modest attempt to explain facts, theory more complex) Ex. tension (aids to a certain point) Processes: constructs as meanings; deduc/induc processes used. Relate constructs to each other.
A set of coherent ideas devel from observed info and that serve the purupose of predicting or explaining what we see or experience (General, complex truths)
Qualities of a good theory
Clear and parsimonious (simple, logical), supported by available data, helps explain occurences, testable and falsifiable (ex. psychoanly could not). Test pieces of theories, research can also be building block of theory; revise theory based on ideas about world. Research <-> thoery. Theories evolve as society changes.
An educ guess, based on theory (induc) or observ (deduc) of the world, how the world works with respect to the topic and variables you are studying. Ex. end of intro to research papers.
Four types of questions science tries to address
Build studies around hypoth.
1) describing beh: observing; building theory phase.
2) Predicting beh: when/what form to occur or not; leading to related beh? Ex. career happiness test (not nec causal). 3) Understanding the causes of behavior: need true experiments with no other causes. 4) Explaining beh: most complex phase, final after prev three. Why (are the above observ true) What process?
Research Process
A) Generating ideas
B) Problem definition of generating hypoth
B) more narrow with focused; generated from multiple sources or parts of theories, prev research. Replicate exactly? Modified replication? Tangential yet logical? Related theory? No other research: broad hypoth, describe beh.
C) Procedures/Design Phase
D) Data collection phase
E) Data analy phase
F) Interpretation phase
G) communication phase
C) How to study/collect data/formate. Who, how many? What statis analysis? Ethical check.
D) Textbk: observ phase. Various ways.
E) Analysis as planned; computers used (SPSS)
F) Interpret; support hypoth? meaning? multiple interp, not the end of research process, follow-up?
Levels of Constraint: controlling the research process through designs
A) Naturalistic
B) case study
Looking for particular char? Low: as nfo occurs naturally; no preconceived ideas. High: structured, controlled environ.
A) few guidelines, no partic beh
B) limit of 7 people, ex. classroom; asking particular ques in a particular setting. In a naturalistic setting perhaps.
C) Correlational research
C) Hypoth driven whether constructs/facts are related; defining variables. Participants to do specific things. Naturalistic or lab setting: degree of control matters (numerical variables).
D) Differential reseasrch
Close to correlational conceptually; quasi-experimental. Groups not assigned (ex. age, gender: naturally occurring groups) Defined advance.
E) Experimental research
Structurally like differentially but conceptually more constrained; specific outcomes, random/systematic assignment. Only type for casual conclusions. Choose design to fi question.
Continuous variables vs. categorical
A feature, char, quality or situation that you study as its values differ across or within your partic (vs. constant).
Different values vs. levels.
By characteristics of variable itself (can do A and B - role)
1) Behavioral 2) Stimulus 3) Organismic/Subject (can be more than one)
Represents particip behaviors or actions as they occur and are measured during resarch (ex. reaction time; accuracy)
In environ; naturally occurring or manipulated, that affect partic's thoughts or beh (ex. time of day).
Are inherent experiences, qualities or char of partic that occur naturally. Ex. eye color (cannot change), attitudes about violence in society (char), divorce, academic achievement.
Manipulated in exp for causality, organismic - occur naturally; non-manip.
DV: often a behavioral var in exper research, otherwise may be a subj as well...
Char or qualities that are same across all of a study's partic, usu because the research prevents them from varying. Ex. Is it teh same becuse it isa coincidence or part of procedure; may be not explicitly measured and held constant. May be held constant artifically through statistics. Based on previous research.
Operational Definition
The technique or procedure used to measure or to manipulate, and thus define, a variable in a study. Ex. anxiety (survey for symptoms, observing, must be the same)
A) Basic
B) Applied
C) Limited (Basic and applied)
A) Research that seeks fundamental knowledge about a topic. B) Seeks practical knowledge to solve a particular problem, often societal. C) Both ex. are the same ques; goals not always diff but phrased goals.
Measurement Scales
Identity, Magnitude, Equal Intervals, True Zero Point, Nominal Scale Properties, Ordinal Scale Properties, Interval Scale Properties, Ratio Scale Properties.
1) Identity
2) Magnitude
1) The values hve meaning.
2) Values have an inherent order to them such that they can be arranged in ascending or desc order.
Class ranks, the order of finish in a race, and a teacher's rankings of students on leadership ability are examples of:
Ordinal scale
The age of participants and the number of trials required to learn a list of words are examples of:
Ratio scale
Temperature (measured on a Celsius scale) and IQ scores are examples of a(n):
Inverval scale
either an interval or ratio scale of measurement will produce:
Score data
Using an ordinal scale of measurement will produce:
1) One major source of measurement error in self- report measures is:
2) The operational definition:
Response set bias (social desirability). 2) Specifies exactly how we are to measure and/or manipulate the variable.
Refers to how strongly different items or observations that make up the measure correlate with one another.
Internal consistency
((Test-retest reliability refers to stability of a measure over
time, index of the stability .))
This refers to the level at which the measure will give accurate and sensitive scores that can discriminate between people.
Effective range
It is important to remember that a measure cannot be ____ without being ____.
Valid, reliable. measure must be reliable in order for the measure to be valid.
Scale attenuation effects:
These are effects that
truncate or limit a range of measures; too high produces
a floor effect and too low produces a ceiling effect.
Driving speed measured in miles per hour as properties of ____.
Identity, magnitude, equal intervals, and a true zero.
Table 1
Frequency table of length of students' romantic relationships in months

Length Frequency Percent
1 1 10.0
2 3 30.0
Table not in italics but description is. Underline categories.
M= ( ∑ X )/ N
The statistical formula for the variance is:
SD2 = Σ (X – M) 2
The statistical formula for the standard deviation is:
The square root of variance.
Every number or category that indicates relative placement within your variable is a value. Thus the variable ‘gender’ has two values, “male” and “female”. If your variable is number of classes enrolled in this semester, you might have values that include 0, 1, 2, 3, 4, 5, and 6.
2) Frequency:
This is the particular value obtained by a participant. A woman who is taking one class would score ‘female’ on the gender variable and would score ‘1’ on the ‘classes enrolled in’ variable.
2) This is a measure of how many participants score at each of the values of your variable.
External Validity
the degree to which your study findings can be generalized to a variety of other contexts, situations, and populations
Internal Validity:
the extent to which you can attribute differences on your dependent variable (outcome) to variation in your independent variable (predictor)
Qualities of a good theory:
• Clear and parsimonious
• Supported by available data
• Helps explain occurrences
• Testable and falsifiable
a set of coherent ideas that are developed from observed information and that serve the purpose of predicting or explaining what we see or experience
Stimulus Variables:
variables in the environment, either naturally occurring or manipulated, that affect participants’ thoughts or behaviors
Descartes’ Rules of Inquiry:
1. Accept nothing as true that is not self-evident
2. Divide problems into their simplest parts
3. Solve problems by proceeding from the simple to the complex
4. Always recheck your reasoning and calculation
What is a Z score?
A Z-score is a standard way of representing how many standard deviations above or below the mean of its distribution a score falls
Converting Raw Scores to Z Scores:
Z = X – M
Converting Z Scores to Raw Scores:
X = (Z) (SD) + M
Normal Curve
- Z scores:
34% + 14 + 2 above mean (2/3 of scores will be 1 SD within mean)
- Avg of z-scores to mean = zero
- SD will be one, variance = 0
- two decimal places
Used for all measurement scales, ex. nominal data (cannot use mean or median), useful for data in whole numbers (ex. number of children), realistic value.
Interval, ratio, ordinal, but not nominal. Actual values not ocunted. Ex. income.
The importance of understanding central tendency
Normal distrib: Most people score at central value, freq found. Summary func: mode, mean, median
Honesty in graphing
Metric units to operationalize variable, intervals, scaling points on axis.
Skew to right
Direction of tail, POSITIVE SKEW, affects type of analyses to do. FLOOR effect.
Skew to left
NEGATIVE SKEW, tail towards negative numbers, CEILING effect. (Intefer with analysis)
May cause analy problems, long tails and skinny or no tails.
Rectangular distributions
Evenly across values, happen if so structured usu artifically (gender, students per year) ((Scores of var distrib in frew usu described vs. depicted in journals, usu presentations.))
Graphing: BAR
Never skip a value, may use intervals. Bar charts: label bars, descriptive axis titles, can reverse bars.
Bars touch, no lines bwtn bars, have Order, usu interval, ratio, maybe ordinal. Best with equal intervals, write midpoint of interval (1-3, under number 2 for name), no color.
Frequency graphs
Add one unit below and one unit above highest and lowest values. Even though not an option, freq zero at beginning and end.
Freq tables for exam
Table 1
Frequency Table for. . . (Ital, descrip title)
Food rating Freq Percent (underlined)
Used with any data scale, not preferable with ordinal but can break into Intervals (logically or empir). Intervals: dividing in various logical ways, ex. by 12 months for age.
Internal vs. external validity
Controlling research vs. generalizing. Ext: the degree to generalize to a variety of other contexts, may be low for STRUC research cond in labs. Int: Extent to attribute diff on var to variation in IV. May be fairly constrained but with low int val. FLAW: overly optimistic.