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Statistics
A set of mathematical procedures for organizing , summarizing, and interpreting information

Population:
A group of two or more individuals or things that share one or more common characteristics

Sample:
A subgroup of two or more individuals or things from a population

Representative Sample
A subgroup of two or more individuals or things randomly and independently selected * from a population
· Randomly and independently selected means each member of the population has an equal opportunity of being included in the sample Parameter
Usually a numerical value, that describes a population.

Relationship between a population and sample (graph: slide 4)

Statistic
A value, usually a numerical value that describes a sample.

Data
 measurements or observations
 Descriptive Statistics
 Statistical procedures used to summarize, organize and simplify data.
 Inferential Statistics
 Techniques that allow us to study samples and then make generalization about the population from which they were selected.

Sampling error
The discrepancy, or amount of error, that exists between a sample statistic and the corresponding population parameter

Variable and Constant
Variable: A characteristic or condition that changes or has different values for different individuals
Constant: A characteristic or condition that does not vary but is the same for every individual.

Correlational Research:
 Observing naturally occurring phenomena
 · Naturalistic observation
 · Archival research
 · Case histories
 · Surveys
 > does not equal causality
Is variable X associated with variable Y?
 ex: Is watching WWE related to aggressive behavior in children?
 –How can we describe this relationship?

Correlational Research: Advantages
 – A good place to start & explore (especially if relevant theory is lacking)
 – Often cheapest & easiest option
 – Can look at more variables simultaneously / greater realism
Fewer ethical issues…

Experimental Research: Manipulation & Measurement
experimenter bias
  Independent (manipulated) variables
 – Dependent (measured) variables
 – Does manipulating IV “X” cause changes in DV “Y?”
 – Example: Does assigning some children to watch WWE cause them to behave more aggressively than other children?
 Experimenter bias, for example
 • Affects treatments
 • Affects measurements

Experimental Research: Limitations
  Often harder, more time consuming, &/or expensive
 – Some variables can’t be manipulated
 – Difficult to control for all extraneous variables (hold them all constant)
 – Difficult to make the experimental situation realistic
 – Procedural mistakes or flawed sampling can make findings useless

Greater ethical obligations
 – Some variables shouldn’t be manipulated, or only with great caution
 Repeat as necessary to build, refine, or discard theory
 – Theories allow us to generate testable hypotheses
 – When hypotheses are supported by evidence, the theory is considered the best explanation so far
 When hypotheses are not supported, the theory is refined or discarded

Role of statistics in experimental research (table: slide 14)

Criteria for evaluating evidence:
 Observations must be
 – Public
 – Replicable
 • Can be repeated by others using same procedures; Reliable
 • Consistent across measurements &/or observers

Hypothetical results from a correlational study (table: slide 16)

Operational Definitions
– Defining a construct in terms of the operation(s) used to measure it Ways to measure fear? attraction?
 Poor operational definitions>bad research / misleading results
 – Problems with reliability of observations
 – Problems with interpretation of results

Independent and Dependent Variables
Independent variable: The variable that is manipulated by the researcher. Independent variable consists of the antecedent condition that were manipulated prior to observing the dependent variable.
Dependent variable: The variable that is observed in order to assess the effect of the treatment.

Control condition
Experimental condition
Confounding variable
Control condition: Individuals do not receive experimental treatment.
Experimental condition: individuals receive experimental treatment.
Confounding variable: An uncontrolled variable that is unintentionally allowed to vary systematically with the independent variable.

An example of a confounding variable (Instructor: slide 21)

Discrete vs. Continuous Variables (table 23)
 Discrete: each item corresponds to a separate value of the variable
 Values/categories do NOT overlap or “touch” on the scale.There are no values “in between”
 Continuous: each item corresponds to an interval on the scale of measurement. Intervals defined by upper & lower real limits
 Real limits are continuous (“they touch”)

Properties of scales of measurement:
4 Types of scales:
 Each scale has all the properties of the ones below it plus an additional property.T
 he higherlevel measurements contain more detailed information about observations & allow more complex analyses.
Types of scales: Nominal, Ordinal, Interval, Ratio

Nominal Scale
 Identification (Name): allows you to label observations.
 Applies to category labels & numbers used as labels.
 Examples: college major, any “yes/no,” participant number, etc…

Ordinal Scale
Magnitude (Order): allows you to make statements about relative size or ordering/ranking of observations.
Applies to ordered category labels & numbers used as ranks.
Examples: any “high/medium/low,” class rank, etc…

Interval Scale
Equal Intervals: allows you to assume that the distances between numbers on the measurement scale are equal & correspond to equal differences in the variable being measured.
Applies to numbers, often scores or ratings.
Examples: attitude as preference ratings, etc...

Ratio Scale
Absolute Zero: allows you to assume that a score of “0” on a variable really means the absence of that property, & that you can make meaningful ratio statements.
Applies to numbers, often tallies or physical measurements.
Examples: stress as change in BP, memory performance as # of words recalled, etc...

Frequency distribution table:
shows a range of possible values for a single variable (X) & the number of observations of each value (f).
Displaying our observations: Frequency distribution tables & graphs of frequency distributions

Nominal data
Example: X =gender of class members (1 = male; 2 = female)

Rank or percentile rank
A particular score is defined as the percentage of individuals in the distribution with scores at or below the particular value.Calculating cumulative frequencies (cf) & cumulative percentages (cum%)
cf = # of observations at or below a given value of X add up frequencies from bottom of table upwards
cum(cumulative)% = percentage of observations at or below a given value of X divide cf/N for each row (better—less rounding error)OR add up percentages from bottom of table upwards

Characteristics of distributions
Symmetry vs. skewness, number of modes or “pileups”

The Normal Distribution
 mean = median = mode
 symmetrical
 Many complexlydetermined traits are normally distributed
 e.g. IQ & SAT scores.

A symmetrical bimodal distribution
mean = median, with 2 modes
Bimodal distributions may also be asymmetrical (mean, median), & multimodal distributions are possible.

A positively skewed distribution
(tail > positive end of scale)Mode<median<mean

A negatively skewed distribution
 (tail >negative end of scale)
 Mean<median<mode

Skewed Distributions Image

