# Statistics Exam I

## Terms

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What are sources of variability as a characteristic of phenomena?
True or Systematic Differences
Random Influences
What are True or Systematic Differences that are a source of variability?
These occur consistently as a result of the effect of some ocn
What are Random Influences that are a source of variability?
These occur by chance and are not consistent for all individuals under the same conditions.
What are statistics?
A set of tools and techniques that researchers use to describe and explain variability. Used to describe, organize, and interpret information or data.
What is the process of scientific inquiry?
1. define the question
2. collect information useful to answering the question
3. analyze the data in relation to the question
4. make some conclusions
What is data?
Information in numerical form that represents a characteristic. Discreet or continuous.
What is discreet data?
a finite number of values between any two points.
ex: # of kids in a household
(some analyses cannot be done with discreet data)
What is continuous data?
an infinite number of values between any two points; only limited by our capacity to measure it.
ex: temperature
How do you classify quantitative statistics?
1. Descriptive
2. Inferential: parametric or nonparametric
What are descriptive statistics?
These are used to classify and summarize data.
What are inferential statistics?
These are used to draw conclusions about a large group (population) by analyzing data from a small group (sample).
parametric and nonparametric
What are parametric statistics?
These are tests that attempt to test conclusions about a population using data from a sample and/or tests that make assumptions about the distribution of variables in a population.
What are nonparametric statistics (distribution free)?
Tests that DO NOT attempt to test conclusions about a population or make assumptions about the distribution of variables in a population.
What is a constant?
A characteristic that takes the same value for every member of the group.
What is a variable?
A characteristic that can take on different values for members of the group.
qualitative
quantitative
independent
dependent
intervening/confounding
Qualitative Variable
Unordered or ordered discreet categories.
ex: enthnicity
Quantitative Variable
Continuous data.
ex: temperature
Independent Variable
Characteristic that the researcher controls or manipulates according to the purpose of the study.
ex: biofeedback
Dependent Variable
A measure of the effect of the independent variable.
ex: anxiety
Intervening/Confounding Variable
Characteristic not of primary interest that affects the dependent variable.
ex: pre-existing levels of stress - effects anxiety level
***Control this with random sampling
What is a population?
All members of a specified group; can be relatively small or infiniety large; seldom is data collected on all members.
What is a target population?
The group to which the researcher would like to apply or generalize the study conclusions.
what is an accessible population?
The entire group that is available to the researcher for inclusion in the study.
What is a sample?
A subset of the specified population.
Random
Non-random
What is a random sample?
Every member of the population has ana equal chance of being included in the sample. Also known as probability sampling.
***process will address confounding variables you have not yet thought of.
What is a nonrandom sample?
Every member of the population does not have an equal chance of being included in the sample.
What is a parameter?
A measure of a population.
What is a statistic?
A measure of a sample.
*** We look at statistics in order to draw conclusions about a parameter and therefore, the population.
What is measurement?
The process of assigning numbers to charaacteristics according to a defined rule.
What are levels of measurement?
Degree of precision.
Nominal
Ordinal
Interval
Ratio
What is the nominal level of measurement?
This classifies objects into mutually exclusive categories based on some defined characteristic with no logical ordering to the categories.
-the most imprecise level
-used with discreet data
ex: M/F; marital status
What is the ordinal level of measurement?
This classifies objects into mutally exclusive categories based on some defined characteristic and the relative amount of that characteristic with a logical order to the categories.
-used with discreet data
ex: age, frequency of exercise
What is the interval level of measurement?
This classifies objects into mutually exclusive categories based on some defined characteristic and the relative amount of that characteristic with a logical order to the categories and equal units (distance) of difference for any point on the scale.
-used with continuous data
ex: temperature, distance
What is the ratio level of measurement?
This classifies objects into mutually exclusive categories based on some defined characteristic and the relative amount of that characteristic with a logical order to the categories and equal units (distance) of difference for any point on the scale, and a meaningful sero point representing the absence of the characteristic.
-used with continuous data
ex: temp.ËšK, income, calories
Why is descriptive statistics used?
To describe the extent of a characteristic in a sample.
To examine how the characteristic in the sample is distributed (central tendency, dispersion)
To determine if there is a relationship between variables in the sample.
-usually communicated via narrative, tables, and/or graphs.
Why is inferential statistics used?
To determine if there is a relationship between variable X and variable Y in a population.
TO describe what type of relationship exists between variable X and variable Y in the population.
To examine how strong the relationship is between variable X and variable Y in the population.
Analyzing Quantitative Data consists of:
Pre-analysis Phase
Preliminary Assessments
Preliminary Actions
Principle Analysis
Interpretive Phase
Analyzing Quantitative Data: Pre-analysis Phase
1. Data coding - deciding how to enter data in the computer; assigning numbers to represent data.
2. Data entry - putting in the numbers.
3. Data inspection - examining the data for unusual values/errors.
4. Data cleaning - making decisions about how to correct data errors and what to do with unusual values.
Analyzing Quantitative Data: Preliminary Assessments
1. Statistical assumptions - is this data consistent with the assumptions that make the mathematical model function correctly in the analysis?
2. Missing data - how much/how will it effect?
3. Data quality - is it useful in answering research questions?
4. Bias - is there any - in collection or entry?
Analyzing Quantitative Data: Preliminary Actions
1. Recodes - changing numbers that were initially assigned to subjects'responses on a specific variable.
2. Transformations - mathematical changes applied to data that allow it to be more consistent w/assumptions made for a given type of analysis.
3. Missing data - substitute values based on statistical rules.
4. Scale composites - calculate total scores for a set of items.
Analyzing Quantitative Data: Principle Analayses
1. Descriptive - describe the sample characteristics; describe the variable characteristics.
2. Inferential - bivariate (2 variables), multivariate (2 or more variables), post hoc (follow-up analyses).
Analyzing Quantitative Data: Interpretation
2. Integrate
3. synthesize
4. Supplementary analyses
5. Evaluation of measurement tools
Statistics that refer to populations are...
designated by Greek letters.
Statistics that refer to samples are...
designated by Roman letters.
population mean
µ
population variance
sigma (lowecase) squared
population std. dev.
sigma (lowercase)
population correlation
p (rho)
sample mean
x bar
sample variance
s squared
sample std. dev.
s
sample correlation
r

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