Statistics Exam I
Terms
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- What are sources of variability as a characteristic of phenomena?
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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?
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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?
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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?
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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?
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1. Descriptive
2. Inferential: parametric or nonparametric - What are descriptive statistics?
- These are used to classify and summarize data.
- What are inferential statistics?
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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?
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A characteristic that can take on different values for members of the group.
qualitative
quantitative
independent
dependent
intervening/confounding - Qualitative Variable
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Unordered or ordered discreet categories.
ex: enthnicity - Quantitative Variable
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Continuous data.
ex: temperature - Independent Variable
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Characteristic that the researcher controls or manipulates according to the purpose of the study.
ex: biofeedback - Dependent Variable
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A measure of the effect of the independent variable.
ex: anxiety - Intervening/Confounding Variable
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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?
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A subset of the specified population.
Random
Non-random - What is a random sample?
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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?
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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?
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Degree of precision.
Nominal
Ordinal
Interval
Ratio - What is the nominal level of measurement?
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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?
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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?
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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?
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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?
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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?
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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:
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Pre-analysis Phase
Preliminary Assessments
Preliminary Actions
Principle Analysis
Interpretive Phase - Analyzing Quantitative Data: Pre-analysis Phase
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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
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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
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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
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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
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1. Addressing the research questions
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