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52 Cards in this Set
- Front
- Back
What are sources of variability as a characteristic of phenomena?
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True or Systematic Differences
Random Influences |
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What are True or Systematic Differences that are a source of variability?
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These occur consistently as a result of the effect of some ocn
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What are Random Influences that are a source of variability?
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These occur by chance and are not consistent for all individuals under the same conditions.
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What are statistics?
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A set of tools and techniques that researchers use to describe and explain variability. Used to describe, organize, and interpret information or data.
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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 |
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What is data?
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Information in numerical form that represents a characteristic. Discreet or continuous.
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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) |
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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 |
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How do you classify quantitative statistics?
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1. Descriptive
2. Inferential: parametric or nonparametric |
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What are descriptive statistics?
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These are used to classify and summarize data.
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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 |
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What are parametric statistics?
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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.
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What are nonparametric statistics (distribution free)?
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Tests that DO NOT attempt to test conclusions about a population or make assumptions about the distribution of variables in a population.
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What is a constant?
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A characteristic that takes the same value for every member of the group.
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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 |
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Qualitative Variable
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Unordered or ordered discreet categories.
ex: enthnicity |
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Quantitative Variable
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Continuous data.
ex: temperature |
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Independent Variable
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Characteristic that the researcher controls or manipulates according to the purpose of the study.
ex: biofeedback |
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Dependent Variable
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A measure of the effect of the independent variable.
ex: anxiety |
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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 |
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What is a population?
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All members of a specified group; can be relatively small or infiniety large; seldom is data collected on all members.
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What is a target population?
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The group to which the researcher would like to apply or generalize the study conclusions.
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what is an accessible population?
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The entire group that is available to the researcher for inclusion in the study.
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What is a sample?
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A subset of the specified population.
Random Non-random |
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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. |
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What is a nonrandom sample?
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Every member of the population does not have an equal chance of being included in the sample.
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What is a parameter?
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A measure of a population.
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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. |
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What is measurement?
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The process of assigning numbers to charaacteristics according to a defined rule.
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What are levels of measurement?
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Degree of precision.
Nominal Ordinal Interval Ratio |
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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 |
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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 |
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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 |
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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.˚K, income, calories |
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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. |
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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. |
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Analyzing Quantitative Data consists of:
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Pre-analysis Phase
Preliminary Assessments Preliminary Actions Principle Analysis Interpretive Phase |
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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. |
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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? |
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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. |
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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). |
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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 |
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Statistics that refer to populations are...
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designated by Greek letters.
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Statistics that refer to samples are...
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designated by Roman letters.
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population mean
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µ
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population variance
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sigma (lowecase) squared
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population std. dev.
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sigma (lowercase)
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population correlation
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p (rho)
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sample mean
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x bar
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sample variance
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s squared
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sample std. dev.
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s
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sample correlation
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r
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