8: Research Methods / Statistics Flashcards
What is ratio data?
Ratio data is the gold standard of measurment, where both absolute and relative differences have a meaning. An example would be distance measure.
Dif between 40 and 30 miles is the same as the dif between 30 and 20 miles. AND 40 miles is twice as far as 20 miles.
Nominal Data
This type of measurement is classified into mutually exculsive groups or categories and lack intristic order.
(examples: zoning classification, social security number).
label of categories does not imply any order.
Hypothesis test
This type of test is designed to reject a null hypothesis, but never to accept the alternative hypothesis.
Symptomatic Method
Uses data sets such as building permits that are reflective of populaAnation change and can be used to estimate current development population estimates.
Systematic random sample
Equal chance of being selected, every Xth person is surveyed
What is the probability of an event that is certain to happen?
1 - probabilities range from 0 to 1
Total acreage of federal indian reservations in the U.S.
56.2 million
What is a positive correlation?
When the high scores on one variable are associated with a high score on a second variable
Analysis of the relationship between two variables
Regression analysis
Total acreage of national forest land in the U.S.
192 million
Difference between the lowest and highest score on an exam
Range
Coefficient of Correlation
Measures the degree to which two variables are related
Stratified Sample
Subdivide the population into at least two different subgroups that share the same characteristics, then draw a sample from each subgroup.
SYSTEMATIC stratified sample represents the most effective way to get an accurate cross-section of the local population.
Qualitative V Quantitative V Mixed Methods
Qualitative: Approach for understanding the meaning individuals and groups ascribe to a human or social problem. Emerging questions.
Quantitative: Approach for testing objective theories by examining the relationships among variables (deductive). Nucmbered data.
Mixed methods: Collection of both qualitative & quantitative data. Integrating the 2 forms of data.
Discourse Analysis
Study of the way versions of the world, society, events, and psyche are produced in the use of language and discourse. It is often concerned with the construction of subjects within various forms of knowledge / power.
EXAMPLES: Semiotics, deconstruction, narrative analysis
Ethnography
Multi-method qualitative approach - studies people in their “naturally occurring settings or “fields” by means of methods which capture their social meanings and ordinary activities.
Grounded Theory
Inductive form of qualitative research where data collection & analysis are conducted together. Theories remain grounded in the observations rather than generated in the abstract.
Approach that develops the theory from the data collected rather than applying a theory to the data.
Narrative Analysis
Form of discourse analysis that seeks to study the textual devices at work in the constructions of process or sequence within a text. Tells researcher about the meaning of events in their lives.
3 steps to a statistical process:
1- Collect Data
2- Describe & Summarize the distribution of values in the data set.
3- Interpret by means of inferential stats & stat modeling.
Ordinal Data
Ordered categories which implies a ranking of the observations. Even though ordinal data may be given numeric values (example 1,2,3,4) - values are meaningless.
Example: Letter grades, suitability for development, response scales on a survey.
Interval data
Ordered relationship where the difference between the scales has a meaningful interpretation. Typical example = temperature.
Dif between 40 & 30 degrees, same as dif between 30 & 20 degrees - but 20 degrees is NOT twice as cold as 40 degrees.
Continuous variables
Can take an infinite number of values, both positive and negative, & with as fine of a degree of precision as desired.
Discrete variables
Can only take a finite number of distinct values. Example = count of the number of events, such as the number of accidents per month. Cannot be negative, can only take on integer values.
Binary or dichotomous variables = can only take on 2 values coded as 0 and as 1.
Population
Totality of some entity
Sample
Subset of the population.
Descriptive Statistics
Describes the characteristics of the distribution of values in a population or in a sample.
For example - descriptive stat such as the mean could be applied to the age distribution in the population of AICP exam takers. On average, test takers are 30 years old.
Inferential statistics
Use probability theory to determine characteristics of a population based on observations made on a sample from that population.
We infer things about the population based on what is observed in the sample.
Example - sample of 25 test takers and use their average age to say something about the mean age of all the test takers.
Distribution
Overall shape of all observed data.
Can be listed as an ordered table or graphically represented by a histogram or density plot.
HISTOGRAM: groups observations in bins represented in bar chart.
DENSITY PLOT: Shows a smooth curve.
Characteristics are summarized by descriptive statistics: like central tendency, dispersion, symmetry or lack thereof (skewness) & presence of thick tails aka higher likelihood of extreme values (kurtosis).
