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Research Methods Exam 1
Terms in this set (64)
Four Traditions of Geography
Spatial, Area Studies, Human-Land, Earth Science
Location Analysis - Position, Distance, Direction
Area Studies Tradtion
Regions - Compare similarities and differences
Humankind's relationship to environment - Resource management
Earth Science Tradition
Physical Earth - Lithosphere, Hydrosphere, Atmosphere, Biosphere
WHAT IS GEOGRAPHY?
Spatial science attempting to explain and predict spatial distribution and variation of human activities and physical features on earth's surface - anything distributed or diffused across a wide variety of physical and cultural landscapes...
Four Major Steps of Geographic Inquiry
1) Collect - 2) Classify - 3) Analyze - 4) Present
Results of (n, number of) Observations or Cases
Data Values -> Data Sets -> Tables
Quantitative or Qualitative information collected by observing, counting, or measuring variables
Things that change or vary that can be counted or measured
Quantitative Methods (Research Methods)
A collection of standard practices and statistical procedures used to collect, classify, analyze and present data in order to answer questions, solve problems and provide evidence to support a particular point-of-view.
Three Forms of Communication for Geographers
Words/Literacy - Numbers/Numeracy - Images/Graphicacy
"Please Excuse My Dear Aunt Sally" - Order of mathematical operations - 1) Parentheses - 2) Exponents - 3) Multiplication - 4) Division - 5) Addition - 6) Subtraction
What is science?
Science is a planned, systematic enterprise that builds and organizes empirical and theoretical knowledge in the form of testable explanations (hypotheses) about nature and the universe.
What is the Scientific Method?
A body of techniques used to investigate natural and cultural phenomena for the purpose of acquiring new knowledge as well as correcting and integrating previous knowledge.
Things you know as a function of direct experience.
Things you know and consider "real" because you have been told they are real.
Traditional Truths - Agreement Reality
Things everybody knows.
Authoritative Truths - Agreement Reality
Things told by experts, authority figures, publications.
Scientific Observation (Methodology)
A conscious, planned activity - observing and attempting to interpret the world around us. CASUAL observations are most often unplanned.
scientifically observed and measured
casually overheard - "circumstantial"
Methods for Collecting Data
1) Experimental - 2) Survey - 3) Field Research - 4) Secondary Analysis - 5) Content Analysis
What are some potential "errors in knowing?"
Inaccurate observation, Overgeneralization, Selective Observation, Made-Up information, Illogical reasoning, Ego involvement, Missing data, Premature closing of inquiry, Mystification, To err is Human
Experimental Group vs. Control Group - rain guage, streamflow guage
Questionnaire / Interview
Questionnaire Guidelines - Utilize short questions, Open/Close Ended, Avoid negative & double-barreled questions
Requires standardized methodology - example: recreational use of SM River (counting tubers and boaters)
Evaluating data collected by someone else - GIS - Census Data
Content from newspapers, journals, television, etc.
Is Kansas flatter than a pancake?
Comparing Kansas and a pancake on the same scale, Kansas is indeed flatter than the pancake.
Using Secondary Analysis, what did the researcher determine about electrical blackouts and birthrates?
There is NO statistically significant change between birthrates corresponding to electrical blackouts and non-blackouts.
Using Field Research, what was found about recreational usage of the San Marcos River?
"By the time the summer season is in full swing...traffic on the lower river will be more than double that in town"
EXACTNESS - a rain gauge that measures in hundreds of an inch is more precise than one that measures in tenths of an inch.
EXTENT OF SYSTEM-WIDE BIAS - Is the rain gauge located under a tree or out in the open?
Are we measuring what we think we are measuring? - Are exam scores a valid measure of knowledge? Is the rain gauge measuring only precipitation or does it also measure an irrigation sprinkler's output?
CONSISTENCY & STABILITY - Can we make these same measurements repeatedly with the same results? Can another scientist make the same measurements with the same results? REPUTABILITY is one of the cornerstones of science
Levels of Measurement
NOMINAL - ORDINAL - SCALE
(Qualitative) (Categorical) (Least amount of detail) - Provides an indication that there is a DIFFERENCE between categories of observations - Pass vs. Fail - Categories must be MUTUALLY EXCLUSIVE (no observation can fit into more than one category) & EXHAUSTIVE (there must be a category to contain each and every observation)
Only TWO possibilities - Yes or No - True of False
(Transitions from Qualitative to Quantitative) (STRONGLY ORDERED vs. WEAKLY ORDERED) - Provides an indication that there is a DIFFERENCE and an ORDER and a DIRECTION to our observations, but we don't know the exact distance between observations or cases - Letter Grades: A, B, C, D, F (We don't know if "A" is 90% or 100%)
Strongly Ordered - Ordinal Level
(Rank Ordered Data) - example: yearly water inflow ranked by volume
Weakly Ordered - Ordinal Level
(Aggregated Data) - generalized data - Chloropleth Maps
(Quantitative) - INTERVAL or RATIO - Provides an indication that there is a DIFFERENCE, ORDER, a DIRECTION, and THE DISTANCE between cases or observations. We CAN use arithmetic with scale-level data.
INTERVAL ~ No real zero (Temperatures)
RATIO ~ True zero (miles per hour)
Explicitly Spatial Data
The location of the observations are themselves directly analyzed.
Implicitly Spatial Data
The locational pattern of observations is NOT directly analyzed.
Variables that have limits on the values the which the variables can take - resulting from counting or tabulating - WHOLE INTEGERS
Variables can be expressed in decimals - resulting from a measurement
Nominal Level - Geographic Examples
Soil Colors - brown, black, red
Regions - north, south, east, west
Transportation Modes - car, bus, light-rail, walk, etc.
Land Use Zoning - residential, commercial, industrial
Ordinal Level - Geographic Examples
Fujita Scale - Tornado intensity
Richter Scale - earthquake severity
Moh's Hardness Scale
Grouped Spatial Data
Likert Scale Responses
Scale Level - Geographic Examples
Distance in miles
Temperature in degrees
Elevation in feet
Public parks in acres
Spring discharge in gallons per minute
Attempting to draw conclusions about individuals from grouped or aggregated data - Attempting to go from Nominal to Ordinal, Nominal to Scale, or Ordinal to Scale - Attempting to go from Less Detail to More Detail
What is the problem with 'averaging' Likert Scale data? How can Likert Scale data be presented to avoid this problem?
Conservative results occur when Likert Scale data is averaged which takes away the "very good" and "very bad" responses on the ends of the spectrum. Representing Likert Scale data as percentages better illustrates the realities of the data
Requires that data values be consistent with measurement precision. SPURIOUS PRECISION results in induced accuracy of results that did not exist when the data was collected (i.e. too many decimals)
Straight forward method to add meaning and quantitative measure to NOMINAL categorical data
Allow for the presentation of data in a more clear format than simple lists
Frequency Distribution Tables
Special tables where a presentation of how many times each "X" occurs in a data set (arranged in descending rank order)
Coding variable responses with a number so that statistical calculation software can process the data more efficiently
getting rid of values outside of the predetermined limits - removing values may require narrative explanation
Classifying scale level data into ordinal level intervals for the purpose of displaying on a chloropleth map
Data Classification Strategies
1) Equal Interval Based on Range - 2) Equal Interval NOT Based on Range - 3) Quantile Breaks - 4) Natural Breaks
Equal Interval Based on Range
Interval 'anchored' to either a minimum or maximum value of the data set - The range of data is divided by the number of intervals desired to determine the width of each interval
Equal Interval NOT Based on Range
Interval NOT 'anchored' to a minimum or maximum value of the data set - The range of data is divided by the number of intervals desired to determine the width of each interval
Classification where each interval contains an equal number of data values
Quartile ~ 4 intervals
Quintile ~ 5 intervals
Natural breaks in the data set determine the classification intervals
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