An Introduction to Scientific Research Methods in Geography

1. An Introduction to Scientific Research Methods in Geography Daniel R. Montello and Paul C. Sutton Fundamental Research Concepts Chapter 2 Summary

2. Learning Objectives • Know the idea concepts and empirical concepts in science • Define causality and its role in scientific inquiry • Know the four levels of measurement • Define scale with regard to geography • Good ways to systematically generate research ideas

3. Overview • Idea Concepts • Empirical Concepts • Concept of Scale (in geography) • Generating Research Ideas • All are fundamental to the conduct and interpretation of scientific research

4. Idea Concepts • Examples: theory, law, hypothesis, causality, model and construct • Theory • Central idea concept • An idea or conjecture about a casual relationship in reality • Answers question of “Why” by identifying causes • Authors define it narrowly in order to recognize explanation as the goal for scientific research • Hypothesis • A conjecture about a pattern of observations of the world • Refers to a testable idea • “If theory A is true, then one hypothesizes that data pattern B will hold”

5. Idea Concepts • Causality • Causality is complex and is accepted by most scientists as an important concept • The apparent fact that event A (cause) is a reason that event B (effect) occurred • Basically, the occurrence of event B depends in some way on the occurrence of event A • David Hume’s 3 principles • They co-occur (covariation) • The cause comes first (temporal precedence) • Controlling the cause controls the effect • Discussion on causality • Is causality in the mind or in the world? • Can causality occur simultaneously between two spatially separated entities? • Can causality move backward in time? • It is probabilistic rather than deterministic • Causes probably bring about effects, but it is not definite every time

6. Idea Concepts • Causality cont. • There are necessary and sufficient causes • A necessary cause is required for the effect to occur, but it doesn’t have to occur every time • A sufficient cause can cause the effect, but something else could cause it as well • Ex: drought & wildfires • Mechanistic and functional causality • Mechanistic causality is the idea that causes move forward “densely” in space and time (ex., light switch) • Functional causality is the idea that places the cause after the effect by seeing the cause as functional or purposeful; cause is seen as a goal (ex., evolution)

7. Idea Concepts • Model • A simplified representation of a portion of reality expressed in conceptual, physical, graphical or computational form • Ex: Huff model shows gravity model in economic geography showing store choice of consumers based on attractiveness and distance of stores in comparison to one another • Construct • What we attempt to measure; a scientific concept; elementary component within a theory • Ex: a table has length (construct) and we try to measure it, but it will always be an imperfect reflection of the construct • Latent and Manifest variables • Latent variables are what we try to measure; they are the constructs (ex., intelligence tests) • Manifest variables are the measurements, the imperfect reflections

8. Empirical Concepts • Examples: case, variables, measurement, measurement levels, discrete vs. continuous variables and accuracy vs. precision of measurement • Case • The thing or entity being studied such as a unit of analysis, entity, element, etc. • We don’t study cases directly, we study attributes or properties of cases (ex., mountains & cities)

9. Empirical Concepts • Variables • The properties studied within a case that change over time and depending on the case; they take on multiple values across cases • Constants stay the same measurement; the process of how we observe and determine values • Dichotomous variable is the simplest variable possible, having two values

10. Empirical Concepts • Measurement • Assigning numbers to cases to reflect their values on a variable • Data refers to the measured numbers • Measurement Level (Hierarchy): • Nominal: assigning numbers to distinguish one case’s value on a variable from that of another case; classification • Ordinal: assigning numbers to distinguish the relative order or rank of the value of one case on a variable from that of another case; ranking; doesn’t express how much more or less of a difference between rankings • Interval: expresses the ranks of cases on a variable and also the quantitative lengths of intervals between the cases; it doesn’t express a value of nothing or a true zero • Ratio: expresses lengths of intervals between cases on a variable and also the lengths of intervals relative to a true zero; comparisons can be made • *Ratio and interval measurements taken together are known as metric • expresses quantitative distances between values…important

11. Empirical Concepts • Discrete vs. Continuous Variables • Discrete variables have a limited set of distinct possible values • Continuous variables can take on an infinite number of values between any two values • Zeno’s Paradoxes, pg. 25 Box 2.1 • The distinction between discrete and continuous may seem straightforward, it is an intellectual enigma when pondered • In relation to levels of measurement there is overlap: • Nominal and ordinal variables = discrete • Interval and ratio variables = discrete or continuous • Discrete variables = any 4 levels • Continuous variables = interval or ratio

12. Empirical Concepts • Accuracy vs. Precision of Measurement • Accuracy is the correctness of measurement at a given levels of precision; how close the measured value is to the true value of what is being measured • Precision is the sharpness or resolution of a measurement; how small the units are with which a value is measured • Ex: Darts & bull’s eye figure 2.1, pg. 27 • Distance from spatial center of the darts to the bull’s eye is accuracy • The spread of the 5 darts around their centroid is precision

13. Scale in Geography • Scale is idea concept and empirical concept • Scale is about size: relative or absolute • Scale is relevant to space, time and theme • Themes are the non-spatial and non-temporal characteristics of human and natural phenomena that geographers measure and map as variables

14. Scale in Geography • Phenomenon Scale • The size of human or physical earth structures or processes actually exist (ex., lake & pond) • Analysis Scale • The size of the unit at which a problem is analyzed • Cartographic Scale • The depicted size of a feature on a map relative to actual size in real life • Hierarchy of scales means that smaller phenomena are nested within larger phenomena (ex., economies)

15. Generating Research Ideas • You can get research ideas from anywhere…be creative! • Systematic Approaches • Intensive case study • Paradoxical incident • Analogical extension • Practitioner’s rule of thumb • Account for conflicting results • Reduce complexity to simpler components • Account for exceptions to general findings • Plan of Action • Find research area of interest to you • Generate ideas on your own first • Think about knowledge you already have and is the idea plausible? • Check existing literature and ask experts • Formulate idea as one or more specific hypotheses- specific research questions • Design research to address the hypotheses