Quantitative Analysis in Geography: Probability, Statistics, and Data, Study notes of Geography

Download Quantitative Analysis in Geography: Probability, Statistics, and Data and more Study notes Geography in PDF only on Docsity! 1 Instructor: Rama Prasada Mohapatra Department of Geography University of Wisconsin-Milwaukee email: [email protected] Quantitative Analysis in Geography University of Wisconsin-Milwaukee Geography 247 Quantitative Analysis in Geography Fall 2008 Week 2: The Nature of Probability and Statistics Quantitative Analysis in Geography University of Wisconsin-Milwaukee 1. Introduction 2. Observational and Experimental studies 3. Variables and Types of Data 4. Data collection and Sampling Techniques 5. Descriptive and Inferential Statistics 6. Uses and Misuses of Statistics Outline Quantitative Analysis in Geography University of Wisconsin-Milwaukee • Meaning • The methodology for collecting, analyzing, and presenting data. • Users • Used in various fields: medicine, engineering, natural sciences, biological sciences, business, education, sports, public health, operations research, quality control, social sciences (including geography) etc 1. Introduction Statistics Quantitative Analysis in Geography University of Wisconsin-Milwaukee • Definition • Statistics is the science of conducting studies to collect, organize, summarize, analyze, and draw conclusions from data. • Statistical data collection • Statistical data is collected by individuals, government agencies, non-profit organizations, and businesses 1. Introduction Statistics: Definition Quantitative Analysis in Geography University of Wisconsin-Milwaukee • Example (distance to school versus travel time) • Data • Distance from living place to school • Travel time • Representation of data • Scatter diagram: distance on x axis, time on y axis 1. Introduction Statistics in Geographical Research Quantitative Analysis in Geography University of Wisconsin-Milwaukee • Summarize the findings of studies • Example: total parking space available on campus • Understanding of the phenomenon under study • Example: on campus parking is costly • Forecast the state of variables • Example: parking space demand in and around the campus • Evaluate performance of certain activity • Example: UPASS ridership • Decision making • Example: finding out the best location for a new parking lot 1. Introduction Why statistical analysis? 2 Quantitative Analysis in Geography University of Wisconsin-Milwaukee 2. Observational and Experimental studies There are several ways to classify statistical studies. Here, we will discuss about two type of studies 1. Observational studies 2. Experimental studies Quantitative Analysis in Geography University of Wisconsin-Milwaukee • Observational study • In such studies, the researcher merely observes what is happening or what has happened in the past and tries to draw conclusions based on these observations. • Example • Most of the undergraduates majoring in Geography continue their studies to complete Master’s degree. 2. Observational and Experimental studies 2. Observational and Experimental studies Quantitative Analysis in Geography University of Wisconsin-Milwaukee • Experimental study • In such studies, the researcher manipulates one of the variables and tries to determine how the manipulation influences other variables. • Example • Knowledge of GIS helps the undergraduates majoring in Geography to secure a job of high beginning salary. 2. Observational and Experimental studies Quantitative Analysis in Geography University of Wisconsin-Milwaukee Comparing salary (Experimental study) Geography undergraduate students With GIS knowledge Without GIS knowledge Treatment group Control Group Students were divided into two groups by random assignment 2. Observational and Experimental studies Quantitative Analysis in Geography University of Wisconsin-Milwaukee Comparing salary (Experimental study) • Treatment group • In this group the students are subjected to treatment (GIS knowledge) • Control Group • The students are not subjected to treatment It is assumed that all other factors influencing beginning salary of students are same for both the groups. 2. Observational and Experimental studies Quantitative Analysis in Geography University of Wisconsin-Milwaukee • Advantages of observational study • It usually occurs in natural settings • Helpful when it is dangerous or unethical to conduct an experiment • Variables are not controlled by researcher • Disadvantages of observational study • Cause-and-effect situation cannot be shown • Accuracy of data • It may be expensive and time consuming 5 4. Data collection and sampling techniques Quantitative Analysis in Geography University of Wisconsin-Milwaukee • Already available data • Census, MPROP (land and building data of city of Milwaukee) etc • Data creation • Experiment • Survey • Personal interview • Telephone survey • Mailed questionnaire Source of data Quantitative Analysis in Geography University of Wisconsin-Milwaukee • Personal interview • Advantage of obtaining in-depth responses to questions from person being interviewed • Interviewer may be biased • Interviewers must be trained • Costly and time consuming 4. Data collection and sampling techniques Survey types Quantitative Analysis in Geography University of Wisconsin-Milwaukee • Telephone survey • Less costly than personal interview • People may be more candid since there is no face-to-face contact. • People may not have access to phones or they may not be available when a call is made • Tone of the voice of the interviewer may affect 4. Data collection and sampling techniques Survey types Quantitative Analysis in Geography University of Wisconsin-Milwaukee • Mailed questionnaire • Can be used to cover wider geographical area • Less expensive to conduct when compared to other two methods • Respondents can remain anonymous if they desire • Disadvantage: low turnout, difficulty in understanding questions and inappropriate answers to questions 4. Data collection and sampling techniques Survey types Quantitative Analysis in Geography University of Wisconsin-Milwaukee • Researchers use samples to collect data as very often it is not possible to collect data for entire population. • It saves time and money • Give each subject in the population an equal likely chance of being selected 4. Data collection and sampling techniques Sampling Quantitative Analysis in Geography University of Wisconsin-Milwaukee • Population • A population consists of all subjects (human or otherwise) that are being studied. • Sample • A sample is a group of subjects selected from a population. 4. Data collection and sampling techniques Sampling 6 Quantitative Analysis in Geography University of Wisconsin-Milwaukee • Statisticians use four basic methods for sampling as described below • Random sampling • Systematic sampling • Stratified sampling • Cluster sampling 4. Data collection and sampling techniques Sampling Quantitative Analysis in Geography University of Wisconsin-Milwaukee • Random sampling • Samples are collected by chance methods or random numbers • Random numbers could be generated in Microsoft Excel • Systematic sampling • By numbering each subject and then selecting the kth subject 4. Data collection and sampling techniques Sampling Quantitative Analysis in Geography University of Wisconsin-Milwaukee • Stratified sampling • The population is divided into groups (strata) at first depending on importance of study • Then samples were selected randomly within each strata • Cluster sampling • The population is divided into groups (clusters) by some means (geographic area) • Then some clusters are randomly selected 4. Data collection and sampling techniques Sampling Quantitative Analysis in Geography University of Wisconsin-Milwaukee • Descriptive Statistics • Descriptive statistics consists of the collection, organization, summarization, and presentation of data. • Inferential Statistics • Inferential statistics consists of generalizing from samples to populations, performing estimations and hypothesis tests, determining relationships among variables, and making predictions. 5. Descriptive and inferential statistics Quantitative Analysis in Geography University of Wisconsin-Milwaukee • Suspect samples • Small number of samples • How the subjects in the sample are selected (convenience samples) • Ambiguous averages • Mean, median, mode for the same data set can differ markedly • Any one of these averages could be used to justify the researchers position • Detached statistics • “Our brand of crackers has one-third fewer calories.” 6. Uses and misuses of statistics Quantitative Analysis in Geography University of Wisconsin-Milwaukee • Implied connections • Use of words such as may, suggest, some etc. • Example: eating fish may help to reduce your cholesterol • Misleading graphs • Inappropriately drawn graphs can misrepresent data and lead to false conclusion Statistics when used properly, can be beneficial in obtaining much information but when used improperly, can lead to much information 6. Uses and misuses of statistics