# Quantitative or Qualitative?

Save this PDF as:

Size: px
Start display at page:

## Transcription

1 2.1 Data: and Levels of Measurement 1 Quantitative or Qualitative?! Quantitative data consist of values representing counts or measurements " Variable: Year in school! Qualitative (or non-numeric) data consist of values that can be placed into nonnumeric categories. " Variable: Political affiliation (rep, dem, ind) Frequency Histogram of 'Year in School' class 2! Quantitative " Numerical values representing counts or measures. " Something we can `measure with a tool or a scale or count. " We can compare these values on a number line.! 2 pounds is less than 4 pounds " You can take a mathematical average of these values, i.e. can be used in computations.! e.g. weight! e.g. number of students in a class 3 1

2 ! Qualitative (or non-numeric) " Non-numerical in nature (but could be `coded as a number, so be careful).! e.g. low=1, med=2, high=3 (still qualitative) " Could be considered a label in some cases.! e.g. Political affiliation (dem, rep, ind)! e.g. Numbers on a baseball uniform " #90 isn t larger than #45 in the mathematical sense. They re just a label.! e.g. ID (34B, 67AA, 19G, )! e.g. Education level (HS, 2-yr, 4-yr, MS, PhD) 4! Qualitative (or non-numeric) " Can t use meaningfully in a computation! Can you take the average of the observed political affiliations? No, it s non-numerical. " Dem, Dem, Rep, Ind, Dem, Rep! e.g. ID #s 56, 213, 788, Average ID? no. " If variable is represented by numbers (as with IDs), ask yourself if an average makes sense if not, then it s qualitative not quantitative. 5! Quantitative " Number of medals won by U.S. in a given year.! Qualitative " Medal Type: Gold/Silver/Bronze Summer Olympic USA medalists

3 ! Quantitative " Number of medals won by U.S. in a single year.! Qualitative " Medal Type: Gold/Silver/Bronze " Summarized with a table or chart. Gold Silver Bronze Gold Silver Bronze Gold Silver Bronze 7! Quantitative " Number of medals won by U.S. in a given year. " Can be shown with a distribution, or summarized with an average, etc. With some reformatting of the earlier data, we can get a count of medals for each year. Year Count ! Qualitative " Medal Type: Gold/Silver/Bronze " Summarized with a table or chart. Number of medals in a year count A distribution. The x-axis shows part of the realnumber line. 8! Quantitative " Can be shown with a distribution, or summarized with an average, etc. " Commonly used summaries:! Average value! Maximum or Minimum value! Standard deviation (a measure of spread of the data) " Summarizing a distribution with a single value can be very useful. " But be aware that averaging (or pooling, or aggregating) can potentially hide some interesting information (next slide). Number of medals in a year A distribution. The x-axis shows part of the realnumber line count 9 3

4 Pooling (or Aggregating) Data Tracing the rise and fall of each country s total medal count over time All sports: Only diving: A Visual History of Which Countries Have Dominated the Summer Olympics, New York Times, Aug. 22, Qualitative Data! Qualitative (two levels of qualitative data) " Nominal level (by name)! No natural ranking or ordering of the data exists.! e.g. political affiliation (dem, rep, ind) " Ordinal level (by order)! Provides an order, but can t get a precise mathematical difference between levels. " e.g. heat (low, medium, high) " e.g. movie ratings (1-star, 2-star, etc.) # Watching two 2-star** movies isn t the same as watching one 4-star**** movie (the math not relevant here). " Could be coded numerically, so again, be careful. 11 Qualitative Data Political affiliation (dem, rep, ind) Nominal Level of pain (low, med, high) Ordinal Answer to survey: (strongly disagree, disagree, agree, strongly agree) Ordinal Eye color (blue, green, brown, etc.) Nominal 12 4

5 Levels of Measurement (Another way to characterize data) Qualitative data is either Nominal or Ordinal (only 2 options) 13 Two kinds of Quantitative Data! Continuous or Discrete? " Continuous! Can take on any value in an interval! Could have any number of decimals " e.g. weight, home value, height " Discrete π " 2.45, , 4.0,, etc.! Can take on only particular values " e.g. number of prerequisite courses (0, 1, 2, ) " e.g. number of students in a course " e.g. shoe sizes (7, 7-1/2, 8, 8-1/2, ) 14 Levels of Measurement (Another way to characterize data) Quantitative data can be either Discrete or Continuous and either Interval or Ratio 15 5

6 Quantitative Data! Interval or Ratio? " Interval level (a.k.a differences or subtraction level)! Intervals of equal length signify equal differences in the characteristic. " The difference in 90 and 100 Fahrenheit is the same as the difference between 80 and 90 Fahrenheit.! Differences make sense, but ratios do not. " 100 Fahrenheit is not twice as hot as 50 Fahrenheit.! Occurs when a numerical scale does not have a true zero start point (i.e. it has an arbitrary zero). " Zero does not signify an absence of the characteristic. " Does 0 Fahrenheit represent an absence of heat?! Designates an equal-interval ordering. " 1 to 2 has the same meaning as 3 to Quantitative Data! Interval or Ratio? " Interval level (a.k.a differences or subtraction level)! May initially look like a qualitative ordinal variable (e.g. low, med, high), but levels are quantitative in nature and the differences in levels have consistent meaning. " Scale for evaluation: See comment on slide 20. " If a change from 1 to 2 has the same strength as a 4 to 5, then we would call it an interval level measurement (if not, then it s just an ordinal qualitative measurement). " To be an interval measurement, each sequential difference should represent the same quantitative change. " But a 5 is not 5 times a 1 (ratios don t make sense here). " This could have been on a 6 to 10 scale (arbitrary start). 17 Quantitative Data! Interval or Ratio? " Interval level (a.k.a differences or subtraction level)! IQ tests (interval scale). " We don t have meaning for a 0 IQ. " A 120 IQ is not twice as intelligent as a 60 IQ.! Calendar years (interval scale). " An interval of one calendar year (2005 to 2006, 2014 to 2015) always has the same meaning. " But ratios of calendar years do not make sense because the choice of the year 0 is arbitrary and does not mean the beginning of time. " Calendar years are therefore at the interval level of measurement. 18 6

7 Quantitative Data! Interval or Ratio? " Ratio level (even more meaning than interval level)! At this level, both differences and ratios are meaningful. " Two 2 oz glasses of water IS equal to one 4 oz glass of water " 4 oz of water is twice as much as 2 oz of water.! Occurs when scale does have a true zero start point. " 0 oz of water is a true zero as it is empty, absence of water.! Ratios involve division (or multiplication) rather than addition or subtraction. 19 Quantitative Data! Quantitative Interval level example " Temperature used to cook food*. A brownie recipe calls for the brownies to be cooked at 400 degrees for 30 minutes. Would the results be the same if you cooked them at 200 degrees for 60 minutes? How about at 800 degrees for 15 minutes? 200 degrees is not half as hot as 400 degrees. The ratio of temperatures does not make sense here. 20 Quantitative Data! Quantitative - Ratio level examples " Centimeters! Difference of 40 cm (an interval) makes sense and has the same meaning anywhere along the scale.! 10cm is twice as long as 5 cm (put two 5 cm items together and they are equivalent to 10 cm). Ratios make sense.! 0cm truly represents no length or absence of length. " Mass " Length " Time 21 7

8 Likert scale (sometimes unclear)! Is it Interval (Quantitative) or Ordinal (Qualitative) scale? " I think, most of the time, these surveys are just ordering responses lowest to highest and NOT fulfilling the interval scale requirements. " Difference of opinions on this though. 22 Possible data types and levels of measure. 23 Possible data types and levels of measure. As a statistician, the type of data that I have dictates the type of analysis I will perform. 24 8

9 2.2 Dealing with Errors! Types of errors: " Random vs. Systematic errors! Size of Errors: " Absolute vs. Relative! Describing Results: " Accuracy and Precision 25 Types of Errors! Random errors: " Affects measurement in an unpredictable manner! Baby squirming on a scale! may cause error above or below truth! Introduces random noise to your measurement! Systematic errors: " Error that affects all measurements in a similar fashion! Scale systematically weighs all babies a little too high (scale needs to be calibrated). 26 Types of Errors! Random errors: " Just part of the process we have to deal with, sometimes called noise " We can measure object numerous times and take an average to reduce the effect of the random error! Systematic errors: " We may be able to remove the error if the source can be detected (e.g. recalibrating) " After data collected, can be corrected if error is detected and quantified. 27 9

10 Types of Errors Picture (a) on the left represents a baby s motion, which introduces random errors to the measurement process. Picture (b) on the right shows the scale reads 1.2 pounds when empty, introducing a systematic error that makes all measurements 1.2 pounds too high. 28 Size of Errors! Consider a clerk that made a mistake and overcharged you \$1. " What if you had just bought! 1) A \$1 piece of pie.! 2) A \$30,000 car. " Would you see the \$1 discrepancy differently?! Should we consider the mistake relative to the price? 29 Size of Errors! 1) \$1 overcharge on a \$1 piece of pie: " Absolute value of overcharge: \$ " Relative value of overcharge: or 100% 1 =1! 2) \$1 overcharge on a \$30,000 car: " Absolute value of overcharge: \$ " Relative value of overcharge: or = % 30 10

11 Size of Errors! This idea can be applied to measurement errors! Absolute errors are expressed as a difference in units! Relative errors are expressed as a ratio with the true value in the denominator and the error in the numerator 31 Size of Error: Absolute versus Relative Absolute and Relative Errors The absolute error describes how far a claimed or measured value lies from the true value: absolute error = claimed or measured value true value The relative error compares the size of the absolute error to the true value. It is often expressed as a percentage: absolute error relative error = true value x 100% Copyright 2009 Pearson Education, Inc. 32! Example: True weight is 25 pounds, but the scale reads 26.5 " Absolute error: 26.5 pounds -25 pounds = 1.5 pounds " Relative error: 1.5 pounds 25 pounds X 100% = 6% 33 11

12 Accuracy vs. Precision! If a measured value is close to the truth, it has accuracy. " We usually quantify close in relative terms rather than absolute terms.! Precision describes the amount of detail (or resolution) in a measurement. " Suppose your true salary is \$47,500! Telling someone your salary is \$49,546 sounds more precise (to a specific dollar) than saying it is \$49,000, but the \$49,000 statement is more accurate (closer to truth).! Precision doesn t necessarily coincide with accuracy. 34 Accuracy vs. Precision! Suppose that your true weight is pounds. The scale at the doctor s office, which can be read only to the nearest quarter pound, says that you weigh 102¼ pounds. The scale at the gym, which gives a digital readout to the nearest 0.1 pound, says that you weigh pounds. " Which scale is more precise? Which is more accurate? 35 Summary: Dealing with Errors Errors can occur in many ways, but generally can be classified into one of two basic types: random errors or systematic errors. Whatever the source of an error, its size can be described in two different ways: as an absolute error or as a relative error. Once a measurement is reported, we can evaluate it in terms of its accuracy and its precision. Copyright 2009 Pearson Education, Inc

### Psychology 312: Lecture 6 Scales of Measurement. Slide #1. Scales of Measurement Reliability, validity, and scales of measurement.

Psychology 312: Lecture 6 Scales of Measurement Slide #1 Scales of Measurement Reliability, validity, and scales of measurement. In this lecture we will discuss scales of measurement. Slide #2 Outline

### Chapter 3 Review Math 1030

Section A.1: Three Ways of Using Percentages Using percentages We can use percentages in three different ways: To express a fraction of something. For example, A total of 10, 000 newspaper employees, 2.6%

### Concepts of Variables. Levels of Measurement. The Four Levels of Measurement. Nominal Scale. Greg C Elvers, Ph.D.

Concepts of Variables Greg C Elvers, Ph.D. 1 Levels of Measurement When we observe and record a variable, it has characteristics that influence the type of statistical analysis that we can perform on it

### 1.1 What is Statistics?

1.1 What is Statistics? Statistics is the science that deals with the collection, analysis, and interpretation of numerical information. This science can be divided into two areas: descriptive statistics

### Descriptive Statistics and Measurement Scales

Descriptive Statistics 1 Descriptive Statistics and Measurement Scales Descriptive statistics are used to describe the basic features of the data in a study. They provide simple summaries about the sample

### Elementary Statistics

Elementary Statistics Chapter 1 Dr. Ghamsary Page 1 Elementary Statistics M. Ghamsary, Ph.D. Chap 01 1 Elementary Statistics Chapter 1 Dr. Ghamsary Page 2 Statistics: Statistics is the science of collecting,

### Chapter 1: The Nature of Probability and Statistics

Chapter 1: The Nature of Probability and Statistics Learning Objectives Upon successful completion of Chapter 1, you will have applicable knowledge of the following concepts: Statistics: An Overview and

### What Is Statistics. Chapter 1

What Is Statistics Chapter 1 Learning Objectives Explain why we study statistics. Explain what is meant by descriptive statistics and inferential statistics. Distinguish between a quantitative variable

### Introduction to Statistics

1 1.1 Introduction Introduction to Statistics Statistics is a collection of methods for planning experiments, obtaining data, and then organizing, summarizing, presenting, analyzing, interpreting and drawing

### Statistical basics for Biology: p s, alphas, and measurement scales.

334 Volume 25: Mini Workshops Statistical basics for Biology: p s, alphas, and measurement scales. Catherine Teare Ketter School of Marine Programs University of Georgia Athens Georgia 30602-3636 (706)

### A variable is the mathematical model for a characteristic observed or measured in an experiment, survey, or observational study.

Types of Variables This material is covered partially by Sall, Lehman and Creighton (2001) Chapters 1 and 2, by Milton (1992) Chapters 1 and 4, by Sokal and Rohlf (1995) Chapters 1 and 2, by Koopmans (1987),

### Basic Statistical Concepts, Research Design, & Notation

, Research Design, & Notation Variables, Scores, & Data A variable is a characteristic or condition that can change or take on different values. Most research begins with a general question about the relationship

### Sheffield Hallam University. Faculty of Health and Wellbeing Professional Development 1 Quantitative Analysis. Glossary

Sheffield Hallam University Faculty of Health and Wellbeing Professional Development 1 Quantitative Analysis Glossary 2 Using the Glossary This does not set out to tell you everything about the topics

### Content DESCRIPTIVE STATISTICS. Data & Statistic. Statistics. Example: DATA VS. STATISTIC VS. STATISTICS

Content DESCRIPTIVE STATISTICS Dr Najib Majdi bin Yaacob MD, MPH, DrPH (Epidemiology) USM Unit of Biostatistics & Research Methodology School of Medical Sciences Universiti Sains Malaysia. Introduction

### Presentation of data

2 Presentation of data Using various types of graph and chart to illustrate data visually In this chapter we are going to investigate some basic elements of data presentation. We shall look at ways in

### F. Farrokhyar, MPhil, PhD, PDoc

Learning objectives Descriptive Statistics F. Farrokhyar, MPhil, PhD, PDoc To recognize different types of variables To learn how to appropriately explore your data How to display data using graphs How

### Statistical Foundations: Measurement Scales. Psychology 790 Lecture #1 8/24/2006

Statistical Foundations: Measurement Scales Psychology 790 Lecture #1 8/24/2006 Today s Lecture Measurement What is always assumed. What we can say when we assign numbers to phenomena. Implications for

### TYPES OF DATA TYPES OF VARIABLES

TYPES OF DATA Univariate data Examines the distribution features of one variable. Bivariate data Explores the relationship between two variables. Univariate and bivariate analysis will be revised separately.

### Fall 2010 Practice Exam 1 Without Essay

Class: Date: Fall 2010 Practice Exam 1 Without Essay Multiple Choice Identify the choice that best completes the statement or answers the question. 1. A researcher wants to visually display the U.S. divorce

### Levels of measurement

Levels of measurement What numerical data actually means and what we can do with it depends on what the numbers represent. Numbers can be grouped into 4 types or levels: nominal, ordinal, interval, and

### Levels of measurement in psychological research:

Research Skills: Levels of Measurement. Graham Hole, February 2011 Page 1 Levels of measurement in psychological research: Psychology is a science. As such it generally involves objective measurement of

### POLI 300 Handout #3 N. R. Miller VARIABLES. 1. Variables and the Unit of Analysis

POLI 300 Handout #3 N. R. Miller VARIABLES 1. Variables and the Unit of Analysis Variables are characteristics of the things that we are studying, commonly called cases. The kind of thing that is being

### Statistics Notes Revision in Maths Week

Statistics Notes Revision in Maths Week 1 Section - Producing Data 1.1 Introduction Statistics is the science that studies the collection and interpretation of numerical data. Statistics divides the study

### Research Design Concepts. Independent and dependent variables Data types Sampling Validity and reliability

Research Design Concepts Independent and dependent variables Data types Sampling Validity and reliability Research Design Action plan for carrying out research How the research will be conducted to investigate

### SOST 201 September 18-20, 2006. Measurement of Variables 2

1 Social Studies 201 September 18-20, 2006 Measurement of variables See text, chapter 3, pp. 61-86. These notes and Chapter 3 of the text examine ways of measuring variables in order to describe members

### Central Tendency. n Measures of Central Tendency: n Mean. n Median. n Mode

Central Tendency Central Tendency n A single summary score that best describes the central location of an entire distribution of scores. n Measures of Central Tendency: n Mean n The sum of all scores divided

### Chapter 1: Chemistry: Measurements and Methods

Chapter 1: Chemistry: Measurements and Methods 1.1 The Discovery Process o Chemistry - The study of matter o Matter - Anything that has mass and occupies space, the stuff that things are made of. This

### Chi-Square Tests. In This Chapter BONUS CHAPTER

BONUS CHAPTER Chi-Square Tests In the previous chapters, we explored the wonderful world of hypothesis testing as we compared means and proportions of one, two, three, and more populations, making an educated

### Lecture 2: Types of Variables

2typesofvariables.pdf Michael Hallstone, Ph.D. hallston@hawaii.edu Lecture 2: Types of Variables Recap what we talked about last time Recall how we study social world using populations and samples. Recall

### Describing Data Exploring Data with Graphs and Tables

Describing Data Exploring Data with Graphs and Tables Dr. Tom Ilvento FREC 408 Statistics provides us tools to describe a set of data The tools involve numerical and graphical summaries The first distinction

### 3. Justify why type of data and scale of measurement is important

TYPES OF DATA OBJECTIVE: 1. Identify how and why data differs 2. Classify data obtained from various variables 3. Justify why type of data and scale of measurement is important TARGET: STA151, STA151,

### Measurement and Measurement Scales

Measurement and Measurement Scales Measurement is the foundation of any scientific investigation Everything we do begins with the measurement of whatever it is we want to study Definition: measurement

### KeyTrain Level 5. For. Level 5. Published by SAI Interactive, Inc., 340 Frazier Avenue, Chattanooga, TN

Introduction For Level 5 Published by SAI Interactive, Inc., 340 Frazier Avenue, Chattanooga, TN 37405. Copyright 2000 by SAI Interactive, Inc. KeyTrain is a registered trademark of SAI Interactive, Inc.

### 6.1 NUMBER SENSE 3-week sequence

Year 6 6.1 NUMBER SENSE 3-week sequence Pupils can represent and explain the multiplicative nature of the number system, understanding how to multiply and divide by 10, 100 and 1000. Pupils make appropriate

### Reporting with Precision and Accuracy

COMMON CORE Locker LESSON Reporting with Precision and Accuracy Common Core Standards The student is expected to: COMMON CORE N-QA3 Choose a level of accuracy appropriate to limitations on measurement

### Accuracy, Precision and Uncertainty

Accuracy, Precision and Uncertainty How tall are you? How old are you? When you answered these everyday questions, you probably did it in round numbers such as "five foot, six inches" or "nineteen years,

### CH 2: SCIENTIFIC MEASUREMENT. Chemistry is a lot of math!

CH 2: SCIENTIFIC MEASUREMENT Chemistry is a lot of math! WARM UP 1.Name 3 tools used for measurement. 2.What is a unit? 3.Give an example of a unit. 4.Why are units important. CH 2 SCIENTIFIC MEASUREMENT

### Measurement. Measurement. Measurement

Cal State Northridge Ψ320 Andrew Ainsworth PhD In much scientific work we are interested in either describing the distributions of and/or relationships among abstract constructs: e.g., Political conservatism

### 2. Describing Data. We consider 1. Graphical methods 2. Numerical methods 1 / 56

2. Describing Data We consider 1. Graphical methods 2. Numerical methods 1 / 56 General Use of Graphical and Numerical Methods Graphical methods can be used to visually and qualitatively present data and

### Geog183: Cartographic Design and Geovisualization Winter Quarter 2015 Lecture 6: Map types and Data types

Geog183: Cartographic Design and Geovisualization Winter Quarter 2015 Lecture 6: Map types and Data types Data types Data dimension: Point, Line, Area, Volume (Text) Data continuity: Discrete, Point, Polygon:

### Statistics Review PSY379

Statistics Review PSY379 Basic concepts Measurement scales Populations vs. samples Continuous vs. discrete variable Independent vs. dependent variable Descriptive vs. inferential stats Common analyses

### AP Physics 1 and 2 Lab Investigations

AP Physics 1 and 2 Lab Investigations Student Guide to Data Analysis New York, NY. College Board, Advanced Placement, Advanced Placement Program, AP, AP Central, and the acorn logo are registered trademarks

### MEASURES OF DISPERSION

MEASURES OF DISPERSION Measures of Dispersion While measures of central tendency indicate what value of a variable is (in one sense or other) average or central or typical in a set of data, measures of

### Accuracy and Precision of Laboratory Glassware: Determining the Density of Water

Accuracy and Precision of Laboratory Glassware: Determining the Density of Water During the semester in the general chemistry lab, you will come into contact with various pieces of laboratory glassware.

### 1.1 What is statistics? Data Collection. Important Definitions. What is data? Descriptive Statistics. Inferential Statistics

1.1 What is statistics? Data Collection Chapter 1 A science (bet you thought it was a math) of Collecting of data (Chap. 1) Organizing data (Chap. 2) Summarizing data (Chap. 2, 3) Analyzing data (Chap.

### Statistics. Measurement. Scales of Measurement 7/18/2012

Statistics Measurement Measurement is defined as a set of rules for assigning numbers to represent objects, traits, attributes, or behaviors A variableis something that varies (eye color), a constant does

### Graphical and Tabular. Summarization of Data OPRE 6301

Graphical and Tabular Summarization of Data OPRE 6301 Introduction and Re-cap... Descriptive statistics involves arranging, summarizing, and presenting a set of data in such a way that useful information

### DATA COLLECTION AND ANALYSIS

DATA COLLECTION AND ANALYSIS Quality Education for Minorities (QEM) Network HBCU-UP Fundamentals of Education Research Workshop Gerunda B. Hughes, Ph.D. August 23, 2013 Objectives of the Discussion 2 Discuss

### 9 Descriptive and Multivariate Statistics

9 Descriptive and Multivariate Statistics Jamie Price Donald W. Chamberlayne * S tatistics is the science of collecting and organizing data and then drawing conclusions based on data. There are essentially

### Dr. Peter Tröger Hasso Plattner Institute, University of Potsdam. Software Profiling Seminar, Statistics 101

Dr. Peter Tröger Hasso Plattner Institute, University of Potsdam Software Profiling Seminar, 2013 Statistics 101 Descriptive Statistics Population Object Object Object Sample numerical description Object

### Statistical Analysis I

CTSI BERD Research Methods Seminar Series Statistical Analysis I Lan Kong, PhD Associate Professor Department of Public Health Sciences December 22, 2014 Biostatistics, Epidemiology, Research Design(BERD)

### LEARNING OBJECTIVES SCALES OF MEASUREMENT: A REVIEW SCALES OF MEASUREMENT: A REVIEW DESCRIBING RESULTS DESCRIBING RESULTS 8/14/2016

UNDERSTANDING RESEARCH RESULTS: DESCRIPTION AND CORRELATION LEARNING OBJECTIVES Contrast three ways of describing results: Comparing group percentages Correlating scores Comparing group means Describe

### DESCRIPTIVE STATISTICS. The purpose of statistics is to condense raw data to make it easier to answer specific questions; test hypotheses.

DESCRIPTIVE STATISTICS The purpose of statistics is to condense raw data to make it easier to answer specific questions; test hypotheses. DESCRIPTIVE VS. INFERENTIAL STATISTICS Descriptive To organize,

### Topic #1: Introduction to measurement and statistics

Topic #1: Introduction to measurement and statistics "Statistics can be fun or at least they don't need to be feared." Many folks have trouble believing this premise. Often, individuals walk into their

### MULTIPLE CHOICE. Choose the one alternative that best completes the statement or answers the question.

Exam Name 1) A recent report stated ʺBased on a sample of 90 truck drivers, there is evidence to indicate that, on average, independent truck drivers earn more than company -hired truck drivers.ʺ Does

### Research Variables. Measurement. Scales of Measurement. Chapter 4: Data & the Nature of Measurement

Chapter 4: Data & the Nature of Graziano, Raulin. Research Methods, a Process of Inquiry Presented by Dustin Adams Research Variables Variable Any characteristic that can take more than one form or value.

### Introduction to Statistics

1 Introduction to Statistics LEARNING OBJECTIVES After reading this chapter, you should be able to: 1. Distinguish between descriptive and inferential statistics. 2. Explain how samples and populations,

### 2 Descriptive statistics with R

Biological data analysis, Tartu 2006/2007 1 2 Descriptive statistics with R Before starting with basic concepts of data analysis, one should be aware of different types of data and ways to organize data

### S P S S Statistical Package for the Social Sciences

S P S S Statistical Package for the Social Sciences Data Entry Data Management Basic Descriptive Statistics Jamie Lynn Marincic Leanne Hicks Survey, Statistics, and Psychometrics Core Facility (SSP) July

### Northumberland Knowledge

Northumberland Knowledge Know Guide How to Analyse Data - November 2012 - This page has been left blank 2 About this guide The Know Guides are a suite of documents that provide useful information about

### Introduction to Statistics for Psychology. Quantitative Methods for Human Sciences

Introduction to Statistics for Psychology and Quantitative Methods for Human Sciences Jonathan Marchini Course Information There is website devoted to the course at http://www.stats.ox.ac.uk/ marchini/phs.html

### Data Types. 1. Continuous 2. Discrete quantitative 3. Ordinal 4. Nominal. Figure 1

Data Types By Tanya Hoskin, a statistician in the Mayo Clinic Department of Health Sciences Research who provides consultations through the Mayo Clinic CTSA BERD Resource. Don t let the title scare you.

### Statistical research is always concerned with a group of research objects, called population or universe (populaatio/perusjoukko).

2. Data and Measurement 2.1. Basic Concepts Statistical research is always concerned with a group of research objects, called population or universe (populaatio/perusjoukko). Determining the bounds of

### UNIT II: MATHEMATICS in CHEMISTRY Earland

Quantitative analysis used in Chemistry is not difficult, but it requires you to be able to toggle between different units and be accurate in your calculation. In other words, you must become extremely

### Measurement and Calibration

Purpose To gain an understanding of the relationships that exist between density, mass, and volume while calibrating laboratory glassware. Introduction Measurement is a regular part of life. For example,

### MULTIPLE CHOICE. Choose the one alternative that best completes the statement or answers the question.

Ch. 2 Methods for Describing Sets of Data 2.1 Describing Qualitative Data 1 Identify Classes/Compute Class Frequencies/Relative Frequencies/Percentages 1) In an eye color study, 25 out of 50 people in

### Introduction to statistics

CHAPTER Introduction to statistics Learning objectives After studying this chapter, you should be able to: identify different types of variable and to distinguish between primary and secondary data understand

### MAKING SENSE OF DATA Essentials series

MAKING SENSE OF DATA Essentials series CORRELATION AND REGRESSION Copyright by City of Bradford MDC Prerequisites Descriptive statistics Charts and graphs The normal distribution Surveys and sampling Correlation

### Chapter 2: Exploring Data with Graphs and Numerical Summaries. Graphical Measures- Graphs are used to describe the shape of a data set.

Page 1 of 16 Chapter 2: Exploring Data with Graphs and Numerical Summaries Graphical Measures- Graphs are used to describe the shape of a data set. Section 1: Types of Variables In general, variable can

### 6. Decide which method of data collection you would use to collect data for the study (observational study, experiment, simulation, or survey):

MATH 1040 REVIEW (EXAM I) Chapter 1 1. For the studies described, identify the population, sample, population parameters, and sample statistics: a) The Gallup Organization conducted a poll of 1003 Americans

### Chemistry and Measurement

Chapter 1 Chemistry and Measurement Concept Check 1.1 Matter can be represented as being composed of individual units. For example, the smallest individual unit of matter can be represented as a single

### Experiment 2 Measurements

Experiment 2 Measurements It is necessary in science to quantify any hypothesis or theory, that is, anyone should be able to reproduce anywhere and at anytime an experiment which supports that theory.

### Figure 1. A typical Laboratory Thermometer graduated in C.

SIGNIFICANT FIGURES, EXPONENTS, AND SCIENTIFIC NOTATION 2004, 1990 by David A. Katz. All rights reserved. Permission for classroom use as long as the original copyright is included. 1. SIGNIFICANT FIGURES

### STATS8: Introduction to Biostatistics. Data Exploration. Babak Shahbaba Department of Statistics, UCI

STATS8: Introduction to Biostatistics Data Exploration Babak Shahbaba Department of Statistics, UCI Introduction After clearly defining the scientific problem, selecting a set of representative members

### II. DISTRIBUTIONS distribution normal distribution. standard scores

Appendix D Basic Measurement And Statistics The following information was developed by Steven Rothke, PhD, Department of Psychology, Rehabilitation Institute of Chicago (RIC) and expanded by Mary F. Schmidt,

### CHOSUN UNIVERSITY SEOK-GANG,PARK CHAPTER 2 SECTION 1: GRAPHICAL AND TABULAR DESCRIPTIVE TECHNIQUES

CHAPTER 2 SECTION 1: GRAPHICAL AND TABULAR DESCRIPTIVE TECHNIQUES MULTIPLE CHOICE 1. The classification of student major (accounting, economics, management, marketing, other) is an example of a(n) a. nominal

### How Do You Measure Up?

. Name Date A c t i v i t y 3 How Do You Measure Up? Height Arm Span Does increasing the amount of time practicing a sport increase performance levels in that sport? Does decreasing the speed at which

### Inferential Statistics

Inferential Statistics Sampling and the normal distribution Z-scores Confidence levels and intervals Hypothesis testing Commonly used statistical methods Inferential Statistics Descriptive statistics are

### What is Statistics? Statistics is about Collecting data Organizing data Analyzing data Presenting data

Introduction What is Statistics? Statistics is about Collecting data Organizing data Analyzing data Presenting data What is Statistics? Statistics is divided into two areas: descriptive statistics and

### Quantitative Research Methods II. Vera E. Troeger Office: Office Hours: by appointment

Quantitative Research Methods II Vera E. Troeger Office: 0.67 E-mail: v.e.troeger@warwick.ac.uk Office Hours: by appointment Quantitative Data Analysis Descriptive statistics: description of central variables

### CA200 Quantitative Analysis for Business Decisions. File name: CA200_Section_04A_StatisticsIntroduction

CA200 Quantitative Analysis for Business Decisions File name: CA200_Section_04A_StatisticsIntroduction Table of Contents 4. Introduction to Statistics... 1 4.1 Overview... 3 4.2 Discrete or continuous

### Framing Business Problems as Data Mining Problems

Framing Business Problems as Data Mining Problems Asoka Diggs Data Scientist, Intel IT January 21, 2016 Legal Notices This presentation is for informational purposes only. INTEL MAKES NO WARRANTIES, EXPRESS

### Introduction to Statistics for Computer Science Projects

Introduction Introduction to Statistics for Computer Science Projects Peter Coxhead Whole modules are devoted to statistics and related topics in many degree programmes, so in this short session all I

### Lab 6: Sampling Distributions and the CLT

Lab 6: Sampling Distributions and the CLT Objective: The objective of this lab is to give you a hands- on discussion and understanding of sampling distributions and the Central Limit Theorem (CLT), a theorem

### IS463 Introduction to Data Mining Semester 1, Academic year Tutorial # 2

IS463 Introduction to Data Mining Semester 1, Academic year 2012-2013 Tutorial # 2 Activity 1: Classify the following attributes as qualitative (nominal or ordinal) or quantitative (interval or ratio),

### EXPERIMENT 1. Precision of Measurements Density of a Metal Cylinder

EXPERIMENT 1 Precision of Measurements Density of a Metal Cylinder Physics is a quantitative science, relying on accurate measurements of fundamental properties such as time, length, mass and temperature.

### Foundation of Quantitative Data Analysis

Foundation of Quantitative Data Analysis Part 1: Data manipulation and descriptive statistics with SPSS/Excel HSRS #10 - October 17, 2013 Reference : A. Aczel, Complete Business Statistics. Chapters 1

### Conceptualization And Measurement

1 Research Methods Chapter 4 Conceptualization And Measurement Prof. Kathrin Zippel 2 Agenda What do we need concepts for? What does measurement mean in social sciences? What do we mean by levels of measurement?

### Two examples of how the concept of the mode is used in reporting statistical results are as follows.

1 Social Studies 201 September 24, 2004 Mode See text, section 5.2, pp. 163-171. This section of the notes gives a formal definition of the mode, some examples of how the mode is determined, and possible

### Simple Predictive Analytics Curtis Seare

Using Excel to Solve Business Problems: Simple Predictive Analytics Curtis Seare Copyright: Vault Analytics July 2010 Contents Section I: Background Information Why use Predictive Analytics? How to use

### Basics and Beyond: Displaying Your Data. Mario Davidson, PhD Vanderbilt University School of Medicine Department of Biostatistics Instructor

Basics and Beyond: Displaying Your Data Mario Davidson, PhD Vanderbilt University School of Medicine Department of Biostatistics Instructor Objectives 1.Understand the types of data and levels of measurement

### GCSE Statistics Revision notes

GCSE Statistics Revision notes Collecting data Sample This is when data is collected from part of the population. There are different methods for sampling Random sampling, Stratified sampling, Systematic

### SIGNIFICANT FIGURES, CALCULATIONS, AND READING MEASUREMENTS LAB

SIGNIFICANT FIGURES, CALCULATIONS, AND READING MEASUREMENTS LAB Purpose: At the core of all science is the ability to take accurate and precise measurements and to do calculations. Central to these concepts

### Measurement and Calibration

Adapted from: H. A. Neidig and J. N. Spencer Modular Laboratory Program in Chemistry Thompson Learning;, University of Pittsburgh Chemistry 0110 Laboratory Manual, 1998. Purpose To gain an understanding

### Revision Notes Adult Numeracy Level 2

Revision Notes Adult Numeracy Level 2 Place Value The use of place value from earlier levels applies but is extended to all sizes of numbers. The values of columns are: Millions Hundred thousands Ten thousands

### DESCRIPTIVE STATISTICS AND EXPLORATORY DATA ANALYSIS

DESCRIPTIVE STATISTICS AND EXPLORATORY DATA ANALYSIS SEEMA JAGGI Indian Agricultural Statistics Research Institute Library Avenue, New Delhi - 110 012 seema@iasri.res.in 1. Descriptive Statistics Statistics

### Chapter 2. Objectives. Tabulate Qualitative Data. Frequency Table. Descriptive Statistics: Organizing, Displaying and Summarizing Data.

Objectives Chapter Descriptive Statistics: Organizing, Displaying and Summarizing Data Student should be able to Organize data Tabulate data into frequency/relative frequency tables Display data graphically