Copyright © 2012 by Nelson Education Limited.1-1 Chapter 1 Introduction

Copyright © 2012 by Nelson Education Limited.1-2 In this presentation you will learn about: The role of statistics in the research process Statistical applications Types of variables

Copyright © 2012 by Nelson Education Limited.1-3 Statistics are mathematical tools used to organize, summarize, and manipulate data. –Data are scores on variables, or information expressed as numbers (quantitatively). Variables are traits that can change values from case to case (e.g., age, gender, social class). –Cases are the entities from which data are gathered (e.g., people, groups, provinces, countries). The Role of Statistics

Copyright © 2012 by Nelson Education Limited.1-4 In a recent survey of university students, Statistics Canada found that the average age of university students in Canada was 21.7 years. Identify the following: 1. What is the variable? 2. What are the data? 3. What are the cases? 4. What is the statistic used? QUIZ

Copyright © 2012 by Nelson Education Limited.1-5 Variable is age. Data are the actual ages (or scores on the variable age): 18, 22, 23, etc. Cases are the university students. Statistic is the average - average age of university students in Canada QUIZ (continued)

Copyright © 2012 by Nelson Education Limited.1-6 Two main statistical applications: –Descriptive statistics –Inferential statistics Statistical Applications

Copyright © 2012 by Nelson Education Limited.1-7 Summarize one variable (univariate). Summarize the relationship between two variables (bivariate). Summarize the relationship between three or more variables (multivariate). Descriptive Statistics

Copyright © 2012 by Nelson Education Limited.1-8 Univariate descriptive statistics include: –Percentages, averages, and charts and graphs. –Example: students have an average GPA (grade point average) of 3.1. Descriptive Statistics (continued)

Copyright © 2012 by Nelson Education Limited.1-9 Bivariate descriptive statistics describe the strength and direction of the relationship between two variables. –Example: Older students tend to have higher GPAs than younger students. Descriptive Statistics (continued)

Copyright © 2012 by Nelson Education Limited.1-10 Multivariate descriptive statistics describe the relationships between three or more variables. –Example: GPAs increase with age for females but not for males. Descriptive Statistics (continued)

Copyright © 2012 by Nelson Education Limited.1-11 Generalize, or infer, from a sample to a population. –Population includes all cases in which the research is interested. –Samples include carefully chosen subsets of the population. Inferential Statistics

Copyright © 2012 by Nelson Education Limited.1-12 Voter surveys are a common application of inferential statistics. –A thousand or so carefully selected voters are interviewed about their voting intentions. –This information is used to estimate the intentions of all voters (millions of people). –Example: The Conservative Party will receive about 36% of the vote. Inferential Statistics (continued)

Copyright © 2012 by Nelson Education Limited.1-13 There are many schemes used to classify variables including: 1.Independent or dependent variables 2.Discrete or continuous variables 3.Nominal, ordinal, or interval-ratio variables (or levels of measurement) Types of Variables

Copyright © 2012 by Nelson Education Limited.1-14 In causal relationships: CAUSE  EFFECT independent variable  dependent variable Independent or Dependent Variables

Copyright © 2012 by Nelson Education Limited.1-15 Discrete variables are measured in units that cannot be subdivided. –Example: Gender Continuous variables are measured in a unit that can be subdivided infinitely. –Example: Age Discrete or Continuous Variables

Copyright © 2012 by Nelson Education Limited.1-16 The mathematical quality of the scores of a variable is measured on three different levels, called levels of measurement: –Nominal - Scores are labels only, they are not numbers. –Ordinal - Scores have some numerical quality and can be ranked. –Interval-ratio - Scores are numbers.* *NOTE: Some statisticians distinguish between the interval level (equal intervals) and the ratio level (equal intervals WITH a true zero point). Since most statistical analysis that is appropriate for interval variables is also appropriate for ratio variables, they are treated as one in the textbook. Nominal, Ordinal, or Interval- Ratio Level of Measurement

Copyright © 2012 by Nelson Education Limited.1-17 Different statistics require different mathematical operations (ranking, addition, square root, etc.), so the level of measurement of a variable tells us which statistics are permissible and appropriate. Nominal, Ordinal, or Interval-Ratio Level of Measurement (continued)

Copyright © 2012 by Nelson Education Limited.1-18 Scores are different from each other but cannot be treated as numbers. –Examples: Gender –1 = Female, 2 = Male Immigrant Status –1 = Canadian-born, 2 =Foreign-born Nominal Level Variables

Copyright © 2012 by Nelson Education Limited.1-19 Scores can be ranked from high to low or from more to less. Survey items that measure opinions and attitudes are typically ordinal. If you can distinguish between the scores of the variable using terms such as “more, less, higher, or lower” the variable is ordinal. –Example: Students at a university were asked “Do you agree or disagree that smoking should be banned on campus?” (A student that agreed would be more in favour to ban smoking on campus than a student who disagreed). Ordinal Level Variables

Copyright © 2012 by Nelson Education Limited.1-20 Scores are actual numbers and have equal intervals between them. Examples: –Age (in years) –Income (in dollars) –Number of children Interval-Ratio Level Variables