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Chapter Two

Chapter Two. Statistics and the Research Process. The Logic of Research. Chapter 2 - 2. Scientific Research. The goal of science is to understand the “laws of nature” We examine a specific influence on a specific behavior in a specific situation

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Chapter Two

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  1. Chapter Two Statistics and the Research Process

  2. The Logic of Research Chapter 2 - 2

  3. Scientific Research The goal of science is to understand the “laws of nature” We examine a specific influence on a specific behavior in a specific situation Then, we generalize back to the broader behaviors and laws with which we began Chapter 2 - 3

  4. Samples and Populations Chapter 2 - 4

  5. Samples and Populations The entire group of individuals to which a law applies is the population A sample is a relatively small subset of a population that is intended to represent, or stand for, the population The individuals measured in a sample are called the participants or subjects Chapter 2 - 5

  6. Drawing Inferences We use the scores in a sample to infer or to estimate the scores we would expect to find in the population. Chapter 2 - 6

  7. Representativeness In a representative sample, the characteristics of the sample accurately reflect the characteristics of the population. Chapter 2 - 7

  8. Random Sampling Randomsampling is a method of selecting a sample in which the individuals are randomly selected from the population. Chapter 2 - 8

  9. Unrepresentative Samples Random sampling should result in a sample that is representative of the population, but it is not foolproof An unrepresentative sample can result in misleading evidence and wrong conclusions Chapter 2 - 9

  10. Obtaining Data A variable is anything that, when measured, can produce two or more different values (scores). Some common variables are: Age Race Gender Intelligence Personality type Chapter 2 - 10

  11. Types of Variables A quantitative variable indicates the amount of a variable that is present A qualitative variable classifies an individual on the basis of some characteristic Chapter 2 - 11

  12. Examining Relationships In a relationship, as the scores on one variable change, the scores on the other variable change in a consistent manner. Chapter 2 - 12

  13. Strength of a Relationship The strength of a relationship is the degree of consistency in the relationship A stronger relationship occurs when one group of similar Y values is associated with one X score and a different group of similar Y scores is associated with the next X score Chapter 2 - 13

  14. Factors Affecting Strength A “weaker” relationship may be due to additional extraneous influences and/or individual differences Individual differences refer to the fact no two individuals are identical Chapter 2 - 14

  15. Graphing Relationships Describe a relationship using the general format: “Scores on the Y variable change as a function of changes in the X variable.” The given variable in a study is the X variable. Chapter 2 - 15

  16. Four Sample Graphs A graph showing a perfectly consistent association. Chapter 2 - 16

  17. Four Sample Graphs A relationship that is not perfectly consistent. Chapter 2 - 17

  18. Four Sample Graphs A weak relationship. Chapter 2 - 18

  19. Four Sample Graphs No consistent pattern. Chapter 2 - 19

  20. Applying Descriptive and Inferential Statistics Chapter 2 - 20

  21. Descriptive Statistics Descriptive statistics are procedures used for organizing and summarizing data. What scores occurred? What is the average or typical score? Are the scores very similar to each other or very different? Is a relationship present? Chapter 2 - 21

  22. Inferential Statistics Inferential statistics are procedures for deciding whether sample data accurately represent a particular relationship in the population Inferential statistics allow us to make inferences about the scores and relationship found in the population Chapter 2 - 22

  23. Statistics and Parameters A statistic is a number that describes a characteristic of a sample of scores A parameter is a number that describes a characteristic of a population of scores Chapter 2 - 23

  24. Understanding Experiments and Correlational Studies Chapter 2 - 24

  25. Research Designs A study’s design is the way the study is laid out There are two major types of designs: Experiments Correlational studies Chapter 2 - 25

  26. Experiments In an experiment, the researcher actively changes or manipulates one variable and then measures participants’ scores on another variable to see if a relationship is produced. Chapter 2 - 26

  27. The Independent Variable The independent variable is the variable that is changed or manipulated by the experimenter A condition is a specific amount or category of the independent variable that creates the specific situation under which participants are examined Chapter 2 - 27

  28. The Dependent Variable The dependent variable is used to measure a participant’s behavior under each condition of the independent variable We apply descriptive statistics only to the scores from the dependent variable Chapter 2 - 28

  29. Correlational Studies In a correlational study, we simply measure participants’ scores on two variables and then determine whether a relationship is present. Chapter 2 - 29

  30. Causality We cannot definitively prove the independent variable causes the scores on the dependent variable to change. It is always possible some other hidden variable is actually the cause. Chapter 2 - 30

  31. The Characteristics of the Scores Chapter 2 - 31

  32. Characteristics of Variables Two important characteristics of variables are The type of measurement scale involved Whether it is continuous or discrete Chapter 2 - 32

  33. Measurement Scales There are four types of measurement scales: A nominal scale does not indicate an amount; rather, it is used for identification, as a name. An ordinal scale indicates rank order. There is not an equal unit of measurement separating each score. Chapter 2 - 33

  34. Measurement Scales (cont’d) An interval scale indicates an actual quantity and there is an equal unit of measurement separating adjacent scores. Interval scales do not have a “true” 0. A ratio scale reflects the true amount of the variable that is present because the scores measure an actual amount, there is an equal unit of measurement, and 0 truly means that zero amount of the variable is present. Chapter 2 - 34

  35. Discrete and Continuous Any measurement scale also may be either continuous or discrete A continuous scale allows for fractional amounts and so decimals make sense In a discrete scale, only whole-number amounts can be measured Chapter 2 - 35

  36. Dichotomous Variable • A dichotomous variable has only two possible categories or scores: • Male or female • Yes or no • Correct or incorrect Chapter 2 - 36

  37. Summary of Measurement Scales Chapter 2 - 37

  38. Key Terms • as a function of • condition • continuous scale • correlational study • dependent variable • descriptive statistics • design • dichotomous variable • discrete scale • experiment • independent variable • individual differences • inferential statistics • interval scale • level • nominal scale • ordinal scale • parameter • participant • population Chapter 2 - 38 Chapter 1 - 38

  39. Key Terms (Cont’d) • ratio scale • relationship • sample • statistic • strength of a relationship • treatment • variable Chapter 2 - 39 Chapter 2 - 39 Chapter 1 - 39

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