1. Chapter Six: The Basics of Experimentation I: Variables and Control

2. The Nature of Variables Variable

3. The Nature of Variables Variable A variable is an event or behavior that can assume at least two values.

4. Operationally Defining Variables Bridgman (1927) suggested that researchers should define their variables in terms of the operations needed to produce them.

5. Operationally Defining Variables Bridgman (1927) suggested that researchers should define their variables in terms of the operations needed to produce them. Such definitions allow others to replicate your research and are called operational definitions.

6. Independent Variables Independent Variables (IV’s)

7. Independent Variables Independent Variables (IV’s) IV’s are those variables that the experimenter purposely manipulates.

8. Independent Variables Independent Variables (IV’s) IV’s are those variables that the experimenter purposely manipulates. The IV constitutes the reason the research is being conducted; the experimenter is interested in determining what effect the IV has.

9. Types of IV’s Physiological IV

10. Types of IV’s Physiological IV The physiological state of the participant that the experimenter manipulates.

11. Types of IV’s Experience IV

12. Types of IV’s Experience IV Manipulation of the amount or type of training or learning.

13. Types of IV’s Stimulus or environmental IV

14. Types of IV’s Stimulus or environmental IV An aspect of the environment that the experimenter manipulates.

15. Participant characteristics Aspects of the participant, such as age, sex or personality traits, that are treated as if they are IV’s.

16. Participant characteristics Aspects of the participant, such as age, sex or personality traits, that are treated as if they are IV’s. They are not IV’s because they cannot be manipulated by the experimenter.

17. Extraneous Variables (confounders) Extraneous variables

18. Extraneous Variables (confounders) Extraneous variables Uncontrolled variables that can cause unintended changes between groups.

19. Extraneous Variables (confounders) Extraneous variables Uncontrolled variables that can cause unintended changes between groups. Confounding

20. Extraneous Variables (confounders) Extraneous variables Uncontrolled variables that can cause unintended changes between groups. Confounding A situation in which the results of an experiment can be attributed to either the operation of an IV or an extraneous variable.

21. Dependent Variables Dependent Variable (DV)

22. Dependent Variables Dependent Variable (DV) A response or behavior that is measured. It is desired that changes in the DV are directly related to manipulation of the IV.

23. Recording or Measuring the DV Correctness

24. Recording or Measuring the DV Correctness Only correct responses are counted.

25. Recording or Measuring the DV Correctness Rate or Frequency

26. Recording or Measuring the DV Correctness Rate or Frequency Rate of responding determines how rapidly responses are made during a specified time period.

27. Recording or Measuring the DV Correctness Rate or Frequency Rate of responding determines how rapidly responses are made during a specified time period. The number of responses or events that occur within a specified time period is the frequency.

28. Recording or Measuring the DV Correctness Rate or Frequency Degree or Amount

29. Recording or Measuring the DV Correctness Rate or Frequency Degree or Amount Latency or Duration

30. Should You Record More than One DV? If you have the measurement capabilities, there is nothing to prohibit the recording of more than one DV.

31. Should You Record More than One DV? If you have the measurement capabilities, there is nothing to prohibit the recording of more than one DV. If recording an additional DV makes a meaningful contribution to your understanding of the phenomenon under study, then you should give it serious consideration.

32. Characteristics of a Good DV A DV is valid when it measures what the experimental hypothesis says it should measure.

33. Characteristics of a Good DV A DV is valid when it measures what the experimental hypothesis says it should measure. A good DV must be directly related to the IV and must measure the effects of the IV manipulation as the experimental hypothesis predicts it will.

34. Characteristics of a Good DV A DV is valid when it measures what the experimental hypothesis says it should measure. A good DV must be directly related to the IV and must measure the effects of the IV manipulation as the experimental hypothesis predicts it will. A good DV is also reliable.

35. Nuisance Variables

36. Unwanted variables that can cause the variability of scores within groups to increase.

37. Nuisance Variables Unwanted variables that can cause the variability of scores within groups to increase. Nuisance variables increase the spread of scores within a distribution; they do not cause a distribution to change its location.

38. Controlling Extraneous Variables The experimenter must exercise control over both extraneous variables and nuisance variables so the results of the experiment are as meaningful (no extraneous variables present) and clear (minimal influence of nuisance variables) as possible.

39. Basic Control Techniques Randomization

40. Basic Control Techniques Randomization A control technique that ensures that each participant has an equal chance of being assigned to any group in an experiment.

41. Basic Control Techniques Randomization Elimination

42. Basic Control Techniques Randomization Elimination A control technique whereby extraneous variables are completely removed from an experiment.

43. Basic Control Techniques Randomization Elimination Constancy

44. Basic Control Techniques Randomization Elimination Constancy A control technique by which an extraneous variable is reduced to a single value that is experienced by all participants.

45. Basic Control Techniques Randomization Elimination Constancy Balancing

46. Basic Control Techniques Randomization Elimination Constancy Balancing A control procedure that achieves group equality by distributing extraneous variables equally to all groups.

47. Basic Control Techniques Randomization Elimination Constancy Balancing Counterbalancing

48. Basic Control Techniques Randomization Elimination Constancy Balancing Counterbalancing A procedure for controlling order effects by presenting different treatment sequences.

49. Basic Control Techniques Randomization Elimination Constancy Balancing Counterbalancing

50. Within-Subject counterbalancing

51. Counterbalancing Within-Subject counterbalancing Presentation of different treatment sequences to the same participant.

52. Counterbalancing Within-Subject counterbalancing Presentation of different treatment sequences to the same participant. Within-Group counterbalancing

53. Counterbalancing Within-Group counterbalancing Presentation of different treatment sequences to different participants. Three basic requirements:

54. Counterbalancing Within-Group counterbalancing Presentation of different treatment sequences to different participants. Three basic requirements: Each treatment must be presented to each participant an equal number of times.

55. Counterbalancing Within-Group counterbalancing Presentation of different treatment sequences to different participants. Three basic requirements: Each treatment must be presented to each participant an equal number of times. Each treatment must occur an equal number of times at each testing or practice session.

56. Counterbalancing Within-Group counterbalancing Presentation of different treatment sequences to different participants. Three basic requirements: Each treatment must be presented to each participant an equal number of times. Each treatment must occur an equal number of times at each testing or practice session. Each treatment must precede and follow each of the other treatments an equal number of times.

57. Counterbalancing Within-Subject counterbalancing Within-Group counterbalancing Complete counterbalancing

58. Counterbalancing Within-Subject counterbalancing Within-Group counterbalancing Complete counterbalancing All possible treatment sequences are presented.

59. Counterbalancing Within-Subject counterbalancing Within-Group counterbalancing Complete counterbalancing All possible treatment sequences are presented. You can calculate the number of sequences by using the formula n! (n factorial).

60. Counterbalancing Within-Subject counterbalancing Within-Group counterbalancing Complete counterbalancing Incomplete counterbalancing

61. Counterbalancing Within-Subject counterbalancing Within-Group counterbalancing Complete counterbalancing Incomplete counterbalancing Only a portion of all possible sequences are presented.

62. Counterbalancing Sequence or Order Effects

63. Counterbalancing Sequence or Order Effects Sequence or order effects are produced by the participant’s being exposed to the sequential presentation of the treatments.

64. Counterbalancing Sequence or Order Effects Sequence or order effects are produced by the participant’s being exposed to the sequential presentation of the treatments. The sequence or order effect depends on where in the sequential presentation of treatments the participant’s performance is evaluated, not which treatment is experienced.

65. Counterbalancing Carryover Effects

66. Counterbalancing Carryover Effects The effects of one treatment persist or carry over and influence responses to the next treatment.

67. Counterbalancing Differential Carryover

68. Counterbalancing Differential Carryover The response to one treatment depends on which treatment was administered previously.

69. Internal Validity: Evaluating Your Experiment from the Inside Internal Validity

70. Internal Validity: Evaluating Your Experiment from the Inside Internal Validity The concept of internal validity revolves around the question of whether your IV actually caused any change that you observe in your DV.

71. Internal Validity: Evaluating Your Experiment from the Inside Internal Validity The concept of internal validity revolves around the question of whether your IV actually caused any change that you observe in your DV. If you use adequate control techniques, your experiment should be free from confounding and you can, indeed, conclude that your IV caused the change in your DV.

72. Threats to Internal Validity History

73. Threats to Internal Validity History History refers to events that occur between the DV measurements in a repeated measures design.

74. Threats to Internal Validity History Maturation

75. Threats to Internal Validity History Maturation Maturation refers to changes in participants that occur over time during an experiment.

76. Threats to Internal Validity History Maturation Maturation refers to changes in participants that occur over time during an experiment. These changes could include actual physical maturation or tiredness, boredom, hunger, and so on.

77. Threats to Internal Validity History Maturation Testing

78. Threats to Internal Validity History Maturation Testing Testing is a threat to internal validity that occurs because measuring the DV causes a change in the DV.

79. Threats to Internal Validity History Maturation Testing Testing is a threat to internal validity that occurs because measuring the DV causes a change in the DV. Campbell (1957) noted that if you take the same test more than once, scores on the second test may vary systematically from the first scores simply because you took the test a second time.

80. Threats to Internal Validity History Maturation Testing Practice Effect

81. Threats to Internal Validity History Maturation Testing Practice Effect A practice effect is a beneficial effect on a DV measurement caused by previous experience with the DV.

82. Threats to Internal Validity History Maturation Testing Practice Effect Reactive Measures

83. Threats to Internal Validity History Maturation Testing Practice Effect Reactive Measures Reactive measures are DV measurements that actually change the DV being measured.

84. Threats to Internal Validity History Maturation Testing Practice Effect Reactive Measures Reactive measures are DV measurements that actually change the DV being measured. Many attitude questionnaires are reactive measures. If we ask you a number of questions about how you feel about people of different racial groups, or about women’s rights, or about the President’s job performance, you can probably figure out that your attitude is being measured.

85. Threats to Internal Validity History Maturation Testing Practice Effect Reactive Measures Instrumentation (Instrument Decay)

86. Threats to Internal Validity History Maturation Testing Practice Effect Reactive Measures Instrumentation (Instrument Decay) Instrumentation is a threat to internal validity that occurs if the equipment or human measuring the DV changes the measuring criterion over time.

87. Threats to Internal Validity History Maturation Testing Practice Effect Reactive Measures Instrumentation (Instrument Decay) Statistical Regression

88. Threats to Internal Validity History Maturation Testing Practice Effect Reactive Measures Instrumentation (Instrument Decay) Statistical Regression Statistical regression occurs when low scorers improve or high scorers fall on a second administration of a test due solely to statistical reasons.

89. Threats to Internal Validity History Maturation Testing Practice Effect Reactive Measures Instrumentation (Instrument Decay) Statistical Regression Selection

90. Threats to Internal Validity History Maturation Testing Practice Effect Reactive Measures Instrumentation (Instrument Decay) Statistical Regression Selection If we choose participants in such a way that our groups are not equal before the experiment, we cannot be certain that our IV caused any difference we observe after the experiment.

91. Threats to Internal Validity History Maturation Testing Practice Effect Reactive Measures Instrumentation (Instrument Decay) Statistical Regression Selection Mortality

92. Threats to Internal Validity History Maturation Testing Practice Effect Reactive Measures Instrumentation (Instrument Decay) Statistical Regression Selection Mortality Mortality can occur if experimental participants from different groups drop out of the experiment at different rates.

93. Threats to Internal Validity History Maturation Testing Practice Effect Reactive Measures Instrumentation (Instrument Decay) Statistical Regression Selection Mortality Interactions with Selection

94. Threats to Internal Validity History Maturation Testing Practice Effect Reactive Measures Instrumentation (Instrument Decay) Statistical Regression Selection Mortality Interactions with Selection Interactions with selection can occur when the groups we have selected show differences on another variable (i.e., maturation, history, or instrumentation) that vary systematically by groups.

95. Threats to Internal Validity History Maturation Testing Practice Effect Reactive Measures Instrumentation (Instrument Decay) Statistical Regression Selection Mortality Interactions with Selection Diffusion or Imitation of Treatments

96. Threats to Internal Validity History Maturation Testing Practice Effect Reactive Measures Instrumentation (Instrument Decay) Statistical Regression Selection Mortality Interactions with Selection Diffusion or Imitation of Treatments Diffusion or imitation of treatments can occur if participants in one treatment group become familiar with the treatment of another group and copy that treatment.

97. Protecting Internal Validity Two Approaches

98. Protecting Internal Validity Two Approaches You can (and should) implement the various control procedures discussed in this chapter.

99. Protecting Internal Validity Two Approaches You can (and should) implement the various control procedures discussed in this chapter. Use a standard procedure

100. Protecting Internal Validity Two Approaches You can (and should) implement the various control procedures discussed in this chapter. Use a standard procedure Experimenters used standard procedures called experimental designs to help ensure internal validity.

101. Protecting Internal Validity How Important is Internal Validity?

102. Protecting Internal Validity How Important is Internal Validity? It is the most important property of any experiment.

103. Protecting Internal Validity How Important is Internal Validity? It is the most important property of any experiment. If you do not concern yourself with the internal validity of your experiment, you are wasting your time.