Chapter 3+5 R & M - Lecture notes 3-5 PDF

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3.1 Constructs and Operational Definitions: Theories and Constructs: 

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Theories: is a set of statements about the mechanisms underlying a particular behavior. Theories help organize and unify different observations of the behavior and its relationship with other variables. A good theory generates predictions about the behavior. o contain hypothetical mechanisms and intangible elements. Constructs/ hypothetical constructs: are hypothetical attributes or mechanisms that help explain and predict behavior in a theory. External Stimulus  Construct  External Behavior.

Operational Definitions:

Operational Definition: is a procedure for indirectly measuring and defining a variable that cannot be observed or measured directly. An operational definition specifies a measurement procedure (a set of operations) for measuring an external, observable behavior and uses the resulting measurements as a definition and a measurement of the hypothetical construct.

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Researchers often refer to the process of using an operational definition as operationalizing a construct. In addition to using operational definitions as a basis for measuring variables, they also can be used to define variables to be manipulated.

Limitations of Operational Definitions:  

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an operational definition is not the same as the construct itself. The primary limitation of an operational definition is that there is not a one-to-one relationship between the variable that is being measured and the actual measurements produced by the operational definition. Second, operational definitions often include extra components that are not part of the construct being measured. knowledge is a construct that cannot be directly observed or measured.

Using Operational Definitions: 

The best method of determining how a variable should be measured is to consult previous research involving the same variable.

3.2 Validity and Reliability of Measurement:

Consistency of a Relationship:   

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validity and reliability of measurements are established by demonstrating the consistency of a relationship between two different measurements. To show the amount of consistency between two different measurements, the two scores obtained for each person can be presented in a graph called a scatter plot. Positive Relationship: The relationship is described as positive because the two measurements change together in the same direction. Therefore, people who score high on the first measurement (toward the right of the graph) also tend to score high on the second measurement (toward the top of the graph). Negative Relationship: . This time the two measurements change in opposite directions so that people who score high on one measurement tend to score low on the other.

A consistent positive relationship like the one in Figure 3.1(a) produces a correlation near +1.00, a consistent negative relationship like the one in Figure 3.1(b) produces a correlation near −1.00, and an inconsistent relationship like the one in Figure 3.1(c) produces a correlation near zero.

Validity of Measurement: 

Validity: of a measurement procedure is the degree to which the measurement process measures the variable that it claims to measure.

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Intelligence is hypothetical and cannot be directly observed or measured Does the measurement procedure accurately capture the variable that it is supposed to measure? Researchers have developed several methods for assessing the validity of measurement. Six of the more commonly used definitions of validity are as follows: 1. Face Validity: is an unscientific form of validity demonstrated when a measurement procedure superficially appears to measure what it claims to measure. Concealing the variables, they are trying to measure. 2. Concurrent Validity: is demonstrated when scores obtained from a new measure are directly related to scores obtained from an established measure of the same variable. Because one procedure is well established and accepted as being valid, we infer that the second procedure must also be valid. 3. Predictive Validity: is demonstrated when scores obtained from a measure accurately predict behavior according to a theory. 4. Construct Validity: requires that the scores obtained from a measurement procedure behave exactly the same as the variable itself. Construct validity is based on many research studies that use the same measurement procedure and grows gradually as each new study contributes more evidence. 5. Convergent Validity: is demonstrated by a strong relationship between the scores obtained from two (or more) different methods of measuring the same construct. 6. Divergent Validity: is demonstrated by showing little or no relationship between the measurements of two different constructs in the same measurement process.

Reliability of Measurement: 

Reliability: of a measurement procedure is the stability or consistency of the measurement. If the same individuals are measured under the same conditions, a reliable measurement procedure produces identical (or nearly identical) measurements. Measured Score = True Score + Error

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Observer error: The individual who makes the measurements can introduce simple human error into the measurement process, especially when the measurement involves a degree of human judgment. Environmental changes: Although the goal is to measure the same individual under identical circumstances, this ideal is difficult to attain. Often, there are small changes in the environment from one measurement to another, and these small changes can influence the measurements. There are so many environmental variables (such as time of day,

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temperature, weather conditions, and lighting) that it is essentially impossible to obtain two identical environmental conditions. Participant changes: The participant can change between measurements. As noted earlier, a person’s degree of focus and attention can change quickly and can have a dramatic effect on measures of reaction time. Such changes may cause the obtained measurements to differ, producing what appear to be inconsistent or unreliable measurements.

Types and Measures of Reliability: 

Successive measurements: o Test-retest reliability: is established by comparing the scores obtained from two successive measurements of the same individuals and calculating a correlation between the two sets of scores. If alternative versions of the measuring instrument are used for the two measurements, the reliability measure is called parallel-forms reliability. o Inter-rater reliability: is the degree of agreement between two observers who simultaneously record measurements of the behaviors. o Split-half reliability: is obtained by splitting the items on a questionnaire or test in half, computing a separate score for each half, and then calculating the degree of consistency between the two scores for a group of participants.

The Relationship between Reliability and Validity: 

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Although reliability and validity are both criteria for evaluating the quality of a measurement procedure, these two factors are partially related and partially independent. They are related to each other in that reliability is a prerequisite for validity; that is, a measurement procedure cannot be valid unless it is reliable. it is not necessary for a measurement to be valid for it to be reliable. Thus, the consistency of measurement is no guarantee of validity. Accuracy: The accuracy of a measurement is the degree to which the measurement conforms to the established standard. In the behavioral sciences, it is quite common to measure variables for which there is no established standard. In such cases, it is impossible to define or measure accuracy.

3.2Scales of Measurement:

The Nominal Scale: simply represent qualitative (not quantitative) differences in the variable measured. The categories have different names but are not related to each other in any systematic way. Measurements from a nominal scale allow us to determine whether two individuals are the same or different, but they do not permit any quantitative comparison. The Ordinal Scale: Often, an ordinal scale consists of a series of ranks (first, second, third, and so on) like the order of finish in a horse race. Occasionally, the categories are identified by verbal labels such as small, medium, and large drink sizes at a fast-food restaurant. Ordinal measurements do not allow us to determine the magnitude of the difference between the two individuals. Interval and Ratio Scales: The categories on interval and ratio scales are organized sequentially, and all categories are the same size. Thus, the scale of measurement consists of a series of equal intervals like the inches on a ruler. The characteristic that differentiates interval and ratio scales is the zero point.   

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Interval scales with an arbitrary zero point are fairly rare. Most common examples Fahrenheit and Celsius. Although the distinction between interval and ratio scales has little practical significance, the differences among the other measurement scales can be enormous. Also, scores from interval or ratio scales are compatible with basic arithmetic, which permits more sophisticated analysis and interpretation. As a result, interval or ratio scale data are usually preferred for most research situations.

Dealing with Equivocal Measurements: 

It also is common for researchers in the behavioral sciences to measure variables using rating scales.

Selecting a Scale of Measurement:  

One obvious factor that differentiates the four types of measurement scales is their ability to compare different measurements. nominal scale can tell us only that a difference exists.

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An ordinal scale tells us the direction of the difference. With an interval scale, we can determine the direction and the magnitude of a difference. The ability to compare measurements has a direct effect on the ability to describe relationships between variables.

3.4Modalities of Measurement:

Self-Report Measures: 

self-report measure: to ask participants to describe or to quantify their own fear. o it is probably the most direct way to assess a construct. o Each individual is in a unique position of self-knowledge and self-awareness. o no one knows more about the individual’s fear than the individual. o On the negative side, however, it is very easy for participants to distort self-report measures.

Physiological Measures:   

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Physiological Measures: to look at the physiological manifestations of the underlying construct. physiological measures involve brain-imaging techniques such as positron emission tomography (PET) scanning and magnetic resonance imaging (MRI). One advantage of physiological measures is that they are extremely objective. The equipment provides accurate, reliable, and well-defined measurements that are not dependent on subjective interpretation by either the researcher or the participant. One disadvantage of such measures is that they typically require equipment that may be expensive or unavailable. In addition, the presence of monitoring devices creates an unnatural situation that may cause participants to react differently than they would under normal circumstances.

Behavioral Measures: 

Behavioral measures: Constructs often reveal themselves in overt behaviors that can be observed and measured. The behaviors may be completely natural events such as laughing, playing, eating, sleeping, arguing, or speaking. Or the behaviors may be structured, as when a researcher measures performance on a designated task. In the latter case, a researcher usually develops a specific task in which performance is theoretically dependent on the construct being measured.

o provide researchers with a vast number of options, making it possible to select the behaviors that seem to be best for defining and measuring the construct. o On the negative side, a behavior may be only a temporary or situational indicator of an underlying construct. o Usually, it is best to measure a cluster of related behaviors rather than rely on a single indicator.

3.5 Other Aspects of Measurement: Multiple Measures:   

One method of obtaining a more complete measure of a construct is to use two (or more) different procedures to measure the same variable. The advantage of this multiple-measure technique is that it usually provides more confidence in the validity of the measurements. One method for limiting the problems associated with multiple measures is to combine them into a single score for each individual.

Sensitivity and Range Effects: 

a researcher begins a study with some expectation of how the variables will behave,

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specifically the direction and magnitude of changes that are likely to be observed. range Effect: the measurement procedure is insensitive to changes that may occur in one direction.

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ceiling Effect: is the clustering of scores at the high end of a measurement scale, allowing little or no possibility of increases in value. Floor Effect: is the clustering of scores at the low end of a measurement scale, allowing little or no possibility of decreases in value. In general, range effects suggest a basic incompatibility between the measurement procedure and the individuals measured.

Artifacts: Experimenter Bias and Participant Reactivity: 

Artifact: nonnatural feature accidentally introduced into something being observed. o In the context of a research study, an artifact is an external factor that may influence or distort the measurements.

Experimenter Bias:   

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Experimenter bias: occurs when the measurements obtained in a study are influenced by the experimenter’s expectations or personal beliefs regarding the outcome of the study. Typically, a researcher knows the predicted outcome of a research study and is in a position to influence the results, either intentionally or unintentionally. The experimenter is manipulating participant motivation, and this manipulation can distort the results.

Rosenthal and Fode (1963) identified a variety of ways that an experimenter can influence a participant’s behavior: o By paralinguistic cues (variations in tone of voice) that influence the participants to give the expected or desired responses o By kinesthetic cues (body posture or facial expressions) o By verbal reinforcement of expected or desired responses o By misjudgment of participants’ responses in the direction of the expected results o By not recording participants’ responses accurately (errors in recording of data) in the direction of the expected or desired results One option for limiting experimenter bias is to standardize or automate the experiment In each case, the goal is to limit the personal contact between the experimenter and the participant. Another strategy for reducing experimenter bias is to use a “blind” experiment. Finally, we should note that there are many research studies in which the participants do not know the hypothesis. Often participants are deliberately misled about the purpose of the study to minimize the likelihood that their expectations will influence their behaviors. single-blind: if the researcher does not know the predicted outcome. double-blind: if both the researcher and the participants are unaware of the predicted outcome.

Demand Characteristics and Participant Reactivity:  

Specifically, living organisms are active and responsive, and their actions and responses can distort the results. Demand characteristics: refer to any of the potential cues or features of a study that (1) suggest to the participants what the purpose and hypothesis is and (2) influence the participants to respond or behave in a certain way.

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Reactivity: occurs when participants modify their natural behavior in response to the fact that they are participating in a research study or the knowledge that they are being measured. First, participants often try to figure out the purpose of the study and then modify their responses to fit their perception of the researcher’s goals. Second, participants can become so dedicated to performing well that they do things in a research study that they would never do in a normal situation. subject roles/subject role behaviors: individuals may adopt different ways of responding to experimental cues based on whatever they judge to be an appropriate role in the situation. There are 4 different subject roles: 1. good subject role: These participants have identified the hypothesis of the study and are trying to produce responses that support the investigator’s hypothesis. 2. negativistic subject role: These participants have identified the hypothesis of the study and are trying to act contrary to the investigator’s hypothesis. 3. apprehensive subject role: These participants are overly concerned that their performance in the study will be used to evaluate their abilities or personal characteristics. They try to place themselves in a desirable light by responding in a socially desirable fashion instead of truthfully. 4. faithful subject role: These participants attempt to follow instructions to the letter and avoid acting on any suspicions they have about the purpose of the study. Two types of participants take on this role: those who want to help science and know they should not allow their suspicions to enter into their responses, and those who are simply apathetic and do not give the study much thought. These are the participants we really want in our study. Reactivity is especially a problem in studies conducted in a laboratory, where participants are fully aware that they are participants in a study. in a field study, participants are observed in their natural environment and are much less likely to know that they are being investigated, hence they are less reactive. Laboratory: is any setting that is obviously devoted to the discipline of science. It can be any room or any space that the subject or participant perceives as artificial. Field: setting is a place that the participant or subject perceives as a natural environment.

Selecting a Measurement Procedure:  

The best starting point for selecting a measurement procedure is to review past research reports involving the variables or constructs to be examined. Most commonly used procedures have been evaluated for reliability and validity.

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decide whether the scale of measurement (nominal, ordinal, interval, or ratio) is appropriate for the kind of conclusion you would like to make. always question the measurement procedures

5.1 Introduction to Sampling: Populations and Samples: 

Population: is the entire set of individuals of interest to a researcher. Although the entire population usually does not participate in a research study, the results from the study are generalized to the entire population.  Sample: is a set of individuals selected from a population and usually is intended to represent the population in a research study

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target population: is the group defined by the researcher’s specific interests. Individuals in a target population typically share one characteristic.

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accessible population: Most researchers select their samples from accessible populations. we not only need to be cautious about generalizing the results of a study to the accessible population but we must also always be extremely cautious about generalizing th...