Lesson 3 Chapter 10 #2 Researching The Polity Chapter Quiz

Likert scales do have some obvious advantages. They are relatively easy to administer, as they involve asking participants to indicate their level of agreement or disagreement with a statement. They also provide a more rational basis for item selection, as they allow researchers to measure attitudes or opinions on a continuum. Additionally, Likert scales provide a range of alternative responses to each question, allowing participants to choose the response that best represents their opinion. Therefore, the statement that Likert scales have some obvious advantages is true.

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Guttman scales have several characteristics. First, they incorporate an empirical test of unidimensionality. They measure only a single dimension or attitude. Second, Guttman scales are cumulative. Potential scale items are ordered according to the degree of difficulty associated with responding positively to each item. The technique, however, assumes that respondents who answer positively to a difficult item will also respond positively to less difficult items.

As a result of the ordering process, Guttman scales, generally yield scale scores resulting from a single set of responses. That is, to get a 20 on the ideological perception scale, a particular pattern of responses is essential.
Table 10-3. Illustration of Unidimensionality


More Difficult Less Difficult
Respondent Stranger Prayer Group Shopping Services Pray Score
1 yes YES yes YES yes 5
2 no yes yes yes yes 4
3 no no yes yes yes 3
4 no no no yes yes 2
5 no no no no yes 1
6 no no no no no 0

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As with nominal variables, ordinal measures merely assign numeric labels to the categories of a variable. There are, however, certain rules you should follow when measuring or coding, ordinal variables. Frankfort-Nachmias and Nachmias offer the following when assigning numbers for variables that can be ranked (Frankfort-Nachmias and Nachmias 2000, 304-309). First, assigned numbers should make intuitive sense. Higher scores, for example, should be assigned higher code numbers.
Second, the coding categories must be mutually exclusive. That is, each unit of analysis should fit into one and only one category. Consider the following measurement scheme for a respondent's level of income:

1. 0–$20,000

1. $20,000–$40,000

1. $40,000–$60,000

1. $60,000–$80,000

The example violates the mutually exclusive rule.
Third, the coding scheme must be exhaustive. This means that every response must fit into a category. Looking at the income example just presented, you can readily see that anyone earning more than $80,000 does not have a category representing their income level. Thus, perhaps an additional category could be coded as “Greater than $80,000.”

no 2 amounts should be use twice

Last, categories must be specific enough to capture differences using the smallest possible number of categories. Frankfort-Nachmias and Nachmias call this requirement the “criterion of detail.” In other words, while you want to ensure you meet the criteria, you do not want to have too many categories for a particular variable. For the income example in this section, you would not want to code income as under $1,000; $1,000 to $2,000; $2,001 to $3,000; and so on.

The statement that Likert scales are more accurate than Guttman scales is false. Both Likert and Guttman scales are commonly used in research to measure attitudes or opinions, but they have different characteristics. While Likert scales measure the degree of agreement or disagreement with a statement using a range of response options, Guttman scales are designed to measure a respondent's level of agreement with a set of statements on a cumulative scale. The accuracy of a scale depends on various factors such as the quality of the items, the sample size, and the context of the study. Therefore, it is not accurate to claim that one type of scale is universally more accurate than the other.

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Metric measurement is more precise than either nominal or ordinal measurement. Numbers do not just stand for categories, as in nominal and ordinal measurements. There are two types of metric measurement: interval measurement and ratio measurement. Interval and ratio measurements are very similar. With each level, the values assigned to the classes of a variable have meaning

Methodological triangulation combines two or more information collection methods in the study of a single concept. It utilizes the strengths of various methods to gather comprehensive and reliable data. By using surveys to collect information and complementing it with discrete observation and activity charting, this method can help compensate for any potential bias that may arise from interviews and surveys.

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Page 184 Ordinal variables, like their name suggests, can be ordered or ranked. Questions such as a survey rating scale (poor, fair, good, excellent) or level of agreement with a statement (strongly agree, somewhat agree, agree, disagree) used in attitude surveys are examples of ordinal variables. The category has a natural order to it, but the distance between choices are not equivalent or standardized by set intervals

When collecting data, it is ideal to measure concepts at the highest level possible. This allows for more advanced mathematical manipulation and sophisticated statistical analysis. By measuring concepts at a higher level, researchers have more flexibility in analyzing the data and drawing meaningful conclusions. This can lead to more accurate and reliable results in their research.

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The given coding scheme includes all possible options or categories for the responses. It covers every possible range of income, including those below $25,000, between $25,001 and $50,000, and above $50,000. Therefore, it can be considered exhaustive as it leaves no option or response uncovered.

Nominal-level variables are always discrete because they represent categories or groups that cannot be ordered or ranked. These variables have distinct categories with no inherent numerical value or order. For example, variables like gender, ethnicity, or marital status are nominal variables as they represent different categories without any specific order or ranking. In contrast, ratio, ordinal, and interval variables involve numerical values and can be continuous or discrete depending on the context.

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Continous and discrete variables

When coding categories of each unit of analysis, the term "mutually exclusive" means that each unit can only fit into one category and cannot fit into multiple categories at the same time. In this case, the given examples of 0-10, 11-20, and 21-30 represent mutually exclusive categories because each unit can only belong to one of these ranges and cannot belong to multiple ranges simultaneously.

Ratio level data is reported in the ten-year census. More specific examples include a state's population, the number of senior citizens living in cities, and the number of African Americans living in a state. All percentages and proportions are also ratio because we start out with actual measurements to derive them.

Nominal measures are the weakest or least precise level of measurement. This is because they only categorize data into distinct groups or categories, without any inherent order or numerical value. Nominal measures simply assign labels or names to different observations or variables, making them the least precise form of measurement. In contrast, ordinal measures have a natural order or ranking, interval measures have equal intervals between values, and ratio measures have a true zero point and meaningful ratios between values.

Investigator triangulation involves the use of multiple observers for the same research activity. This method reduces potential bias that might come from a single observer. By having several interviewers, analysts, and decision makers, different perspectives and interpretations can be gathered, increasing the reliability and validity of the research findings.

Scales involve the principle of unidimensionality, which means that the items comprising the scales reflect a single dimension or concept. Scales are used in measurement to assign numerical values to different levels of a specific characteristic or attribute. These numerical values help in quantifying and comparing the levels of the concept being measured. Therefore, scales are a reliable and valid tool for measuring and assessing various constructs or variables in research and data analysis.

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page 185 Interval does not have an absolute zero point. A good example of an interval variable is the Fahrenheit thermometer.

This question is asking about the type of measurement that lacks any sense of relative size or magnitude and only allows you to say that the classes of a variable are different, without any mathematical relationship between the classes. The correct answer is "nominal".

Page 183 Nominal measurement merely involves the assignment of numeric labels to the categories of a variable. Race, as discussed, is an example of a nominal-level measure. Each time the program counts a 1, it is also counting an African American. There are many other examples of nominal measures applicable to political research. Gender, political party affiliation, nationality, and college major are common examples that you will probably use in your studies

Ratio-level variables can be continuous or discrete. Ratio level of measurement is the highest level of measurement that provides the most precise and comprehensive information about a variable. It has all the properties of interval level, along with a true zero point, which allows for meaningful ratios and comparisons between values. Therefore, ratio-level variables can take on any numerical value, whether they are continuous (can take on any value within a range) or discrete (can only take on specific values).

The given question is an example of a Likert Scale because it asks for the respondent's level of agreement or disagreement with a statement. The options provided range from strongly disagree to strongly agree, allowing the respondent to express their opinion on the Court's decision.

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