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Section 1-2

Section 1-2. Variables and types of Data. Objective 3: Identify types of Data. In this section we will detail the types of data and nature of variables. Data. 1-2 Variables and Types of Data. Qualitative Categorical. Quantitative Numerical, Can be ranked. Discrete Countable

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Section 1-2

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  1. Section 1-2 Variables and types of Data

  2. Objective 3: Identify types of Data • In this section we will detail the types of data and nature of variables.

  3. Data 1-2 Variables and Types of Data Qualitative Categorical Quantitative Numerical, Can be ranked Discrete Countable 5, 29, 8000, etc. Continuous Can be decimals 2.59, 312.1, etc. Bluman Chapter 1

  4. Qualitative variables • Are variables that can be placed into distinct categories, according to some characteristic or attribute such as: • Gender • Political affiliation • Grade level

  5. Quantitative variables • Are numerical and can be ordered or ranked. For example: • Age • height • weight

  6. Discrete variables • Assumes values that can be counted. For example: • Number of coffee shops in NoPo. • Number of turkeys your mama is going to cook on Thanksgiving. • Number of unwanted hair on your chin.

  7. Continuous variables • Result from a measurement and can take on infinite number of values, including decimal. For example: • Temperature of a fully cooked turkey.. • Weigh of a turkey • Ounces of gravy on mashed potatoes.

  8. Rounding • Continuous data is rounded. • Continuous data is usually rounded to the nearest given unit. • Raw data and boundaries

  9. 1-2 Recorded Values and Boundaries 14.5-15.5 cm 85.5-86.5 F 0.425-0.435 sec 1.55-1.65 g Bluman Chapter 1

  10. About Boundaries • Boundaries always have one more decimal place than the data. • Boundaries always end in a 5. • We will cover boundaries further in chapter 2.

  11. Examples Page 8 Levels of measure Ordinal Nominal Interval Ratio

  12. Nominal– categorical (names) • When measuring using a nominal scale, one simply names or categorizes responses. Gender, handedness, favorite color, and religion are examples of variables measured on a nominal scale. • The essential point about nominal scales is that they do not imply any ordering among the responses. For example, when classifying people according to their favorite color, there is no sense in which green is placed "ahead of" blue. • Responses are merely categorized. Nominal scales embody the lowest level of measurement. Bluman Chapter 1

  13. Ordinal– nominal, plus can be ranked (order) • A researcher wishing to measure consumers' satisfaction with their microwave ovens might ask them to specify their feelings as either "very dissatisfied," "somewhat dissatisfied," "somewhat satisfied," or "very satisfied." The items in this scale are ordered, ranging from least to most. • This is what distinguishes ordinal from nominal scales. Unlike nominal scales, ordinal scales allow comparisons, ranking or “ordering” Bluman Chapter 1

  14. Interval– ordinal, plus intervals are consistent Interval scales are numerical scales in which intervals have the same interpretation throughout. Interval scales are not perfect, however. In particular, they do not have a true zero point even if one of the scaled values happens to carry the name "zero." Bluman Chapter 1

  15. Ratio– interval, plus ratios are consistent, true zero • The ratio scale of measurement is the most informative scale. It is an interval scale with the additional property that its zero position indicates the absence of the quantity being measured. • You can think of a ratio scale as the three earlier scales rolled up in one. Like a nominal scale, it provides a name or category for each object (the numbers serve as labels). Like an ordinal scale, the objects are ordered (in terms of the ordering of the numbers). Like an interval scale, the same difference at two places on the scale has the same meaning. And in addition, the same ratio at two places on the scale also carries the same meaning. Bluman Chapter 1

  16. Consequences of level of measurement Why are we so interested in the type of scale that measures a dependent variable? The crux of the matter is the relationship between the variable's level of measurement and the statistics that can be meaningfully computed with that variable. Bluman Chapter 1

  17. Let's practice The website above also some more examples and explanation of what we just covered. Bluman Chapter 1

  18. 1-2 Variables and Types of Data Determine the measurement level. Variable Nominal Ordinal Interval Ratio Level Hair Color Yes No Nominal Zip Code Yes No Nominal Letter Grade Yes Yes No Ordinal ACT Score Yes Yes Yes No Interval Height Yes Yes Yes Yes Ratio Age Yes Yes Yes Yes Ratio Temperature (F) Yes Yes Yes No Interval Bluman Chapter 1

  19. On your own • Study the examples listed on table 1-2 on page 8. • Do applying concepts on page 9 • And answer questions 1-10 on page 26. Bluman Chapter 1

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