Data & Scores

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Data & Scores

  1. 1. DATA/SCORES Pamela M. Veroy RN, MAN
  2. 2. What are Data? • The term “data” refers to the kinds of information researchers obtain on the subjects of their research. • Instrumentation • The term “instrumentation” refers to the entire process of collecting data on the research investigation.
  3. 3. Validity and Reliability • An important consideration in the choice of an instrument to be used in a research investigation is validity; • the extent to which results permit researchers to draw warranted conclusions about the characteristics of the individual studied. • A reliable instrument is one of that gives consistent results.
  4. 4. Objectivity and Usability • Whenever possible, researchers try to eliminate subjectivity from the judgment they make about the - achievement, - performance, - or characteristics of subjects. • An important consideration for any researcher in choosing or designing an instrument is how easy the instrument will actually be to use.
  5. 5. Ways to classify instrument • Research instrument can be classified in many ways. Some of the more common are in terms of ; - who provides the data, - the method of data collection, - who collects the data, and - what kind of response they require from the subjects.
  6. 6. Ways to classify instrument • Research data are data obtained by directly or indirectly assessing the subjects of a study. • Self-report data are data provided by the subjects of a study themselves. • Informant data are data provided by other people about the subjects of a study.
  7. 7. Types of Instruments • Many types of researcher-completed instrument exist. • Some of the more commonly used are • rating scales, • interview schedules, • tally sheets, • flow charts, • performance checklist, • anecdotal records, • and time-and-motion logs.
  8. 8. Types of Instruments • There are so many types of instruments that are completed by the subjects of a study rather than the researcher. • Some of the more commonly used of this type are questionnaires; • self-checklist; • attitude scales; • personalities inventories; • achievement aptitude, • and performance test; • project devices; • and sociometric devices.
  9. 9. Types of Instruments • The types of items or questions used in subject-completed instruments can take many forms, • but they all can be classified as either selection or supply items. • Examples of selection items include true-false items, multiple-items, matching items, and interpretive exercise. • Examples of supply items include short answer items and essay questions.
  10. 10. Types of Instruments • An excellent source for locating already available test in the ERIC clearinghouse on assessment and evaluation. • Unobtrusive measures require no intrusion into the normal course of affairs.
  11. 11. Types of scores • A raw score is initial score obtained when using an instrument; a derived score is a raw score that has been translated into a more useful score on some type of standardized basis to aid I interpretation. • Age/grade equivalents are scores that indicate the typical age or grade associated with an individual raw score. • A percentile rank is the, percentage of a specific group scoring at or below a given raw score. • A standard score is a mathematically derived score having comparable meaning on different instruments.
  12. 12. Measurements Scales • Four types of measurement scales—nominal, ordinal, interval, and ratio—are used in educational research. • A nominal scale involves the use of numbers to indicate membership in one or more categories. - The simplest form of measurement • An ordinal scale involves the use of the numbers to rank or order scores from high to low. - One in which data may be ordered in some way high to low or least to most.
  13. 13. Measurements Scales • An interval scale involves the use of numbers to represent equal intervals in different segments in a continuum. - Possess all the characteristics of an ordinal scale with one individual features. - The distances between the points on the scale are equal.
  14. 14. Measurements Scales • A ratio scale involves the use of numbers to represent equal distances from a known zero point. - An interval scale that does not possess an actual, or true, zero point is called a ratio scale. -example; the zero on the bathroom scale represents zero point or no weight
  15. 15. Measurements Scales Measurement Scale Characteristics NOMINAL ORDINAL INTERVAL RATIO - GROUPS AND LABELS DATA ONLY - REPORTS FREQUENCIES OR PERCENTAGE - RANKS DATA; USES NUMBERS ONLY TO INDICATE RANKING - ASSUMES THAT EQUAL DIFFERENCE BETWEEN SCORES REALLY MEAN EQUEAL DIFFERENCES IN THE VARIABLE MEASURED - ALL OF THE ABOVE, PLUS TRUE ZERO POINT
  16. 16. Technique For Summarizing Quantitative Data • Frequency polygon: Listed below are raw scores of a group of 50 students on a mid- semester biology test. • 64,27,61,56,52,51,3,15,6,17,24,64,31,29,31,29 ,29,31,31,29,61,59,56,34,59,51,38,38,38,38,34 ,36,34,36,21,21,24,25,27,27,27,63 • How many students received a score of 34? • Did most students a score above 50? • How many receive a score below 30?
  17. 17. How to put it (scores) in some order? • Frequency distribution – this is done by listing the scores in rank order from high to low, with tallies to indicate the number of subjects receiving each score. • Group frequency distribution – scores in the distribution are grouped into intervals • Frequency polygon – a graphical display of a data to further understanding and interpretation of quantitative data.
  18. 18. Table 7.3: Comparison of Two Counseling Method (Group Frequency Distribution) Score for “Rapport” Method A Method B 96-100 91-95 86-90 81-85 76-80 71-75 66-70 61-65 56-60 51-55 46-50 41-55 36-40 0 0 0 2 2 5 6 9 4 5 2 0 0 N= 35 0 2 3 3 4 3 4 4 5 3 2 1 1 N=35
  19. 19. Frequency Polygon of Table 7.3 Data
  20. 20. Preparing Data for analysis & Coding • Collecting data must be scored accurately and consistently. • Once scored, data must be tabulated and coded. • - ID number for coding every individual must have 3 digits. If 100 subjects it will be 000-100 • Ex. 000-001 as first individual • Category coding in demographic data can be; e.g. (a), (b), ©, (d) as “1”, “2”, “3”, or “4” respectively
  21. 21. Preparing Data for analysis & Coding • The most important thing to remember is to ensure that the coding is consistent • Once the decision is made about how to code someone, all others must be coded the same way. • Another example: gender coding (categorical data must be coded numerically) • Female – coded as “1” • Male – coded as “2”
  22. 22. -END-

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