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Data & Scores Data & Scores Presentation Transcript

  • DATA/SCORES Pamela M. Veroy RN, MAN
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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 .
  • 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.
  • 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.
  • 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.
  • 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.
  • 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
  • Measurements Scales - 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 Measurement Scale Characteristics NOMINAL ORDINAL INTERVAL RATIO
  • 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?
  • 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.
  • 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
  • Frequency Polygon of Table 7.3 Data
  • 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
  • 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”
  • -END-