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 ScalesMeasurement

ScaleCharacteristics

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

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

96-10091-9586-9081-8576-8071-7566-7061-6556-6051-5546-5041-5536-40

0002256945200

N= 35

0233434453211

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”

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