Discuss

1. DATA/SCORES
Pamela M. Veroy RN, MAN

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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. Frequency Polygon of Table 7.3 Data

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. 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. -END-