This document discusses various concepts related to research instruments and data collection. It defines key terms like data, instrumentation, validity, reliability, and objectivity. It describes different ways to classify instruments based on who provides the data, the method of collection, who collects it, and the type of response. Various types of instruments are outlined, including rating scales, questionnaires, tests, and unobtrusive measures. The document also discusses different types of scores, measurement scales, techniques for summarizing quantitative data like frequency distributions and polygons, and how to properly code and prepare data for analysis.

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-