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Sampling, Measurement, Validity and Reliability

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Title: Sampling, Measurement, Validity and Reliability


1
Sampling, Measurement, Validity and Reliability
2
Sampling
  • Many full scientific texts have been written
    about this subject, but it is also a general
    phenomena we all come to conclusions based on
    samples of experience that we have had.
  • Why sample
  • More economical and efficient
  • May be more accurate
  • More able to control for biases due to over- or
    under-representation of some population segment

3
Sampling Terms to Know
  • 1. Sampling the process of selecting a part of
    the population to represent the entire population
  • 2. Population an entire aggregation of cases
    which meet a designated set of criteria all
    nurses, all BSN nurses, all nurses in Hamilton
    County
  • Accessible population all cases which conform
    to the criteria and which are accessible for the
    study
  • Target population the entire field of cases
    which conform to the criteria

4
Sampling Terms to Know
  • 3. Sampling Unit elements or a set of elements
    used for sampling if you want an element of BSN
    students, send questionnaires to BSN schools
    the school is the sampling unit and each student
    is an element (the most basic unit about which
    information is collected).

5
Sample Size and Sample Error
  • Sample size Always use the largest sample
    possible. In general, a sample size should be at
    least ten for every subdivision of the data.
    20-30 is preferable. The absolute size is more
    important than the relative size.
  • Sampling error the difference between values
    obtained from the sample and the values of the
    whole population.
  • Sampling Bias This occurs when samples are not
    carefully selected, i.e. some parts of the
    population are left out internet samples,
    volunteers

6
Steps in Sampling
  • Identify the target population
  • Identify the part of the population that is
    accessible to you
  • Ask the sample subjects for cooperation
  • Select subjects randomly if possible
  • Collect data
  • Interpret the results based on the sample be
    realistic and conservative

7
Types of Samples
  • Representative sample a sample in which the key
    characteristics of the elements closely
    approximate those of the population
  • Probability sample a sample that uses some form
    of random selection in choosing the elements
    the researcher can specify the probability that
    each element of the population would be included
  • Non-probability sample a sample in which the
    elements are not chosen by random selection

8
Types of Samples
  • Non-probability sample the elements are
    selected by non-random methods. This type of
    sampling is more convenient and economical.
  • Convenience sample this is where the researcher
    uses the most readily available persons also
    called accidental samples such as the first
    persons who come into a supermarket or a clinic.
    This is the weakest method of sampling
  • Snowball sample persons known to the researcher
    are asked to participate then the elements are
    asked to give names of others they know with the
    same characteristic.

9
Types of Samples
  • Quota sampling the researcher identifies
    different strata of the population and determines
    the proportions of elements needed from those
    various segments of the population (establishes a
    quota and fills the quota as the elements present
    themselves)
  • Purposive sampling (judgmental sampling) the
    researchers knowledge about the population is
    used to handpick the elements to be included so
    that the sample meets the widest type variety
    or thetypical element. It is good for testing
    instruments or validating tests, but it does risk
    bias.
  • Sequential sampling Sample one person at a time
    until you prove or disprove a statement (Seven
    out of 10 times ASA works better.)

10
Quota Sampling
  • Smokers Non-Smokers
  • Males lll llllI
    Illll
  • Females IIIII III IIIII III

11
Types of Sampling
  • Probability Sampling
  • Simple random sampling establish a list from
    which the sample will be chosen (a sample frame)
    and number all elements consecutively. Use a
    table of random numbers or a computer to draw
    numbers. This guarantees that the differences in
    attributes of the sample and of the population
    are purely a function of chance and the the
    probability of selecting a deviant sample is low.
    As the size of the sample increases, the
    probability of its deviance from the attributes
    of the population decreases.

12
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13
Types of Sampling
  • Stratified random sample mutually exclusive
    segments of the population are established by one
    or more specifications (male/female below
    30yrs/30-45yrs/46yrs and over diploma/ADN, BSN)
    and elements are picked randomly from each
    stratification of the population. (Decisions
    about which strata the elements belong to are
    made before the selection as opposed to quota
    sampling where the person is questioned and then
    put into a stratum.) This method increases
    representativeness
  • Proportional elements in proportion to
    population
  • Disproportional to compare greatly unequal
    proportions

14
Stratified Random Sampling
  • Numbered list of male smokers - 45
  • Numbered list of female smokers - 33
  • Numbered list of male non-smokers - 47
  • Numbered list of female non-smokers 37
  • ________________________________
  • Smokers Non-smokers
  • Male 10/45 random 10/33 random
  • Female 10/47 random 10/37 random

15
Types of Sampling
  • Cluster sampling this is a process in which a
    successive random sampling of units is drawn
    (states, then cities, then districts, then
    blocks, then households) moving from the largest
    unit down to the basic element. It is also
    called multi-staged sampling. The sampling error
    may be larger with it.
  • Systematic sampling the researcher selects
    every k th person from a list or a group. It is
    not random if you select every 10th person
    walking by nor is it random unless you draw the
    first number to start the list.

16
Random Assignment
  • Random assignment of subjects to groups
  • This eliminates as much systematic bias as
    possible. Each subject has an equal chance to be
    in any of the study groups and differences are
    explained on the basis of the experimental
    conditions rather than on differences in
    subjects.
  • Random assignment of treatments to groups
  • Exemplified in double-blind studies carried out
    in clinical trials. This is particularly useful
    when the researcher has to deal with intact
    groups such as in classrooms or on hospital units

17
Measurement
  • Measurement is assigning numbers to objects to
    represent quantities of attributes or concepts.
  • Measurement procedures are operational
    definitions of concepts or attributes the
    concept or attribute should really exist although
    it may be an abstraction
  • Measurement always deals with abstraction you
    dont measure a person, but a characteristic of
    that person

18
Measurement
  • Numbers are assigned to quantify an attribute
    whatever exists, exists in some amount and can
    be measured The variability of an attribute is
    capable of numerical expression which signifies
    how much of the attribute is present in the
    element.
  • Rules for measuring may have to be invented. The
    researcher must specify under what conditions and
    according to what criteria, and in what
    increments, numerical values are to be assigned.
  • Measurement should have a rational correspondence
    to reality

19
Advantages of Measurement
  • What would you work with if you did not have
    measurement of height, weight, temperature
    intuition, guesses, personal judgment
  • Objectivity scoring minimizes subjectivity.
    Analytical procedures are not subjective
  • Communication numbers constitute a
    non-ambiguous language

20
Levels of Measurement
  • Nominal scale measurement at its weakest
    numbers or other symbols are used to classify an
    element such as a psychiatric diagnostic number
    - 295. You can partition a given class of
    elements into a set of mutually exclusive
    subclasses 295.30, 295.20. The only
    relationship involved is equivalence () The
    kinds of statistics that can be used with this
    type of measurement are modes and frequency
    counts. You can test hypotheses regarding
    distribution of cases among categories (X2).

21
Nominal Level
  • Marital Status Gender
    Total
  • Male Female
  • Married 60 100
    160
  • Single 140 200
    340
  • Total 200 300
    500
  • ____________________________________
  • Calculate the percentages.

22
Levels of Measurement
  • Ordinal scale this measurement shows
    relationships among classes such as higher than ,
    more difficult than, etc. It involves two
    relations equivalence () and greater than (gt)
    The researcher can test hypotheses using
    non-parametric statistics of order and ranking
    such as the Spearman Rank Order Correlation or
    the Mann Whitney U.

23
Ordinal Level
  • Considerable Moderate
    Little
  • Social Support Social Support
    Social Support
  • Bulimics 13 30 57
  • Non-
  • Bulimics 35 55 10

24
Levels of Measurement
  • Interval scale this is similar to the ordinal
    scale, but the distance between any two numbers
    is of a known size. All parametric tests are
    able to be used mean, standard deviation,
    Pearson correlation, T-test, F-test, etc. It
    involves three relations equivalence (),
    greater than (gt), and a known ratio of any two
    intervals.

25
Interval Level
  • Abused Women Score Before Score After
    Difference
  • 1 36 42 6
  • 2 25 40 15
  • 3 39 45 6
  • 4 40 40 0
  • 5 41 44 3
  • 6 35 40 5

26
Levels of Measurement
  • Ratio scale it is like the interval scale, but
    it has a true zero point as its origin. You can
    use arithmetic with it and all parametric tests
    as well as those involving geometric means. It
    involves four relationships equivalence(),
    greater than(gt), the known ratio of any two
    intervals, and the known ratio of any two scale
    values.

27
Ratio Level
  • Experimental Group
    Control Group
  • Sample Size 35 lbs 35 lbs
  • Mean Weight Loss 26 lbs 19 lbs
  • Standard Deviation 2 lbs 4.5 lbs

28
Reliability and Validity - Criteria for Assessing
Measuring Tools
  • Every score is part true and part error
  • Sources of errors in scores
  • Situational contaminants
  • Response set bias
  • Transitory personal factors
  • Administration variations
  • Instrument clarity
  • Response sampling (a person scores 95 and 90 on
    two tests which claim to test the same thing)
  • Instrument format

29
Reliability
  • This is the major criterion for assessing a
    measuring instruments quality and adequacy. It
    is the consistency with which the instrument
    measures the attribute it is supposed to be
    measuring.
  • The reliability of an instrument is not a
    property of the instrument, but rather of the
    instrument when administered under certain
    conditions to a certain sample. (A death anxiety
    instrument would not measure the same when given
    to teenagers as it measures for geriatric
    patients.)

30
Ways to Check Reliability
  • Stability (test-retest reliability) the same
    test is given to a sample of individuals on two
    occasions, then the scores are compared by
    computing a reliability coefficient. (A
    reliability coefficient is a correlation
    coefficient between the two scores)
  • Internal consistency (homogeneity) all of the
    subparts of the instrument must measure the same
    characteristic. Use the split-half technique
    split the test items in half, score each half,
    then compare the scores using a correlation
    coefficient or compare each item (by
    correlation) with the total score (a)

31
Ways to Check Reliability
  • Equivalence can be tested in two ways
  • 1. Using two or more forms of a test to see if
    they are equal
  • 2. Inter-rater reliability
  • Carefully train observers, develop clearly
    defined,non-overlapping categories, and use
    behaviors that are molecular rather than molar
  • Two or more observers watch the same event
    simultaneously and independently record variables
    according to a plan or code
  • Reliability is computed
  • Reliability number of agreements
  • number of agreements
    number of disagreements


32
Ways to Check Reliability
  • Interpretation of reliability coefficients
  • If you are interested only in group-level
    comparisons, a reliability coefficient of .70 or
    even .60 is sufficient (male/female, Dr./nurse,
    smoker/non-smoker)
  • If you are interested in decisions about
    individuals, such as who gets into school, then a
    coefficient of .90 or higher is needed
  • If the coefficient were .80, then 80 of the
    scores variability would be true variability and
    20 would be extraneous

33
Ways to Improve Reliability
  • Add more items
  • Have a more varied group of subjects the more
    homogeneous the group the lower the reliability
    coefficient

34
Validity
  • The degree to which an instrument measures what
    it is supposed to be measuring. Validity is
    difficult to establish. An instrument that is
    not reliable cannot be valid, but, an instrument
    can be reliable and still not be valid. (Example
    a patient satisfaction scale does not measure
    quality of nursing care.)

35
Aspects of Validity
  • Face Validity
  • Content Validity
  • Criterion-related validity
  • Construct validity

36
Validity
  • Face validity refers to whether the instrument
    looks as though it is measuring the appropriate
    attribute. It is based on judgment. There are
    no objective criteria used for assessment of its
    appropriateness.

37
Types of Instrument Validity
  • Content validity looks at the sampling adequacy
    of the content area used especially for tests
    that measure knowledge of a specific content
    area. It is evaluated by examining the extent to
    which the content of the test represents the
    total domain of behaviors encompassing the
    ability being measured. It is usually measured
    by expert opinion. It is based on judgment. The
    more experts who agree on the content to be
    included, the better a blueprint could be
    developed or a content validity index (CVI) could
    be developed see method in Polit, p. 459

38
Types of Instrument Validity
  • Criterion-related validity this establishes a
    relationship between the instrument and some
    other criterion that is accepted as measuring the
    same attribute. The scores on both should
    correlate highly indicating directly how valid
    the instrument is.
  • Concurrent validity the criterion measure is
    obtained at the same time the test is given
  • Predictive validity the criterion measure is
    obtained some time after the test is given and
    the test is used to predict future performance on
    the criterion measure

39
Types of Instrument Validity
  • Construct validity asks the question Is the
    abstract concept/construct under investigation
    being adequately measured with this instrument
    is there a fit between the conceptual definition
    and the operational definition of a variable. One
    way to test it is through the known groups
    technique groups expected to differ on the
    critical attribute are tested and scores should
    be different. If the test is a sample of
    behaviors characteristic of the construct. Its
    items must be representative of the content of
    the construct. A good way to support this
    assumption is to use factor analysis.

40
Types of Instrument Validity
  • Statistical Conclusion Validity determines
    whether the conclusions drawn about the
    relationships are an accurate reflection of the
    real world and/or whether the differences drawn
    from statistical analyses are an accurate
    reflection of the real world.

41
Benefits and Limitations of Statistical
Conclusion Methods
  • Benefits
  • Enhances interpretability of relationships
  • Easy and economical
  • Can be used with a large number of extraneous
    variables
  • Limitations
  • Requires knowledge of which variables to control
    to enhance the independent variable
  • Requires statistical sophistication regarding
    statistical power and statistical precision

42
Interpretation of Validity
  • Validity cannot be proved but it can be
    supported. The researcher does not validate the
    instrument itself, but actually some application
    of the instrument

43
Other Criteria for an Instrument
  • Efficiency the number of items, the time it
    takes to complete
  • Sensitivity how small a variation in the
    attribute can be detected and measured use item
    analysis of tests
  • Objectivity two researchers should agree about
    its measurement
  • Comprehensibility subjects can understand what
    to do with it
  • Balance to minimize response sets
  • Time allowance adequate time is available for
    completion
  • Simplicity
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