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Research > Results > Flashcards

Results Flashcards

1
Q

measurement

A

Assigning numbers to objects or events

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2
Q

instrumental measurement

A

◦ Audiometer ◦ Nasometer ◦ Visipitch

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3
Q

quantified measurement

A

◦ Hearing level ◦ Nasality ◦ Voice Fo

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4
Q

behavioral measurement

A

◦ Tests ◦ Surveys ◦ Questionnaires

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5
Q

nominal

A

attributes are only named -Nominal data deals with names, categories, or labels. -Data at the nominal level is qualitative

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6
Q

ordinal

A

-attributes can be ordered -Data at this level can be ordered, but no differences between the data can be taken that are meaningful. Subjects were classified based on degree of hearing loss 1 = Mild 2 = Moderate 3 = Moderately-Severe 4 = Severe 5 = Profound

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7
Q

interval

A

distance is meaningful -The interval level of measurement deals with data that can be ordered, and in which differences between the data does make sense. -There is an equal distance between each value. -Data at this level does not have a starting point. ex) Words were presented at 3 different levels: ◦ Soft = 40 dB ◦ Medium = 60 dB ◦ Loud = 80 dB

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8
Q

ratio

A

absolute zero

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9
Q

measurement accuracy

A

Observed score = true score + error X=T+E - True score obtained under ideal conditions - Error occurs because “ideal” never exists - Measurement with less error = more reliable

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10
Q

unsystematic (random) errors

A
  • Random errors in experimental measurements are caused by unknown and unpredictable changes in the experiment. - Random errors often have a normal distribution
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11
Q

sources of random error

A
  • Fluctuation in equipment - Changes in the environment - Changes in the subject - Changes in the observer - Measure is limited sample of behavior that is not stable
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12
Q

systematic error

A
  • Systematic errors usually come from the measuring instruments. ◦ There is something wrong with the instrument ◦ The instrument is used incorrectly by the experimenter - There’s something wrong with the examiner :(
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13
Q

reliability

A

Can we depend on a measurement? Precision and Accuracy

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14
Q

precision

A

◦ Stability of the measurement ◦ Reproducibility

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15
Q

accuracy

A

◦ Closeness of obtained score to expected or true score

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16
Q

how to estimate the reliability of a measurement

A
  • Test-retest - Coefficient of stability - Equivalence of Measurement ◦ Alternate equivalent forms - Coefficient of equivalence
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17
Q

internal consistency

A

◦ How well each item is measuring same thing?

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18
Q

standard error of measurement

A
  • It is an estimate of how often you can expect errors of a given size - Based on the reliability coefficient and the test’s standard deviation - Low SEM = high level of score accuracy
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19
Q

inter-observer reliability

A

◦ Two or more observers measuring the same event ◦ Equivalence

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20
Q

intra-observer reliability

A

◦ One observer measuring event at different times ◦ Stability

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21
Q

coefficient of correlation

A

• A correlation coefficient is a statistical summary of the relation between two variables.
• It is the most common way of reporting the answer to such questions as the following:
▫ Does this test predict performance on the criterion test?
▫ Do these two tests measure the same thing?
▫ Are these two constructs related?

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22
Q

validity of measurements

A

Degree to which the measurement measures what it is supposed to measure
Are we really measuring what we intend to measure?
Truthfulness of measure.

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23
Q

internal validity

A

Is researcher justified in concluding cause/effect
relationship?
▫ Were variances controlled to avoid contamination
of results?

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24
Q

external validity

A

Can findings can be generalized to population as a whole?

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25
Q

content validity

A

Subjective, logical assessment of the measurement instrument
•What behaviors are being measured?
•How well does instrument measure a sample of the behaviors?
•Are you really measuring the behavior that you think you are measuring?

26
Q

criterion reliability-concurrent reliability

A

Do results agree with results of current, well validated test of the same thing?
▫Scores on Spivak’s Handy Dandy IQ Test (SHDIQT) are the same as those obtained on the Wechsler Intelligence Scale

27
Q

criterion reliability-predictive reliability

A

▫Criterion test administered after measurement
▫Did measurement predict criterion results?

28
Q

construct validity

A

•The degree to which a test measures an intended hypothetical construct
▫ie. Is the variable you’re testing actually measured by the experiment?
▫I think I’m measuring memory, but maybe I’m just measuring attention, level of interest, IQ, language proficiency—–

29
Q

threats to internal validity-history

A

•Any event outside of the research study that can alter or affect subjects’ performance.
•Randomization often minimizes this risk
▫outside events that occur in one group are also likely to occur in the other

30
Q

threats to internal validity-maturation

A

•The natural physiological or psychological changes that take place as we age.
•Maturation can play a major role in longer-term studies.
▫Was it the treatment that caused outcome or maturation of the subject?
▫Would result have occurred naturally with time?
▫This is especially important in childhood

31
Q

threats to internal validity-testing

A
  • People tend to perform better at any activity the more they are exposed to that activity.
  • Act of taking a pre-test may affect performance on post-test (Re-active Pre-test)
  • How can I avoid affect of pre-test?
32
Q

threats to internal validity-instrumentation

A
  • Changes in scores may be related to the changes in the instrument or test rather than the independent variable.
  • How can I control for influence of instrument?
33
Q

threats to internal validity-statistical regression

A
  • Refers to the tendency for subjects who score very high or very low to score more toward the mean on subsequent testing.
  • Statistical regression, or regression to the mean, is a concern especially in studies with extreme scores.
34
Q

threats to interal validity-subjects

A
  • Manner in which subjects are selected and assigned to groups
  • Groups equal on important dimensions
  • Subject matching or Randomization
35
Q

experimenter bias

A

(Rosenthal Effect)
•Researchers may be biased toward the results we want
•This bias can effect our observations
•Control for this bias by blinding

36
Q

mortality

A
  • Mortality, or subject dropout
  • One group may experience greater dropout than another, thereby upsetting equivalence between groups
37
Q

external validity

A

Generalizability of the conclusions

38
Q

threats of external validity

A

•Effect of study location
•Reactive effects of experimental arrangements
▫Generalize from one context to another
•Studies in laboratory environment often have poor external validity

39
Q

demand characteristics (john henry effect, hawthorne effect)

A
  • Performance of subjects influenced by the anticipated results of a study
  • Exhibit performance that they believe is expected of them.
  • control for this by-Blinding
40
Q

order/carryover effects

A

•Order effects refer to the order in which treatment is administered and can be a major threat to external validity if multiple treatments are used.

41
Q

treatment interaction effects

A
  • Treatment can affect people differently depending on the subject’s characteristics.
  • Potential threats to external validity include the interaction between treatment and any of the following: selection, history, and testing.
42
Q

data distribution

A

All quantitative data forms a distribution
◦Characteristics
Central tendency
Variability
Skewness
Kurtosis

43
Q

normal data distribution

A

Normal distribution is symmetrical
The mean, median, and mode of a normal distribution are identical
◦68% of the population fall between one and two standard deviations from the mean
◦ 95% of the population fall between two standard deviations from the mean.

44
Q

skewed data distribution

A

The skew of a distribution refers to how the curve leans.
The more skewed a distribution is, the more difficult it is to interpret.

45
Q

kurtosis

A

Kurtosis refers to the peakedness or flatness of a distribution.
◦leptokurtic.
Positive Kur
◦platykurtic.
Negative kur
◦mesokurtic
Normal distribution.

46
Q

what does the nature of data distribution affect?

A

Type of distribution will effect type of statistics used
◦Normal distribution: Parametric Statistics
◦Non-normal: Non-parametric Statistics

47
Q

how to test for skewness to the right

A

55, 55, 56, 57, 60, 61, 63, 66, 66, 310
Mean = 82.4
SD =75.57 so… SD x 2 = 76 x 2 = 152
Mean minus 2xSD = 82 – 152 = -70
Result should not be lower than 0
Negative # indicates skew to the right

48
Q

how to test for skewness to the left

A

12, 46, 55, 55, 60, 65, 65
Mean = 45
SD = 18 so… SD x 2 = 18 x 2 = 36
Mean plus 2xSD = 45 + 36 = 81
If answer is larger than largest score (65) than distribution is skewed to left
This distribution is not skewed to left.

49
Q

variability

A

How scores differ from mean
◦Spread out
◦Cluster around the mean

50
Q

measures of variability

A

Range
◦Lowest to highest score
Variance
◦Average of the squares of distance from mean
Standard Deviation
◦Square root of the variance
Interquartile range ( Q)
◦When values not symmetrical around mean
◦Range of scores in middle 50% of scores
◦Excludes extreme scores in lower & higher 25%

51
Q

goals of inferential stats

A

to determine what might be happening in a population based on a sample of the population and to determine what might happen in the future (estimation and prediction)

52
Q

confidence intervals

A
  • -theresdhf -theredsfsdfsdf dfg fgfgdfgertertertertretertthere will be a range of sample means within any population
  • how closely does the mean of our sample match the mean of our population?
53
Q

standard error of the mean

A
  • how far each sample mean varies from the population mean
54
Q

probability of error

A
  • decide prior to the experiment
  • common levels of acceptable error (referred to as significance-.05, .01, .001) abbrviated with “a”
  • How much error are we willing to accept?
  • abbreviated with “p”
55
Q

if null is accepted..

A

then p>a

probability of error is greater than acceptable error

56
Q

if null is rejected…

A

then p<=a

probability of error is less than or equal to acceptable error

57
Q

type I error

A

when the results of research show that a difference exists but in reality there is no difference

  • perhaps also (a) was set too high and lowering the amt of acceptable error (a) would reduce the chances of type 1 error
  • lowering the amt of a also increases the chance of type II error…
58
Q

type II error

A
  • the acceptance of the null hypothesis when in fact the alternative is true: there IS a significant difference in the population but we fail to find this difference
  • study is said to lack power.
  • power refers to a study’s strength to find a difference when it actually exists.
59
Q

power analysis

A
  • probability that your test will find a statistically significant difference when it actually exists.
  • generally accepted that power should be .8 or greater (80%)
  • increases with sample size, means you have collected more info
60
Q

effect size

A
  • when a difference is statistically sig, it doesnt mean that it is big, important, or helpful in decision making
  • cohen’s d= mean1-mean2/SD
  • .1=trivial effect
  • .1-.3=small effect
  • .3-.5=moderate
  • >.5=large difference effect
61
Q

t-test

A
  • tests significance between 2 groups based on mean, SD, and # of subjects
62
Q

anova

A
  • used for more than 2 groups or more than 1 IV or DV
  • tells us significant differences among groups and if variance btw groups is larger than variance within groups

Research (6 decks)

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