Exam 2: Quantitative Data Collection and Analysis Flashcards

1
Q

descriptive statistics

A
  • summarize or describe data

- reduce large sets of data into meaningful pieces

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2
Q

inferential statistics

A
  • draw conclusions about a population based on data from sample
  • looks at differences between groups
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3
Q

correalational statistics

A
  • strength of relationship between two variables
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4
Q

what is quantitative data analysis

A
  • process of organizing data to draw conclusions
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5
Q

how is data in descriptive statistics displayed?

A
  • through frequency distribution
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6
Q

what is frequency distribution in descriptive statistics?

A
  • a table or figure that shows number of data observations that fall into specific intervals
  • histograms, pie charts, line charts, table
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7
Q

What is a measure of central tendency?

A
  • it describes the center point of data using a single value

- mean, median, mode

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8
Q

what is a measure of variability/ measure of data dispersion

A
  • describes how far a data set has strayed away from the mean
  • standard deviation
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9
Q

mean

A

average of adding all values and dividing

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10
Q

median

A

middle score

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11
Q

mode

A

most frequently occurring

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12
Q

Normal distribution of central tendency

A
  • smooth bell shape curve

- mean, median mode are same value

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13
Q

skewness

A
  • negative is skewed left

- positive is skewed right

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14
Q

range

A

difference between highest and lowest score (11 - 4 = 7)

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15
Q

variance

A

reflects variation of distribution within set of scores; used to calculate standard deviation

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16
Q

standard deviation

A

spread the data of the mean; square root of variance

- most commonly used measure of variance

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17
Q

empirical rule

A

if distribution scores follow a bell-shaped curve we would expect 68% in 1 SD, 95% within 2 SD, 99.7% within 3 SD.

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18
Q

Descriptive statistics is split into what 2 categories?

A
  • central tendency (mean, median, mode)

- variability (standard deviation, range)

19
Q

one tailed hypothesis test

A
  • directional test
  • research suspect intervention is better, sets up hypotheses to reject null hypothesis
  • mean 1 greater or less than mean 2
20
Q

two tailed test

A
  • non-directional test
  • consider either possibility
  • null hypothesis rejected if result fails at either tail
  • mean 1 not equal to mean 2
21
Q

what is probability (p-value)

A
  • significance level ( denoted by alpha)
22
Q

what does low probability (p-value) indicate? less than .05

A
  • lower chance to find effect due to chance
  • probability of found different due to chance is low
  • reject null hypothesis
23
Q

Type I error

A
  • error created by rejection null hypothesis when it is true

- test that shows patient has disease but really doesn’t

24
Q

Type II error

A
  • accepting null hypothesis when its false

- blood test failing to test for disease when disease is present

25
Student t-test (independent t-test)
- used when there is no relationship between two groups
26
paired t -test
- used when groups are same | - compares pre and post test of same individuals
27
analysis of variance (ANOVA)
- test difference between two means - computes multiple t-test at one time and tells you if there is difference between groups - compares within group difference between group difference
28
post-hoc analysis
- after ANOVA, this tells us which groups differ from one another on which specific variables - tukey scheffe and duncan adjustments test
29
parametric data
samples that assume normal distribution (bell curve) - have homogenous variance - continuous data i.e. weight
30
non-parametric data
- samples that do not have homogenous variance - does not follow normal distribution - nominal (chi square), ordinal scale (mann-whitney U)
31
Chi-square test
- non parametric statistic - analyzes frequencies or proportions - sum of differences rather than expectations - one tailed test - coin toss
32
correlation coefficient
- strength of relationship bt two variables - no cause - effect can be inferred - ranges from +1.0 to -1.0; 0 shows no relationship
33
Know degrees of correlation: strong positive, strong negative, weak positive, weak negative, etc..
visualize it!
34
correlational statistics relationship test: person r test
used with parametric data
35
correlational statistics relationship test: spearman rank-order correlation coefficeint
- used with non-parametric data
36
regression analysis - predictive values
- as one variable increases predict increase in other variables
37
simple regression
examine relationship bt dependent and independent variable
38
multiple regression
used when there is more than one independent variable
39
factor analysis
- identification of factors that compose a construct - group items together to create a factor i. e. sensory profile
40
considering how to choose the right statistic
- consider research question - how many groups compared - simplest way to understand data
41
Ratio
- continuous - numeric - can take any value - height, weight, age
42
Interval
- discrete - countable - ordered numeric; no smooth value to value transition - # of students, # of strokes
43
Ordinal
- Categorical - Natural order - Education Level, socioeconomic status
44
Nominal
- Categorical - No natural order - Gender, Ethinicity