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HESP724 Exam 2 > Sampling Statistics > Flashcards

Sampling Statistics Flashcards

1
Q

What is a population?

A
  • All people (or items, locations, etc.) of interest
  • Who you want your results be relevant for, generalize to
  • Can be large (i.e. all 4-year-old children who are English-Spanish bilinguals) or relatively small (i.e. all children in a particular education center)
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2
Q

What is a sample?

A
  • The individuals actually in your study
  • Representative of the population (equal chance of people selected; intended vs. accessible population)
  • Use sample statistics to make inferences about population parameters
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3
Q

What are parameters?

A
  • Numbers used to describe a population

- EX: mu = population mean

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4
Q

What are statistics?

A
  • Numbers used too describe a sample

- EX: x bar = sample mean

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5
Q

What occurs in a census?

A

-Population = Sample

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6
Q

What is sampling bias?

A
  • Failure to identify/examine all members of a population

- Sources: samples of convenience, volunteerism

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7
Q

What are the two main types of sampling?

A
  • Probability

- Non-probability

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8
Q

What are the types of probability sampling?

A
  • Simple random sampling
  • Systematic random sampling
  • Stratified random sampling
  • Cluster sampling
  • Multistage sampling
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9
Q

What are the types of non-probability sampling?

A
  • Convenience sampling

- Purposive sampling

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10
Q

What is probability sampling?

A
  • Uses some form of random selection, based on probability
  • Requires setting up a procedure that assures that the different members of your population have equal probabilities of being chosen
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11
Q

What is simple random sampling?

A
  • Choose such that each sample in the population has an equal chance of being selected (i.e. picking out of a hat)
  • Advantages: equal chances of selection, fair, free from sampling bias
  • Disadvantages: need to know entire population, not most statistically efficient method, luck of the draw (may not represent subgroups well)
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12
Q

What is systematic random sampling?

A
  • Selecting one member randomly and then choose additional members at evenly spaced intervals
  • EX: want a sample of 20/100 students, select 1 every 5th person in the alphabetical class list until you have N= 20
  • Disadvantages: you need a complete listing, need to watch out for periodicity in the list
  • Advantages: fairly easy to do
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13
Q

What is stratified random sampling?

A
  • Population can be divided into different groups based on criteria (i.e. strata)
  • Separate simple random sample from each population stratum
  • EX: men vs. women who are ASHA members
  • Advantages over simple: assures representation of overall population AND key subgroups, potentially apply results to subgroups
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14
Q

What is cluster sampling?

A
  • Select clusters from population on the basis of simple random sampling, then sample all people in the cluster
  • EX: if you want to sample all pre-k kids in MD, take a random sample of MD schools with pre-k programs, sample all those kids in the sampled schools
  • Economical, but still susceptible to sampling bias (clusters are intrinsically homogeneous)
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15
Q

What is multistage sampling?

A
  • Combine different methods of probability sampling

- EX: using cluster sampling to select certain schools, and then random sampling within each school

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16
Q

What is non-probability sampling?

A
  • Does not involve random selection
  • May or may not represent the population well, hard to know how well (even with large N)
  • Susceptible to researcher bias
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17
Q

What is convenience sampling?

A
  • Convenient samples are chosen from a population (what we do most frequently)
  • EX: college students, local volunteers
  • Disadvantage: no evidence that they are representative of the populations we’re interested in generalizing to (and often would suspect that they are not)
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18
Q

What is purposive sampling?

A
  • Specific, predefined groups that we seek
  • Frequent in qualitative research
  • Smaller N
  • Useful for situations where you need to reach a targeted sample quickly and where sampling for proportionality is not the primary concern
  • EX: expert, extreme/deviant cases, criterion sampling (“all white cars”)
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19
Q

Describe how to determine sample size.

A
  • Determine BEFORE you start an experiment (a priori)
  • Practical concerns
  • Sample size estimates depend on:
  • The size of the effect you’re interested in (effect size)
  • Variability across the sample (i.e. participants)
  • Reliability of your measure
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20
Q

Describe random assignment to groups.

A
  • Ideal way to divide your sample into groups

- Any TRUE experiment with treatment/control or multiple treatments

21
Q

Describe counterbalancing of repeated measures conditions: within-subjects.

A
  • Each person completes each condition 1+ time(s)
  • Reverse counterbalancing: ABCCBA
  • Block counterbalancing: select # of repetitions of each condition, present in a variety of sequences (i.e. ABCBACBCA)
22
Q

Describe counterbalancing of repeated measures conditions: across-subjects.

A
  • Each participant only gets 1 sequence of conditions, but the sequences differ across people
  • Can be complete or partial
23
Q

What is the goal of counterbalancing?

A

-To balance out carry-over or order effects

24
Q

Describe across-subject counterbalancing when complete is not feasible.

A
  • Randomized partial counterbalancing

- Latin-square counterbalancing

25
Q

What is randomized partial counterbalancing?

A

-Each participant gets a different random sequence

26
Q

Describe complete across-subject counterbalancing.

A
  • Need condition! (factorial) groups

- EX: 3 conditions, 3! = 3 X 2 X 1 = need 6 groups

27
Q

What is latin-square counterbalancing?

A

-A square of sequences such that each condition appears only once in any order position in the sequences

28
Q

What is balanced latin-square counterbalancing?

A

-A square of sequences such that each condition appears only once in any order position in the sequences
AND
-Follows each other condition an equal number of times

29
Q

What are the 4 levels of measurement?

A

1) Nominal
2) Ordinal
3) Interval
4) Ratio

30
Q

What is nominal level of measurement?

A
  • Categorical
  • Ideally exhaustive and mutually-exclusive categories
  • EX: type of hearing loss
31
Q

What is ordinal level of measurement?

A
  • Categorical
  • Ordered
  • EX: degree of HL
32
Q

What is interval level of measurement?

A
  • Discrete or Continuous
  • Equal distances between scores
  • Calculate differences but not proportions
  • EX: dB HL, temperature
33
Q

What is ratio level of measurement?

A
  • Discrete or Continuous
  • Interval and has a true zero point
  • EX: WR score
34
Q

How can you report descriptive statistics?

A
  • Frequency, percentage, proportion
  • Measures of central tendency
  • Measures of variability
35
Q

What are measures of central tendency?

A
  • Mean: M, interval/ratio data
  • Median: Mdn, ordinal/interval/ratio data
  • Mode: all types
36
Q

What are measures of variability?

A
  • Min, max
  • Range
  • Interquartile range
  • Standard deviation
  • Standard error of the mean
37
Q

What is interquartile range?

A
  • Score at 75th percentile - score at 25th percentile

- Relevant if your data have extreme highs or lows

38
Q

What is standard deviation?

A
  • Dispersion of scores around the mean
  • Colloquially: On average, how much do observations differ from the mean?
  • SD = SQRT(variance)
39
Q

What is standard error of the mean?

A
  • How far is the sample mean likely to be from the population mean?
  • SE = s/ (SQRT[n])
40
Q

What is it important to know shapes of distributions?

A
  • To determine the best way to summarize your data
  • To determine the type of statistical test you should perform (some tests assume a particular distribution [i.e. “normally distributed”)
41
Q

Describe normal distribution.

A
  • AKA “Gaussian curve”
  • Largest number of observations at the center
  • Symmetric
  • Fewer as you get towards extreme values (2/3 of observations will fall within 1 SD of the mean)
42
Q

Describe skewed distributions.

A
  • Not symmetric
  • More extreme scores in one directions (i.e. negatively skewed, positively skewed)
  • Mean most affected by skew (the more skewed the more difference between mean and median)
43
Q

Describe bimodal distribution.

A

-2 peaks

44
Q

What are standardized scores?

A
  • Account for both average and variability of the score
  • Z score = (score-M)SD
  • Resulting M(z-score) = 0 and SD(z-score) = 1
  • How many SD above/below the mean is a given score?
  • Straightforward way to relate a value to a normal distribution and to other z-scores
45
Q

What are outliers?

A
  • Extremely deviant values
  • Not necessarily inaccurate
  • Can be identified via: reviewing experiment notes, plotting raw data, setting a priori criteria
46
Q

How do you deal with outliers?

A
  • NEVER EVER remove data without minimally describing:
    a. How and why you did
    b. What was the impact on the results
    c. How much data you’ve removed and was removal equally distributed across condition
  • Problems can arise from interpreting data with real outliers or without “outliers”
47
Q

What is the rule of thumb regarding data representation in tables?

A

-Data that required less than 2 columns or rows should just be presented in the text

48
Q

What are different types of figures?

A
  • Pie chart
  • Scatterplot
  • Column/bar graph
  • Line graph

HESP724 Exam 2 (3 decks)

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