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BEHP5018 COPY COPY > 5018 Unit 8 > Flashcards

5018 Unit 8 Flashcards

1
Q

Top-down approach, large to small

Theory-hypothesis-test hypothesis-specific answer

A

Deductive Research Paradigm

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2
Q

Bottom-up, small to large

data-analysis-generalize

A

Inductive Research Paradigm

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3
Q

Required in deductive approach to interpret data

A

Statistics; quantitative data

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4
Q

Required in inductive research paradigm

A

Qualitative approach

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5
Q

Four types of data

A

Nominal
Ordinal
Interval
Ratio

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6
Q

Type of data that refers to categories

A

Nominal

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7
Q

Type of data that refers to order

A

Ordinal

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8
Q

Type of data where difference between each value is even

A

Interval

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9
Q

Type of data where difference between each value is even and has a true zero

A

Ratio

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10
Q

Three measures of central tendency

A

Mean
Median
Mode

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11
Q

Sum of scores divided by number of scores; most preferred measure of central tendency

A

Mean

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12
Q

Score that divides distribution exactly in half; gives two groups of equal sizes

A

Median

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13
Q

Score that has the greatest frequency

A

Mode

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14
Q

Two types of Mode

A

Bimodal

Multimodal

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15
Q

Two modes or peaks

A

Bimodal

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16
Q

More than two modes

A

Multimodal

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17
Q

Used for nominal scales, discrete variables, or describing shape

A

Mode

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18
Q

Used for extreme scores, skewed distribution, undetermined values, and open-ended distributions

A

Median

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19
Q

Three measures of variability

A

Range
Interquartile range
Standard Deviation

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20
Q

Describes the distribution in terms of distance from the mean or between two scores; how spread out or clustered together scores are in a distribution

A

Variability

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21
Q

Distance between targets score and smallest score + 1

A

Range

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22
Q

Criticisms of Range

A

Crude and unreliable measure of variability

Does not consider all scores in the distribution

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23
Q

Most important measure of variability that measure typical distance from mean and uses all scores in the distribution

A

Standard deviation

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24
Q

A tool in inferential statistics that measure the likelihood of an event

A

Probability

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25
Q

Two types of probability

A

Subjective

Objective

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26
Q

How to express probability

A

Always positive

Can be in the form of fractions, decimals or percentages

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27
Q

Each individual in the population has an equal chance of being selected; there must be constant probability for each and every selection

A

Random sampling

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28
Q

The most common occurring shape for population distribution

A

Normal shaped distributions

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29
Q

Provide incomplete pictures of the population

A

Samples

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30
Q

The discrepancy, or amount of error between a sample statistic and its corresponding population parameter

A

Sampling error

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31
Q

Distribution of statistics obtained by selecting all possible samples of a specific size of population

A

Sampling distribution

32
Q

For any population, the distribution of sample means will approach a normal distribution as n approaches infinity

A

Central Limit Theorem

33
Q

The shape of distribution of sample means will be almost perfectly normal if one of the following conditions is satisfied

A
  1. Population from which sample is selected is normal

2. The number of scores (n) in each sample is relatively larger (n>30)

34
Q

The larger the sample size, the more probable that the sample mean will be close to the population mean

A

The Law of Large Numbers

35
Q

Statistical method that uses sample data (statistics) to evaluate a hypothesis (question) about a population parameter

A

Hypothesis Testing

36
Q

Basic common inferential procedure of hypothesis testing

A

z scores, probability, and the distribution of sample means

37
Q

Purpose of hypothesis testing

A

Help researchers differentiate between real patterns I data and random patterns in data

38
Q

Hypothesis testing begins with…

A

known parameters

39
Q

The goal of hypothesis testing

A

determine what happens to the population after the tx is administered

40
Q

Assumptions for hypothesis tests with z-score

A

Random sampling
Independent observations
Value of SD is unchanged by the tx
Normal sampling distribution

41
Q

4 Main Steps of Hypothesis Testing

A

State hypothesis
Set criteria
Collect data
Make decision

42
Q

Predicts that IV (tx) will have no effect on the DV

A

Null hypothesis

43
Q

Predicts that IV(tx) will have an effect on the DV

A

Alternative hypothesis

44
Q

The probability value that is used to define the very unlikely sample outcomes if the null hypothesis is true

A

Alpha level (level of significance)

45
Q

Extreme sample values that are very unlikely to be obtained if the null hypothesis is true

A

Critical region

46
Q

Purpose of statistic

A

Determine whether the result of research study (the obtained difference) is more than what would be expected by chance alone

47
Q

Types of Hypothesis Testing Errors

A

Type I Error

Type II Error

48
Q

Reject null hypothesis when it is actually true

False reports in scientific literature

A

Type I Error

49
Q

Failing to reject the null hypothesis when it is actually false

A

Type II Error

50
Q

A test used to compare two means

Alternative to Z scores

A

T-test

51
Q

Occurrence of first event has no effect on the probability of the second event

A

Independent observations

52
Q

Advantages of related-samples design (AKA within-subject designs)

A
  1. Eliminate the problem of individual differences between subjects
  2. Greatly reduces sample variance
53
Q

2 Types of Contaminating Factors

A

Carryover effects

progressive error

54
Q

Subject’s response in 2nd tx is altered by lingering aftereffects from the 1st tx

A

Carryover effects

55
Q

Subject’s performance changes consistently over time

A

Progressive error

56
Q

2 ways to deal with contaminating factors

A
  1. Counterbalance the order of tx presentation

2. Use different experimental design f contamination is expected

57
Q

Assumptions of Related-Samples t-Test

A
  1. Observation within each tx condition must be independent

2. Population distribution of difference scores (D values) must be normal

58
Q

Analysis of Variance (ANOVA)

A

Tell whether or not there is a significant difference between 3 or more groups

59
Q

Follow up that would tell you where the difference is located

A

Multiple Comparison Procedure (MCP)

60
Q

Statistical technique used to measure and describe relationship between two variables

A

Correlation

61
Q

What does correlation measure?

A

Direction
Form
Degree

62
Q

Two types of direction

A

Positive Correlation

Negative Correlation

63
Q

Positive Correlation

A

X and Y change together moving in the same direction

64
Q

Negative Correlation

A

X and Y change inversely

65
Q

Describes linear relationship between 2 or more variables

A

Regression

66
Q

Ways to distort correlation

A

Restricted range

Outliers

67
Q

Measure of the strength of a phenomenon

A

Effect size

68
Q

Benefits of Effect Size

A
  1. Relatively easy to calculate and interpret for group data
  2. Can be used to summarize data from many studies with different DV
  3. Not dependent on sample size
69
Q

2 Types of Statistics

A

Descriptive Statistics

Inferential Statistics

70
Q

Goal of descriptive statistics

A

Describe properties of the samples you are working with

71
Q

Measures used in descriptive statistics

A

Central tendency
Variability
Effect size

72
Q

Reasons for using descriptive statistics

A

Complement visual analysis
We already use them
Program evaluation
My open doors for funding

73
Q

Reason for not using descriptive statistics

A

May hide trends

74
Q

Goal of inferential statistics

A

Use sample data as the basis for answering questions about the population

75
Q

Reasons for using inferential statistics in ABA

A

Appropriate for certain types of research
May open doors for funding
Perceived weakness of reliance on visual analysis in ABA

76
Q

Reasons for not using inferential statistics in ABA

A
  1. Don’t tell how likely results are replicated
  2. Don’t tell the probability of results were due to chance
  3. The probability is conditional
  4. Best way to increase chances of significance is to increase n of participants
  5. Large number of variables that will have very small effects become important
  6. Limits the reason for doing experiments
  7. Reduce scientific responsibility
  8. Emphasize population parameters at the expense of behavior
  9. Bx is something an individual does, not what a group average does.
  10. We should be attending to social significance
  11. Durability of changes
  12. Number and characteristics of participants that improve in a socially significant manner

BEHP5018 COPY COPY (9 decks)

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