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BEHP5018 > 5018 Unit 8 > Flashcards

5018 Unit 8 Flashcards

(76 cards)

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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
Two types of probability
Subjective | Objective
26
How to express probability
Always positive | Can be in the form of fractions, decimals or percentages
27
Each individual in the population has an equal chance of being selected; there must be constant probability for each and every selection
Random sampling
28
The most common occurring shape for population distribution
Normal shaped distributions
29
Provide incomplete pictures of the population
Samples
30
The discrepancy, or amount of error between a sample statistic and its corresponding population parameter
Sampling error
31
Distribution of statistics obtained by selecting all possible samples of a specific size of population
Sampling distribution
32
For any population, the distribution of sample means will approach a normal distribution as n approaches infinity
Central Limit Theorem
33
The shape of distribution of sample means will be almost perfectly normal if one of the following conditions is satisfied
1. Population from which sample is selected is normal | 2. The number of scores (n) in each sample is relatively larger (n>30)
34
The larger the sample size, the more probable that the sample mean will be close to the population mean
The Law of Large Numbers
35
Statistical method that uses sample data (statistics) to evaluate a hypothesis (question) about a population parameter
Hypothesis Testing
36
Basic common inferential procedure of hypothesis testing
z scores, probability, and the distribution of sample means
37
Purpose of hypothesis testing
Help researchers differentiate between real patterns I data and random patterns in data
38
Hypothesis testing begins with...
known parameters
39
The goal of hypothesis testing
determine what happens to the population after the tx is administered
40
Assumptions for hypothesis tests with z-score
Random sampling Independent observations Value of SD is unchanged by the tx Normal sampling distribution
41
4 Main Steps of Hypothesis Testing
State hypothesis Set criteria Collect data Make decision
42
Predicts that IV (tx) will have no effect on the DV
Null hypothesis
43
Predicts that IV(tx) will have an effect on the DV
Alternative hypothesis
44
The probability value that is used to define the very unlikely sample outcomes if the null hypothesis is true
Alpha level (level of significance)
45
Extreme sample values that are very unlikely to be obtained if the null hypothesis is true
Critical region
46
Purpose of statistic
Determine whether the result of research study (the obtained difference) is more than what would be expected by chance alone
47
Types of Hypothesis Testing Errors
Type I Error | Type II Error
48
Reject null hypothesis when it is actually true | False reports in scientific literature
Type I Error
49
Failing to reject the null hypothesis when it is actually false
Type II Error
50
A test used to compare two means | Alternative to Z scores
T-test
51
Occurrence of first event has no effect on the probability of the second event
Independent observations
52
Advantages of related-samples design (AKA within-subject designs)
1. Eliminate the problem of individual differences between subjects 2. Greatly reduces sample variance
53
2 Types of Contaminating Factors
Carryover effects | progressive error
54
Subject's response in 2nd tx is altered by lingering aftereffects from the 1st tx
Carryover effects
55
Subject's performance changes consistently over time
Progressive error
56
2 ways to deal with contaminating factors
1. Counterbalance the order of tx presentation | 2. Use different experimental design f contamination is expected
57
Assumptions of Related-Samples t-Test
1. Observation within each tx condition must be independent | 2. Population distribution of difference scores (D values) must be normal
58
Analysis of Variance (ANOVA)
Tell whether or not there is a significant difference between 3 or more groups
59
Follow up that would tell you where the difference is located
Multiple Comparison Procedure (MCP)
60
Statistical technique used to measure and describe relationship between two variables
Correlation
61
What does correlation measure?
Direction Form Degree
62
Two types of direction
Positive Correlation | Negative Correlation
63
Positive Correlation
X and Y change together moving in the same direction
64
Negative Correlation
X and Y change inversely
65
Describes linear relationship between 2 or more variables
Regression
66
Ways to distort correlation
Restricted range | Outliers
67
Measure of the strength of a phenomenon
Effect size
68
Benefits of Effect Size
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
2 Types of Statistics
Descriptive Statistics | Inferential Statistics
70
Goal of descriptive statistics
Describe properties of the samples you are working with
71
Measures used in descriptive statistics
Central tendency Variability Effect size
72
Reasons for using descriptive statistics
Complement visual analysis We already use them Program evaluation My open doors for funding
73
Reason for not using descriptive statistics
May hide trends
74
Goal of inferential statistics
Use sample data as the basis for answering questions about the population
75
Reasons for using inferential statistics in ABA
Appropriate for certain types of research May open doors for funding Perceived weakness of reliance on visual analysis in ABA
76
Reasons for not using inferential statistics in ABA
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 (9 decks)

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