8- Statistics and hypothesis testing in ABA Flashcards Preview

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Flashcards in 8- Statistics and hypothesis testing in ABA Deck (102)
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1

You work Top : Large to small-
Theory
Hypothesis
Test hypothesis
Specific answer

Requires statistics to interpret large amounts of data (Quantitative/hard number)

majority of Social science researchers have a ____ orientation

Deductive Research Paradigm
(AKA deductive approach)

2

Work from Bottom Up, small to large:

Generalize
Analysis - (Results: come to conclusions that you can generalize to other people about. )
Data

Fluid, qualitative approach

Examples of qualitative research:
interviews
observation of cultures
Focus groups



.

Inductive approach – research

3

Research in ABA is typically ........in that we do not test hypotheses

but we are also quantitative

Reversal designs are :
-flexible (ABA vs. ABAC)
-quantitative,
- without a pre-determined outcome
Why the differences? Not withstanding the differences can we use the tools?

inductive

4

•Goal:
- To “Describe” Properties of the sample(s) you’re working with
- can talk about the central tendency of the sample or population in terms of what the most typical score in your sample or population look like.
- can talk about the variability Around the measure of typicalness be it mean median or mode. What is the variability around that measure of central tendency
- and talk about Effect size

”Descriptive” statistics

5

-Complements visual analysis

Already use them to describe:
•level change
• IOA
Can use in Program evaluation By aggregating data across clients

May open doors for Funding.
Ex. Effect size (Can be compared to other effect sizes)

Descriptive Statistics in ABA- Reasons for using

6

May hide Trends in behavior

Descriptive statistics in ABA: reasons for not using

7

Goal:
• To Use a sample data as a basis for Answering questions about the Population. (Can’t access whole populations. Instead we collect samples.)
• Since we rely on samples, we must to better understand how they relate to populations.
••Then we use HYPOTHESIS testing to make those inferences : T-tests, ANOVA etc

(The inferences about the samples are about the population from which the sample was drawn.)

(And the inferences are about relationships or features Of the population.)





Inferential statistics- Goal

8

Appropriate for certain types of research-
ex. When ABA does not use single case design such as contingency management – group

May open doors to funding
• hypothesis testing

Perceived weakness of reliance on Visual analysis in ABA.
Inconsistent?

Reasons for using Inferential statistics - ABA

9

• Do not tell us how likely the results are to be replicated.
- in ABA We use an ABA design or Multiple BASELINE design.
- INFERENTIAL statistics, we’re not Operating under circumstances that allow us to REPLICATE effect.

Do not tell us the probability that the results were due to Chance

Tells us The Probability is a CONDITIONAL probability event under true null hypothesis

• Very few situations in which there is only randomness in data.

•Best way to increase your chances of significance is increasing number of participants.

•A large number of variables that will have very small effects become important.

•Limits the reasons for doing experiments.

•Reduce scientific responsibility.

• Emphasizes population parameters at the expense of behavior.

“Behavior is something an individual does not what a group average does.”

•We should be attending to:
- value/social significance,
-durability of changes
- Number and characteristics of participants that improve in a socially significant manner.

Inferential - Some reasons for not using it in ABA

10

Looked at behavioral treatment and normal educational and intellectual functioning and young autistic children (Journal of consulting and clinical psychology, 1987)

Hypothesis: the construction of a special, Intense, and comprehensive learning environment for very young children with autism would allow them to catch up with their normal peer is by first grade.

Subjects were young children diagnosed with autism.

• Group one : 19 subjects – 40 hours a week of ABA
•Group twi: 19 subjects- 10 hours a week of ABA
• Group 3:21 subjects – other treatments

Groups of one and two received two or more years of therapy




Lovaas

11

Statistical analysis (MANOVA) used to compare the DV (IQ) To show that the intensive group demonstrated a large increase relative to the other conditions

He was a behavior analyst. Why hypothesis testing, statistics, and IQ as a dependent variable?-

-Intensive, long-term study that used measures and analysis that others NOT in our field would pay attention to.

-Control groups allowed for strong conclusions









Inferential Statistics

Lovaas Study

12

1. Nominal (name) refers to categories
Ex. School districts and colors

2. Ordinal (order), Quantities that have an order
Ex. Physical fitness and pain scale
(Not a lot you can do with these two types of data)

3. Interval - difference between each value is Even
Ex. Degrees Fahrenheit

4. Ratio: when the difference between each value is even, has a true Zero
Ex. Time, weight, temperature in kelvin

Practically, interval and ratio are types of data we are interested in

data used in statistics 4x

13

1. Mean

2. Median

3. Mode

More than one because many different types of Distributions are possible.

Three measures of central tendency

Descriptive statistics

14

The sum of the score is divided by the number of scores

Advantage: every number in the distribution is used in its calculation

However changing a single score or adding a new score will change it, except when the new score equals it

Most preferred measure
-Every score used it it’s calculation
- used to calculate other statistics

However Some situations in which mean cannot be calculated or is not most Representative measure.

Remember, the goal is to find a single value that best represents the entire distribution (median and mode)

Mean

15

The score that divides the Distribution exactly in half

A ____ Splits gives researchers two groups of equal sizes..
-Low Scores
-High Scores

Median

16

1. Collect all Odd number of scores
-List from Lowest to Highest
- It’s the Middle score

Ex., (10, 11, 12, 13, 14. )____. = 12

Even number of scores:
-List from lowest to highest
- Add the middle 2 scores and divide by two

Example, 2, 3, 5, 8, 10, 12, = 5+8/2 = 6.5

Calculate Median

17

Use when:
there are Extreme scores/skewed distribution’s

Undetermined Values

Open ended distribution’s

Median: When to use

18

Is the score or category that has the greatest flexibility ( Peak)

A distribution can have more than one mode,
• bimodal
•multimodal

Easy to find in basic frequency distribution tables

NOT A frequency. It’s a score or category



Mode

19

Two modes/peaks;
Can be equal or major/minor

BiModal

20

More than two modes

Multimodal

21

Use when It can be used in place of or in conjunction with other measures of central tendency. That is, when there are:
1. NOMINAL Scales; (only measure of central tendency for nominal Scales),
Ex. Are you male or female. 40 are male, 60 female. Can’t calculate the mean or median but can say the most TYPICAL participant is a female because thats 60% of the sample.

2. Use when there are: Discrete Variable: “What is most typical” score; remember the goal of measures of central tendency
Ex. to know the number of golf clubs – calculate the mean.. Most typical score

3. Describing shape: easy to figure out

Mode

22

Describes the distribution in terms of Distance;
How far is that person from the central tendency whether mean, median, or mode

Distance between one score and another or,

Distance between one score and the mean

Describes how well each score or a group of scores describes the entire distribution.

Provides A quantitative measure of the degree to which scores in a distribution are spread out or clustered together.


Variability

23

1. Range

2 interquartile

3. standard deviation - Most important

Three measures of variability

24

The distance between the Largest score and the “Smallest” score plus 1

A crude, unreliable measure of variability because:
-Does not consider ALL the scores in the distribution

Calculate:
Ex. 1
1, 4, 5, 8, 9, 10

10 - 1 + 1 = 10

Ex. 2:
10, 15, 20, 25, 30, 35, 40

40- 10+ 1 = 31

Take Highest and lowest, ignore the others in the range. Not detailed variability.

Range – variability Measure

25

Most important measure of variability

Measures the “Typical” DISTANCE from the MEAN and uses ALL Of the scores in the distribution

How far is Score from the mean.

Using an ABA:
- can be used to identify variability in behavioral data (Autocorrelation can be used for this too).
- Can be described to identify important variability in IOA Data.
Mean and range tell us nothing about which set of circumstances we have which is why we should always report standard deviation over IOA scores along with mean.

Standard deviation – variability measure

26

The relationship between samples of populations

Cannot talk about the Exact Relationship between samples of populations…
But we can talk about Potential outcomes (I.e. Probability)



Probability - inferential statistics

27

To make ”inferences” about Populations based on sample data

We are Sampling the population with a certain Probability

Two kinds:
1. Subjective
2. Objective

Inferential statistics – Role

28

Based on experience or intuition
-Chance of rain, likelihood of recession, chance of getting married in the next year, likelihood of Miami Heat winning another championship

Subjective probability

29

Based on mathematical concepts and theory

Objective probability – inferential statistics

30

P(event) =. # of outcomes classified as the event divided by/ total number of Possible outcomes

The probability of event A, p(A), Is the ratio of the number of outcomes that include event A to the total number of possible outcomes

Example What is the probability that a selected Person has a birthday in October, assume 365 days in a year?
Step 1: how many chances are there to have a birthday in a year?

Step 2: how many chances are there to have a birthday in October?

Step 3: the probability that a randomly selected person has a birthday in October is:
P (October birthday) = 31/365 = 0.0849

Probability formula