Flashcards in 8- Statistics and hypothesis testing in ABA Deck (102)
You work Top : Large to small-
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)
Work from Bottom Up, small to large:
Analysis - (Results: come to conclusions that you can generalize to other people about. )
Fluid, qualitative approach
Examples of qualitative research:
observation of cultures
Inductive approach – research
Research in ABA is typically ........in that we do not test hypotheses
but we are also quantitative
Reversal designs are :
-flexible (ABA vs. ABAC)
- without a pre-determined outcome
Why the differences? Not withstanding the differences can we use the tools?
- 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
-Complements visual analysis
Already use them to describe:
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
May hide Trends in behavior
Descriptive statistics in ABA: reasons for not using
• 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
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.
Reasons for using Inferential statistics - ABA
• 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
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
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
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
More than one because many different types of Distributions are possible.
Three measures of central tendency
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)
The score that divides the Distribution exactly in half
A ____ Splits gives researchers two groups of equal sizes..
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
there are Extreme scores/skewed distribution’s
Open ended distribution’s
Median: When to use
Is the score or category that has the greatest flexibility ( Peak)
A distribution can have more than one mode,
Easy to find in basic frequency distribution tables
NOT A frequency. It’s a score or category
Can be equal or major/minor
More than two modes
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
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.
3. standard deviation - Most important
Three measures of variability
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
1, 4, 5, 8, 9, 10
10 - 1 + 1 = 10
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
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
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
To make ”inferences” about Populations based on sample data
We are Sampling the population with a certain Probability
Inferential statistics – Role
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
Based on mathematical concepts and theory
Objective probability – inferential statistics