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- 1Relationships of Statistics to Clinical Practice1. A hypothesis is generated
2. Research plan is devised to study effects of treatment method used by clinicians
3. Quantifiable data are collected
4. Data are statistically analysed
5. Results are interpreted
6. Conclusions are drawn & information is shared with scientific, research and clinical community - 2Hypothesis Testing Define 'Null Hypothesis'A statement of no difference between or among groups of participants
EXAMPLE: Children with autism have the same communication strategies as children with Down's Syndrome - 3Hypothesis Testing: Define 'Alternate Hypothesis'A statement that there is a definite difference between or among groups of subjects
EXAMPLE: Children with Autism have different communication strategies as children with Down's Syndrome - 4What is statistical analysis?Concerned with the organisation and interpretation of data according to well-defined, systematic & mathematical procedures and rules
Data are represented by numbers - 5Levels of statistical analysis1. Descriptive Statistics
2. Inferential Statistics
3. Associational Statistics
4. Difference - 6Descriptive StatisticsDATA REDUCTION
Used to reduce large sets of observations into more compact and interpretable forms of data
Used to reduce, summarise & describe the data derived from a sample (or subgroup from a population)
Involves counting occurrences, proportions, percentages or distributions of phenomena (self esteem, intelligence etc.) - 7Inferential StatisticsINFERENCE TO KNOWN POPULATION
Used to draw conclusions about population (larger group) parameters based on findings from a sample (smaller subgroup)
Concerned with tests of significance to generalise findings to the population from which the sample is drawn
Need to have recruited a representative sample/ portion in order to be able to infer information representative of the whole population - 8Associational StatisticsCAUSALITY
refers to a set of procedures designed to identify relationships between multiple variables and to determine whether knowledge of one set of data allows the researcher to infer or predict the characteristics of another set
Primary purpose is to make casual statements and predictors - 9Associational Statistics ExampleStudy involving a group of smokers might allow prediction or generalisation to be made that a certain percentage of smokers will develop lung cancer
- 10Determining DifferenceDetermining differences between two sets of data
Statistics are often used to compare two different groups of participants or data collected from the same participants on two different occasions to see if significant difference occurs - 11Examples of determine differencesT-tests, ANOVA, MANOVA
- 128 types of descriptive statistics1. Measures of Central Tendency - Mode, Median, Mean
2. Measures of Variability - Range, IQR, Sum of Squares, Variance, Standard deviation
3. Bivariate descriptive statistics - contingency tables & correlational analysis
4. Tables
5. Bar graphs
6. Histograms
7. Scatter diagrams
8. Statistical Pies/Pie Graphs - 13What is frequency distribution?Reflects a simple count or tally of how frequently each value of a variable occurs in a set of measured objects
Arranged in a table format with values of variable arranged from lowest to highest - 14What can frequencies be represented by?Graphs, pie charts, polygons
- 153 types of frequency distributions1. Normal distribution
2. Non symmetrical
3. Kurtosis - 16Define: Normal DistributionBoth halves of distribution are identical
- 17Define: Non SymmetricalEither positively or negatively skewed; positive skew has curve that is high on the left and a tall to the right; opposite for negative skew
- 18Define: KurtosisDistribution is characterised by its shape; flatness is called Platykurtic and peakedness is called Leptokurtic
- 19Define: Measures of Central TendencyProvides information about the most typical or representative scores in a group (largest area represented under the standard Bell curve)
- 20Three measures of central tendency1. Mean
2. Mode.
3. Median - 21Define: ModeValue that occurs most frequently
- 22Define: MedianThe point on a scale above or below which 50% of the cases fall; it lies at the midpoint of the distribution
- 23Define: MeanMost fundamental concept in statistical analysis used with continuous data
M= X1 + X2 + X3/N
Average - 24Define: Measures of VariabilityGroup of scale scores differ from one another or have what us termed 'variability' (spread or dispersion)
- 255 types of variability1. Range
2. Interquartile Range
3. Sum of Squares
4. Standard Deviation
5. Variance - 26RangeRefers to the difference from the highest to the lowest value in a collection of data
- 27IQRRefers to the range of the middle 50% of participants
Describes the middle of the sample or the range of a majority of participants - 28Sum of SquaresCalculated by squaring the difference each score and the mean. These squared scores are then summed and referred to as the sum of squares. The larger the SS the greater the variance
- 29VarianceThe mean or average of the SS
- 30Standard DeviationMost widely used dispersion measure
It is an indicator of the average deviation of scores around the mean or simply put the square root of the variance - 31Define: Bivariate StatisticsInvolves looking for relationships among two or more variables
- 32Two types of Bivariate statistics1. Contingency Tables
2. Correlational analysis - 33Contingency Tables2D frequency distribution that is used with categorical (nominal) data
Attributes of one variable are related to the attributes of another variable - 34Correlational AnalysisDetermines relationships amongst variables
Examines the extent to which 2 variables are related to each other across a group of participants - 35Correlational StatisticAn index calculated that describes the direction and magnitude of a relationship
- 36Three types of directional relationships1. Positive correlation
2. Negative correlation
3. Zero correlation (no correlation) - 372 correlational statistics used1. Pearson product-moment correlation
2. Intra-Class Correlation Coefficient (ICC) - 38Pearson product-moment correlationAlso known as the Pearson r
Used with interval level data
Spearman rho - used with ordinal data - 39ConstructsAbstractions that cannot be observed directly but are helpful when trying to explain behaviour
- 40Examples of constructsIntelligence
Attitudes towards persons with disabilities
Self concept
Play skills - 41Data terminology: Operational DefinitionThe ways by which constructs are observed and measured
- 42Examples of operational definitionsWeschler IQ test
Virgillo Teacher Effectiveness Inventory
Tennesse Self-Concept Scale - 43Data Terminology VariableA construct that has been operationalised and has two or more values
- 444 Measurement ScalesNominal - categories
Ordinal - Ordered Categories
Interval - Equal Intervals
Ratio - absolute zero - 45Examples of the 4 measurement scalesNominal - Gender, ethnicity
Ordinal - Rank in class, order of finish
Interval - Temperature, height
Ratio - time, income - 46Two types of variablesCategorical Variable
Quantitative Variable - 47Types of variables: Categorical VariableReflect nominal scales and measure the presence of different qualities
- Gender, ethnicity etc. - 48Types of Variables" Quantitative Variablereflect ordinal, interval or ratio scales and measure different quantities of a variable
- Test scores, self-esteem scores etc. - 49Two major concerns related to test selectionReliability
Validity - 507 Types of ValidityContent/Face
Criterion-related - predictive & concurrent
Construct
Discriminant
Consequential
Internal
External - 516 Types of ReliabilityStability
Equivalence
Equivalence and Stability combined
Internal consistency
Scorer/rater
Standard Error of Measurement - 524 techniques of selecting random sampleRandom
Stratified random
Cluster
Systematic - 533 Techniques for selecting Non-Random SamplesConvenience Sampling
Purposive Sampling
Quota Sampling
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- Fichas sobre Sin título, creado por 42408325 el 10/09/2014.
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