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42408325 Inglês 53 flashcards

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  1. 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
  2. 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
  3. 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
  4. 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
  5. 5Levels of statistical analysis1. Descriptive Statistics
    2. Inferential Statistics
    3. Associational Statistics
    4. Difference
  6. 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.)
  7. 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
  8. 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
  9. 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
  10. 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
  11. 11Examples of determine differencesT-tests, ANOVA, MANOVA
  12. 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
  13. 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
  14. 14What can frequencies be represented by?Graphs, pie charts, polygons
  15. 153 types of frequency distributions1. Normal distribution
    2. Non symmetrical
    3. Kurtosis
  16. 16Define: Normal DistributionBoth halves of distribution are identical
  17. 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
  18. 18Define: KurtosisDistribution is characterised by its shape; flatness is called Platykurtic and peakedness is called Leptokurtic
  19. 19Define: Measures of Central TendencyProvides information about the most typical or representative scores in a group (largest area represented under the standard Bell curve)
  20. 20Three measures of central tendency1. Mean
    2. Mode.
    3. Median
  21. 21Define: ModeValue that occurs most frequently
  22. 22Define: MedianThe point on a scale above or below which 50% of the cases fall; it lies at the midpoint of the distribution
  23. 23Define: MeanMost fundamental concept in statistical analysis used with continuous data
    M= X1 + X2 + X3/N
    Average
  24. 24Define: Measures of VariabilityGroup of scale scores differ from one another or have what us termed 'variability' (spread or dispersion)
  25. 255 types of variability1. Range
    2. Interquartile Range
    3. Sum of Squares
    4. Standard Deviation
    5. Variance
  26. 26RangeRefers to the difference from the highest to the lowest value in a collection of data
  27. 27IQRRefers to the range of the middle 50% of participants
    Describes the middle of the sample or the range of a majority of participants
  28. 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
  29. 29VarianceThe mean or average of the SS
  30. 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
  31. 31Define: Bivariate StatisticsInvolves looking for relationships among two or more variables
  32. 32Two types of Bivariate statistics1. Contingency Tables
    2. Correlational analysis
  33. 33Contingency Tables2D frequency distribution that is used with categorical (nominal) data
    Attributes of one variable are related to the attributes of another variable
  34. 34Correlational AnalysisDetermines relationships amongst variables
    Examines the extent to which 2 variables are related to each other across a group of participants
  35. 35Correlational StatisticAn index calculated that describes the direction and magnitude of a relationship
  36. 36Three types of directional relationships1. Positive correlation
    2. Negative correlation
    3. Zero correlation (no correlation)
  37. 372 correlational statistics used1. Pearson product-moment correlation
    2. Intra-Class Correlation Coefficient (ICC)
  38. 38Pearson product-moment correlationAlso known as the Pearson r
    Used with interval level data
    Spearman rho - used with ordinal data
  39. 39ConstructsAbstractions that cannot be observed directly but are helpful when trying to explain behaviour
  40. 40Examples of constructsIntelligence
    Attitudes towards persons with disabilities
    Self concept
    Play skills
  41. 41Data terminology: Operational DefinitionThe ways by which constructs are observed and measured
  42. 42Examples of operational definitionsWeschler IQ test
    Virgillo Teacher Effectiveness Inventory
    Tennesse Self-Concept Scale
  43. 43Data Terminology VariableA construct that has been operationalised and has two or more values
  44. 444 Measurement ScalesNominal - categories
    Ordinal - Ordered Categories
    Interval - Equal Intervals
    Ratio - absolute zero
  45. 45Examples of the 4 measurement scalesNominal - Gender, ethnicity
    Ordinal - Rank in class, order of finish
    Interval - Temperature, height
    Ratio - time, income
  46. 46Two types of variablesCategorical Variable
    Quantitative Variable
  47. 47Types of variables: Categorical VariableReflect nominal scales and measure the presence of different qualities
    - Gender, ethnicity etc.
  48. 48Types of Variables" Quantitative Variablereflect ordinal, interval or ratio scales and measure different quantities of a variable
    - Test scores, self-esteem scores etc.
  49. 49Two major concerns related to test selectionReliability
    Validity
  50. 507 Types of ValidityContent/Face
    Criterion-related - predictive & concurrent
    Construct
    Discriminant
    Consequential
    Internal
    External
  51. 516 Types of ReliabilityStability
    Equivalence
    Equivalence and Stability combined
    Internal consistency
    Scorer/rater
    Standard Error of Measurement
  52. 524 techniques of selecting random sampleRandom
    Stratified random
    Cluster
    Systematic
  53. 533 Techniques for selecting Non-Random SamplesConvenience Sampling
    Purposive Sampling
    Quota Sampling

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FlashCards sobre Sem título, criado por 42408325 em 10-09-2014.
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