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Flashcards

BEM 314 chapter 19

chelseaklokow English 39 cards

Cards in this deck (39)

  1. 1The Analytical Framework:- The analysis and presentation of that data analysis
    - Fourth and final framework
    - In the data analysis chapter in the thesis
    - Or the analysis section in the report of the research
  2. 2"Variable" - In quantitative data analysis- Unit of measure in quantitative data analysis
    - A characteristic with more than one value
    - Eg. Gender - Two values
  3. 3Different Level Variables1. Nominal
    2. Ordinal
    3. Interval
  4. 41. Nominal Variables- Lowest level of measurement
    - No assumptions can be made about relations between values
    - Each value is a distinct category and serves simply as a label
    - Categories cannot be ranked
    - Examples: Gender, race
    - Yes/no scales
  5. 52. Ordinal Variables- Intermediate level of measurement
    - Values are ranked according to some criteria
    - There is distance between the different values in the scale
    - Distance cannot be calculated or quantified
    - Example: level of education
    - Simplest ordinal level scale = ranking
  6. 63. Interval- highest level of measurement
    - Data with a meaningful and measurable distance between the values
    - Variables are distributed in an even, continuous manner
    - Examples: age, income
  7. 7"Dichotomous Variable"One that has only 2 values
    Eg. Gender
    Yes/no = dichotomous response
  8. 8"Independent Variable"- The cause
    - when examining the relationship between two variables, the assumed cause is the independent variable
  9. 9"Intervening Variable"The means by which the independent variable affects the dependent variable
  10. 10"Descriptive Statistics"Used to describe each variable in the data gathered
    Eg. Summarizing statistics
    Presented as: percentages, ranges, ratios, averages, SD
  11. 11"Descriptive Analysis"The elementary transformation of raw data in a way that describes the basic characteristics - central tendency, distribution, variability
  12. 12"Histogram"A graphical way of showing a frquency distribution in which the height of a bar corresponds to the observed frequency of the category.
  13. 13Types of descriptive statistics:1. MEASURES OF CENTRAL TENDENCY:
    - Mean
    - Mode
    - Median
    2. MEASURES OF DISPERSION:
    - Range
    - IQR
    - SD
    3. OTHER MEASURES:
    - Percentages
    - Ratios
    - Proportions
    - Frquency distribution
  14. 14"Mean"- The arithmetic average
    - Calculate: Sum all values and divide by the no. of values
  15. 15"Mode"Most commonly occuring value in the range of values
  16. 16"Median"Middle value of a range of values
  17. 17"Range"The maximum and minimum values in a range of data
  18. 18"Interquartile Range"- A robust measure of sample distribution
    - Eliminates outliers by focusing on the difference between the 1st and 3rd quartiles.
    - Middle value of the lower and upper halves of the data
  19. 19"Standard Deviation"- Measures the spread of data about the mean
    - Used to compare sets of data that have the same mean but different range of data
    - Square root of the variance
    CALCULATE:
    1. Establish the mean
    2. Subtract the mean from each value
    3. Square each devation
    4. Sum all the squares
    5. Divide the sum by the no. of data points, -1
    6. Then square root
    = SD
  20. 20"Percentages"Particular kind of scaes with measures of 1-100
  21. 21"Ratios"Calc. the ratio of A to B ; A divided by B
  22. 22"Proportions"A type of ratio in which the denominator is the total number of cases.
  23. 23"Frequency Distribution"Condenses information into a simple format which allows the reader to picture the way in which the variable is distributed
  24. 242. Inferential Statistics- Infer, based on a study of a sample population, what the entire population might think or do.
    - Used in quantitative data analysis for prediction
    - Based on the science of probability
    - Sampling method - probability sampling
  25. 25Types of inferential stats:- T-test
    - ANOVA - MANOVA
    - Correlation tests
    - Simple linear regression
    - Multiple linear regression
  26. 26"T - tests"Used to decide if the means of two groups are statistically different to one another.
  27. 27"ANOVA" "Analysis of variance"- Also used to tests the differences between means of groups
    - Tests differences among the means of many groups at once
    - 'One-way ANOVA': Purpose is to test whether the means of different groups are common or different.
    - 'Two-way ANOVA': Used when groups tested have two different defining characteristics
    - MANOVA: Multivariate version of analysis of variance
  28. 28"Correlation Tests"Measure the extent to which an independent variable predicts a dependent variable
  29. 29"Simple linear regression"Same as correlation test, except it ALSO tells how well the line fits the data.
    The smaller the distance of the data from the regression line, the better the fit
  30. 30"Multiple linear regression"Measures how well multiple independent variables predict the value of a dependent variable
  31. 31"P-value"Probability of an outcome
    p=(0)
    measured from 0-1
    0 = complete certainty prediction will not occur
    1= complete certainty prediction will occur
  32. 32Data analysis can be carried out using:1. Univariate - one varibale
    2. Bivariate - two variable
    3. Multivariate - more than two variables
  33. 33Steps for hypothesis testing:1. Hypotheses are stated and must be mutually exclusive
    2. Then the sample data is tested
    3. Results are interpreted and hypothesis is accepted/rejected
  34. 34Graphing Data:Allows for the communication of large quantities of data in a very succinct manner.
    Visually displays data
    Can make trends and patterns in the data apparent
  35. 35Graph types:1. Line graphs
    2. Scattergram
    3. Bar chart
  36. 36"Line graphs"Used to visually display a line of data
    Useful in discerning trends in data
  37. 37"Bar chart"Graphs data in tabular form
  38. 38"Scattergram"Useful way of visually summarizing bivariate data
    Gives a good representation of the relationship between two variables
  39. 394 Stages of data analysis:1. Descriptive:
    - Simply describing what it is that you see in the data.
    2. Interpretation:
    - Attempting to explain the meaning of what is seen in the data, as you have described it in the descriptive analysis.
    3. Conclusions:
    - Move the interpretation along from stating what you see in the data and what you think it means, to drawing some minor conclusion about that.
    4. Theorisation:
    - Look at literature review and compare findings to other theorists findings.

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Flashcards on BEM 314 chapter 19, created by chelseaklokow on 15/05/2016.
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