QUANTITATIVE RESEARCH

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SUMMARY OF CHAPTER 9
Jessica Sarabia
Flowchart by Jessica Sarabia, updated more than 1 year ago
Jessica Sarabia
Created by Jessica Sarabia almost 7 years ago
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Flowchart nodes

  • Measures of frequency
  • Measures of central tendency
  • MODE
  • MEDIAN
  • Is the score at the center of the distribution
  • It is the mist frequent score obtained by a particullar group of learners
  • MEAN
  • It is the arithmetic average
  • Measures of dispercion
  • STANDARD DERIVATION
  • It is a number that shows how scores are spread around the mean
  • DESCRIPTIVE STATISTICS
  • QUANTITATIVE RESEARCH
  • NORMAL DISTRIBUTION
  • Describes the clusterings of scores/ behaviours, also known as a bell curve
  • STANDARDS SCORES
  • There are times when we want to compare an individual's performance on different tests.
  • PROBABILITY
  • It provides confidence in the claims that are being made about the analysis of the data.
  • INFERENTIAL STATISTICS
  • the goal is to generalize beyond the results.
  • Prerequisites
  • two—standard error of the mean and standard error of the difference between sample means
  • Degrees of Freedom
  • is the number of scores that are not fixed.
  • Critical Values
  • This is the value that we can use as a confidence measure to determine whether our hypothesis can be substantiated.
  • One-Tailed Versus Two-Tailed Hypotheses
  • The former (those that predict a difference in one direction) are known as one-tailed hypotheses and require a different critical value than do the "neutral" or two-tailed hypotheses.
  • Parametric Versus Nonparametric Statistics
  • Parametric Statistics
  • t-tests
  • Analysis of Variance (ANOVA)
  • Two-way ANOVA
  • Analysis of Covariance (ANCOVA).
  • Multivariate Analysis of Variance (MANOVA)
  • Repeated Measures ANOVA
  • Nonparametric Tests
  • Chi Square (X2)
  • Mann-Whitney U/Wilcoxon Rank Sums
  • Kruskal-Wallis/Friedman
  • STATISTICAL TABLES
  • tables that can be consulted to determine if your test results are significant
  • STRENGTH OF ASSOCIATION
  • There are times when we might want to determine how much of the variation is actually due to the independent variable in question
  • ETA2 AND OMEGA2
  • goes beyond the fact that there is a significant difference and gives us an indication of how much of the variability is due to our independent variable
  • EFFECT SIZE
  • It is a measure that gives an indication of the strength of one's findings.
  • META-ANALYSES
  • To make a meaningful comparison, effect sizes become the main comparative tool.
  • ORRELATION
  • Correlational research attempts to determine the relationship between or among variables; it does not determine causation.
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