Data Analysis PSYA4

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Introduction Inferential tests

Significance and Probability

  • Inferential tests provide a means of assessing whether any pattern in data collected is meaningful or significant.
  • They enable us to make inferences form the research sample to the population.
  • Probability= likelihood that a pattern of results could arise by chance.
  • Probability levels represent acceptable level of risk or of making a Type 1 error.
  • More important research requires more strigent significance levels
  • Type 1 error= null hypothesis rejected when true.
  • Type 2 error= null hypothesis accepted when false.

Inferential tests

  • Significance of observed value determined in table of critical values.
  • Degree of Freedom normally the amount of participants in the study.
  • One tailed test is a directional hypothesis
  • Two tailed test is a non-directional hypothesis
  • Significance level is usually p(equal to or more than) 0.05 (5%)
  • Different research designs and levels of measurement (nominal, ordinal, interval) require different tests
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Inferential tests- Spearman's Rho

Used when...

  • Hypothesis states correlational between two variables.
  • Each person is measured on both variables.
  • Data is at least ordinal (i.e not norminal)
  • With repeated measurres and matched pairs
  • Scatter line graph is used
  • Observed value is greater than or same as the critical value.
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Inferential tests- Chi-Square

Used when...

  • Hypothesis states differences between two conditions or association between two variables
  • Data is independant.
  • Data in frequencies. (nominal)
  • Expected frequencies in each cell must not fall below 5.
  • Bar graph is used
  • Observed value equal to or greater than the critical value
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Inferential tests- Mann-Whitney U-Test

Used when...

  • Hypothesis states difference between two sets of data.
  • Independant groups design
  • Data at least ordinal (i.e not nominal)
  • Bar chart is used
  • Observed value is equal to or less than the critical
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Inferential tests- Wilcoxon T Test

Used when...

  • Hypothesis states difference between two sets of data
  • Related design (repeated measures or matched pairs)
  • Data at least ordinal (i.e not norminal)
  • Line of scatter graph is used.
  • Observed value is equal to or less than the Critical value.
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Descriptive Statistics- Central Tendency

  • Indicates typical or 'average' score
  • Mean = sum of all scores divided by number of scores however it is unrepresentative of extreme scores
  • Median = middle value in ordered list of scores. Not affected by extreme scores but not as sensitive of all scores than the mean.
  • Mode = most common value. Not useful is there are many modes in a set of scores.
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Descriptive Statistics- Measures of Dispersion

  • Indicate spread of scores
  • Range= difference between highest and lowest score. Not representative if extreme scores.
  • Standard Deviation= spread of data around mean. Precise measure but influence of extreme scores not taken into account.
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Descriptive Statistics- Graphs

  • Bar Chart= Illustration of frequency, height of bar represents frequency.
  • Scattergram= illustration of correlation, suitable for correlational data. Indicated strength of correlation and direction (positive or negative).
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Qualitive Data

Key Points

  • Quantitive methods not relevant to 'real life'
  • Qualitive methods represent world as seen by individual (subjective)
  • Data sets tend to be large but few participants
  • Qualitive data connot be reduced to numbers
  • Reflexivity indicates attitudes and biases of researcher.
  • Validity demonstrated by triangulation.

Qualitive analysis

  • Summarised by identifying themes in data.
  • Inductive (bottom-up) approach so themes emerge, although sometimes deductive (top-down)
  • Iterative process- imposing order on the data, reflexting participants perspective.
  • 1) Consider data
  • 2) Break into meaningful units
  • 3) Code each unit
  • 4) Create categories themes
  • 5) Check themes using new data set
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Qualitive V Quantitive

  • Quantitive easy to analyse and produces neat conclusions
  • But oversimplifies reality and human experience.
  • Qualitive sata represents true complexities of behaviour through rich detail of thoughts, feelings etc.
  • But more difficult to detect patterns and subject to bias of subjectivity.
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