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DATA ANALYSIS

DATA ANALYSIS. Indawan Syahri. Qualitative Data - Words. Recording Data: Transcripts from taped Interview Field-notes of Observation Diaries Photographs Documents. Qualitative Data - Typologies. Types of errors Errors in Addition Errors in Omission etc. Social variables: Gender

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DATA ANALYSIS

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  1. DATA ANALYSIS IndawanSyahri

  2. Qualitative Data - Words • Recording Data: • Transcripts from taped Interview • Field-notes of Observation • Diaries • Photographs • Documents

  3. Qualitative Data - Typologies • Types of errors • Errors in Addition • Errors in Omission • etc. • Social variables: • Gender • Ages • Occupations • Ethnic groups • etc.

  4. The data appear in words rather than in numbers. • The data may have been collected in a various ways: • Observation • Interviews • Extracts from documents • Tape recordings • Numbers used have no arithmetic values. • Numbers may be used for coding the data. e.g.: Male (1), Female (2), Teachers (3), Bankers (4), Policemen (5) • Qualitative data exist dominantly in descriptive studies Reminders:

  5. Stages in Qualitative Data Analysis

  6. Quantitative Data – Descriptive Statistics

  7. Measures of Central Tendency Central tendency is used to talk about the central point in the distribution of value in the data.

  8. Measures of variability • In order to describe the distribution of interval data, the measure of central tendency will not suffice. • To describe the data more accurately, we have to measure the degree of variability of the data of the data from the measure of central tendency. • There are 3 ways to show the data are spread out from the point, i.e. range, variance, and standard deviation.

  9. Range • Range = X highest – X lowest • E.g. The youngest student is 17 and the oldest is 42, Range = 42 – 17 = 25 The age range in this class is 25. • If range is so unstable, some researchers prefer to stabilize it by using the semi-interquartile range (SIQR) SIQR = Q3 – Q1 / 2 Q3 is the score at the 75th percentile and Q1 is the score at the 25th percentile. • E.g., the score of the toefl score at the 75th percentile is 560 and 470 is the score at the 25th percentile. SIQR is 560 – 470 / 2 = 45

  10. Variance • To see how close the scores are to the average for the test. • E.g., if the mean score on the exam was 93.5 and a student got 89, the deviation of the score from the mean is 4.5. • If we want a measure that takes the distribution of all scores into account, it is variance. • To compute variance, we begin with the deviation of the individual scores from the mean. Stages: • Compute the mean: X • Subtract the mean from each score to obtain the individual deviation scores x = X – X. • Square each individual deviation and add: ∑ x² • Divide by N – 1: ∑ x²/ N - 1

  11. Standard Deviation • Variance = standard deviation are to give us a measure that show how much variability there is in the scores. • They calculate the distance of every individual score from the mean. • Standard deviation goes one step further, to take the square root of the variance. S =√∑ (X –X)²/ N – 1 or s = √ ∑x² / N - 1

  12. QUANTITATIVE DATA –Inferential Statistics • Correlation is that area of statistics which is concerned with the study of systematic relationships between two (or more) variables. • It attempts to answer questions such as: • Do high values of variable X tend to go together with high values of variable Y? (positive correlation) • Do high values of X go with low values of Y? (negative correlation) • Is there some more complex relationship between X and Y, or perhaps no relationship at all?

  13. Visual representation of correlation: Scatter diagram Y Y Y X X High positive r X High negative r Lower positive r Y Y Y X X X Nonlinear r Lower negative r No r

  14. Correlation coefficients: • To supplement the information given by a scatter diagram a correlation coefficient is normally calculated. • The expressions for calculating such coefficients are so devised that a value of +1 is obtained for perfect positive correlation, a value of -1 for perfect negative correlation, a value of 0 for no correlation at all. • For interval variables, the appropriate measure is the so-called Pearson product-moment correlation coefficient. • For ordinal variables (scattergraghsare not really appropriate), they use the Spearman rank correlation coefficient. • For nominal variables, they use the phi coefficient.

  15. t-test • t-test is used to compare two means of sets of scores: • Pre-test vs. posttest • Test scores in experimental group vs. test scores in control group • It means to observe the differences between the scores obtained by Group A and those obtained by Group B

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