Data Analysis. Associate.Prof.Dr.Ratana Sapbamrer Department of Community Medicine, Faculty of Medicine Chiang Mai University
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1 Data Analysis Associate.Prof.Dr.Ratana Sapbamrer Department of Community Medicine, Faculty of Medicine Chiang Mai University
2 Topic Outline Data analysis for descriptive statistics (qualitative data) Data analysis for descriptive statistics (quantitative data) Data analysis for analytical/inferential statistics Methods for normal distribution test
3 Review of knowledge Statistics Types of Statistics Descriptive Analytical/Inferential Describe features of data (1) make assumptions about the population at large (2) make predictions about what might happen in the future. Testing hypothesis Independent VS Dependent variables 3 Predictor VS Outcome
4 Review of knowledge Scale of Data Qualitative/ Categorical Quantitative/ Numerical Nominal Ordinal Interval Ratio
5 Data analysis for descriptive statistics
6 Data analysis for descriptive statistics in different scales Scale Qualitative Quantitative Nominal Ordinal Interval Ratio Frequency distribution Frequency Percentage 6 Central tendency Dispersion
7 Data analysis for Descriptive statistics (qualitative data) Qaulitative data Nominal Ordinal Frequency distribution Frequency Distribution table Pie chart One way table Two way table Percentage Bar chart Line chart
8 One way distribution table Analyze Descriptive statistic Frequency
9 Example: one way distribution table Personal data of gender
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13 Two way distribution table Analyze Descriptive statistic Table Custom table
14 Example: personal data classified by gender and headache symptom
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18 Pie chart Bar chart analyze by Excel and SPSS Line chart
19 Data analysis for descriptive statistics (quantitative data) Stat. for quantitative data Central tendency Dispersion Mean Median Mode Standard deviation Percentile IQR Coefficien t of variance Range Analyze Descriptive statistic frequency
20 Example: age
21
22 Central tendency Dispersion
23 Data analysis for Analytical/inferential statistics
24 Testing hypothesis Independent VS Dependent variables Analytical/inferential statistics Normal distribution Non-normal distribution Parametric test Testing normal distribution Non-parametric test
25 Method of normal distribution test Graph -Mean and Median -Histogram -Stem and leaf -Boxplot Statistics -Kolmogorov-Smirnov (n > 50) -Shapiro-Wilk (n 3-50) Analyze Compare mean explore
26 Mean and Median Descriptives cholesterol Mean 95% Confidence Interval for Mean Lower Bound Upper Bound Statistic Std. Error % Trimmed Mean Median Variance Std. Dev iation Minimum Maximum Range Interquartile Range Skewness Kur tosis
27 Histogram Stem and leaf Boxplot
28 * ค าข อม ลท มากกว า Q3 + 3IQR เร ยก Extreme ค าข อม ลท มากกว า Q IQR เร ยก Outlier ค าข อม ลท ย งไม ส งผ ดปกต ค าส งส ดของ Box = Q3 ค าม ธยฐาน= Q2 ค าต าส ดของ Box = Q1 ค าข อม ลท ย งไม ต าผ ดปกต ค าข อม ลท มากกว า Q1-1.5IQR เร ยก Outlier * 28 ค าข อม ลท น อยกว า Q1-3IQR เร ยก Extreme
29 Central tendency สมมาตร เบ บวก/ เบ ขวา Skewness+ เบ ลบ/ เบ ซ าย Skewness- 29
30 Dispersion High dispersion Low dispersion High dispersion Low dispersion 30
31 Statistics Kolmogorov- Smirnov test Shapiro-Wilk test N > 50 N 3-50 Tests of Normality Kolmogorov-Smirnov a Shapiro-Wilk Statistic df Sig. Statistic df Sig. cholesterol * *. This is a lower bound of the true significance. a. Lilliefors Significance Correction
32 Example: Testing normal distribution of age Analyze Compare mean explore
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35 Non-normal distribution
36 age Stem-and-Leaf Plot Frequency Stem & Leaf Non-normal distribution Stem width: 10 Each leaf: 1 case(s)
37 Non-normal distribution
38 Testing hypothesis Independent VS Dependent variables Analytical/inferential statistics Normal distribution Non-normal distribution Parametric test Testing normal distribution Non-parametric test
39 Independent/ predictor Qualitative data 2 groups (pre-post) Qualitative data 2 groups (independent each other) Qualitative data >2 groups (independent each other) Dependent/ outcome Quantitative data Quantitative data Parametric test Non-parametric test Paired sample t-test 2-relate sample test 2-independent sample t-test Mann-Whitney U test Quantitative data One-way ANOVA Kruskal Wallis test Quantitative data Quantitative data Pearson correlation Spearman correlation Qualitative data Qualitative data Chi-square Qualitative + Qauntitative data Qualitative + Qauntitative data Quantitative data Qualitative data Multiple linear regression Discriminant analysis
40 Example: parametric test Pearson correlation Independent/ predictor Dependent/ outcome Parametric test Non-parametric test Quantitative data Quantitative data Pearson correlation Spearman correlation Example: Investigation association between age and cholesterol levels Independent/predictor Dependent/outcome Age (Ratio) Cholesterol levels (Ratio)
41 ถ าอาย มากข น ระด บคลอเรสเตอรอลจะส งข น ถ าอาย มากข น ปร มาณมวลกระด ก จะลดลง
42 ความส มพ นธ Positive correlation Negative correlation การแปลความ สมบ รณ ส งมาก ส ง ปานกลาง ต า ต ามาก ไม น ยสาค ญ 42
43 age Correlations Totol lipid, age mg/dl age Pearson Correlation 1.899** Sig. (2-tailed)..000 N Totol lipid, mg/dl Pearson Correlation.899** 1 Sig. (2-tailed).000. N **. Correlation is significant at the 0.01 lev el (2-tailed).
44 Example: Correlation between age and cholesterol levels
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46
47 Pearson correlation coefficient Correlation between Ratio and Ratio
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50 Practice: (1) Oneway distribution table: personal data of education status (2) Two way distribution table: personal data classified by gender and occupation (3) Two way distribution table: personal data classified by smoking and headache symptom
51 Practice: (4) Central tendency and dispersion of age (Mean, median, min, max, SD., Percentile25th and 75 th ) (5) Test of normality of cholesterol (Mean, median, boxplot, histogram, stem and leaf) (6) Correlation coefficient between age and cholesterol levels
52 The end
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