🗊Презентация Statistical data processing

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Статистическая обработка данных Prepared by Artur Galimov M.D.
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Статистическая обработка данных Prepared by Artur Galimov M.D.

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Methods Section From JAMA (impact factor - 47.661): In the Methods section, describe statistical methods with enough detail to enable a knowledgeable reader with access to the original data to reproduce the reported results.
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Methods Section From JAMA (impact factor - 47.661): In the Methods section, describe statistical methods with enough detail to enable a knowledgeable reader with access to the original data to reproduce the reported results.

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Study Designs in Medical Research
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Study Designs in Medical Research

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Distinguishing Between Study Designs
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Distinguishing Between Study Designs

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Common types of experiments
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Common types of experiments

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Experiment Introduce a treatment to observe its effects Might not involve randomization Might not even have a control group
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Experiment Introduce a treatment to observe its effects Might not involve randomization Might not even have a control group

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Randomized Experiment The gold standard for demonstrating causality Units (people, animals, groups, etc.) are randomly assigned to receive either treatment or control. If the sample is large enough, we can assume that on the average, everything else about the two groups is similar because the two groups were randomly selected. So any difference between the two groups after the experiment must be due to the treatment.
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Randomized Experiment The gold standard for demonstrating causality Units (people, animals, groups, etc.) are randomly assigned to receive either treatment or control. If the sample is large enough, we can assume that on the average, everything else about the two groups is similar because the two groups were randomly selected. So any difference between the two groups after the experiment must be due to the treatment.

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Quasi-experiment There is a control group, but no random assignment to treatment vs. control Usually happens because it’s impossible or unethical to do random assignment Assignment to conditions occurs by self-selection (some people choose to smoke or exercise or join a program) Example: effects of a new health media campaign that’s introduced in one community but not others The main problem is that the groups are different in other ways (people who become smokers have different demographics and genetics)
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Quasi-experiment There is a control group, but no random assignment to treatment vs. control Usually happens because it’s impossible or unethical to do random assignment Assignment to conditions occurs by self-selection (some people choose to smoke or exercise or join a program) Example: effects of a new health media campaign that’s introduced in one community but not others The main problem is that the groups are different in other ways (people who become smokers have different demographics and genetics)

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Natural experiment (Not exactly an experiment because the experimenter didn’t manipulate the cause, but the cause occurred) Compare a group that experienced a cause with a group that didn’t (Or compare the same group before and after the cause) Examples: effect of a natural disaster, effect of a policy change
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Natural experiment (Not exactly an experiment because the experimenter didn’t manipulate the cause, but the cause occurred) Compare a group that experienced a cause with a group that didn’t (Or compare the same group before and after the cause) Examples: effect of a natural disaster, effect of a policy change

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Correlational study Nonexperimental because nothing is manipulated Measure some variables and see if there’s a mathematical relationship between them Results can be consistent with causality, but they can’t prove causality
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Correlational study Nonexperimental because nothing is manipulated Measure some variables and see if there’s a mathematical relationship between them Results can be consistent with causality, but they can’t prove causality

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Even randomized experiments aren’t perfect Experimental conditions are usually artificial They’re conducted in one particular time and place – might not generalize to other times or places But we usually want to generalize the findings to other times and places Cronbach: we usually want to generalize to other UTOS – units, treatments, observations (outcomes), and settings
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Even randomized experiments aren’t perfect Experimental conditions are usually artificial They’re conducted in one particular time and place – might not generalize to other times or places But we usually want to generalize the findings to other times and places Cronbach: we usually want to generalize to other UTOS – units, treatments, observations (outcomes), and settings

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Populations
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Populations

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Statistical data processing, слайд №13
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Types of Data (Variables) Categorical
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Types of Data (Variables) Categorical

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Types of Data (Variables) Categorical
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Types of Data (Variables) Categorical

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Histograms Know how to interpret a histogram, i.e., normal, skewed left (left tail), skewed right (right tail), and most importantly, infer from it the appropriate descriptive statistics and analytical method, e.g., mean vs median, parametric vs. non-parametric
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Histograms Know how to interpret a histogram, i.e., normal, skewed left (left tail), skewed right (right tail), and most importantly, infer from it the appropriate descriptive statistics and analytical method, e.g., mean vs median, parametric vs. non-parametric

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Measures of Central Tendency Mean: what’s commonly called “average” Median (m): middle-most observation of ordered data n odd: m = the (n + 1)/2-th largest observation n even: m = average of the (n/2)-th and (n/2 + 1)-th largest observations Mode: most frequently occurring observation(s)
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Measures of Central Tendency Mean: what’s commonly called “average” Median (m): middle-most observation of ordered data n odd: m = the (n + 1)/2-th largest observation n even: m = average of the (n/2)-th and (n/2 + 1)-th largest observations Mode: most frequently occurring observation(s)

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Measures of Variability (Dispersion) Range: difference between largest and smallest observations (or actual values) Interquartile range (IQR): the difference between the 25th and 75th percentiles (or actual values) (Sample) Variance: (Sample) Standard Deviation (s or sd): Standard Error of the Mean (se or sem):
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Measures of Variability (Dispersion) Range: difference between largest and smallest observations (or actual values) Interquartile range (IQR): the difference between the 25th and 75th percentiles (or actual values) (Sample) Variance: (Sample) Standard Deviation (s or sd): Standard Error of the Mean (se or sem):

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Statistical data processing, слайд №19
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SPSS Output
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SPSS Output

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SPSS Output
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SPSS Output

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Statistical data processing, слайд №24
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What is correlation? Correlation captures the extent to which two variables have a linear relationship. Correlation coefficients are descriptive statistics that describe the degree or strength of the linear relationship between two variables. To calculate correlations we need pairs of numbers.
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What is correlation? Correlation captures the extent to which two variables have a linear relationship. Correlation coefficients are descriptive statistics that describe the degree or strength of the linear relationship between two variables. To calculate correlations we need pairs of numbers.

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SPSS output
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SPSS output

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Statistical data processing, слайд №29
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Simple linear regression Purpose: to model the change in one variable (Y, the “dependent variable”) as the other variable (X, the “independent variable”) changes. Assumptions Independence: For any particular value of X, the Y-values are statistically independent of each other. Homoscedasticity: For any particular value of X, the Y-values have the same variance. Normality: For any particular value of X, the Y-values have a normal distribution.
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Simple linear regression Purpose: to model the change in one variable (Y, the “dependent variable”) as the other variable (X, the “independent variable”) changes. Assumptions Independence: For any particular value of X, the Y-values are statistically independent of each other. Homoscedasticity: For any particular value of X, the Y-values have the same variance. Normality: For any particular value of X, the Y-values have a normal distribution.

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Procedure for linear regression Make a scatterplot of Y vs. X to determine if data are linear and homoscedastic. If the scatterplot looks reasonable, then assume the simple linear regression model: where  is the intercept,  is the slope, and  represents individual differences (“errors”) from the true population regression line:
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Procedure for linear regression Make a scatterplot of Y vs. X to determine if data are linear and homoscedastic. If the scatterplot looks reasonable, then assume the simple linear regression model: where  is the intercept,  is the slope, and  represents individual differences (“errors”) from the true population regression line:

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Multilevel Structured Data Multilevel data frequently encountered in social sciences research refer to data which contain multilevel (hierarchical or nested) structure. Multilevel structure indicates that data to be analyzed were obtained from units (e.g., individual) which are nested within higher level units (e.g., groups or clusters).
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Multilevel Structured Data Multilevel data frequently encountered in social sciences research refer to data which contain multilevel (hierarchical or nested) structure. Multilevel structure indicates that data to be analyzed were obtained from units (e.g., individual) which are nested within higher level units (e.g., groups or clusters).

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Example of Multilevel Data in Prevention Research In school-based substance use prevention research, schools are usually the units of assignment to experimental conditions (program or control). Data are then collected from both student (micro) and school (macro) levels student (micro) and school (macro) levels to evaluate program effect.
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Example of Multilevel Data in Prevention Research In school-based substance use prevention research, schools are usually the units of assignment to experimental conditions (program or control). Data are then collected from both student (micro) and school (macro) levels student (micro) and school (macro) levels to evaluate program effect.

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Missing Data Data are missing on some variables for some observations. Three goals of missing data handling Minimize bias Maximize use of available information Get good estimates of uncertainty (get accurate estimates of standard error, CI, p value) Not a goal: imputed values “close” to real values
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Missing Data Data are missing on some variables for some observations. Three goals of missing data handling Minimize bias Maximize use of available information Get good estimates of uncertainty (get accurate estimates of standard error, CI, p value) Not a goal: imputed values “close” to real values

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Missing Data: Methods to Deal with Missing Listwise Deletion: Delete cases with any missing on the variables being analyzed. Missing replacement by imputation: Mean replacement: using variable mean or group mean will not affect mean, but reduce variance Regression approach predicting the missing value on one variable with scores on other variables Multiple imputation Sensitivity analysis complete cases vs. missing replacement
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Missing Data: Methods to Deal with Missing Listwise Deletion: Delete cases with any missing on the variables being analyzed. Missing replacement by imputation: Mean replacement: using variable mean or group mean will not affect mean, but reduce variance Regression approach predicting the missing value on one variable with scores on other variables Multiple imputation Sensitivity analysis complete cases vs. missing replacement

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Methods Section Outline Participants and Procedures Measures Data Analysis
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Methods Section Outline Participants and Procedures Measures Data Analysis

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Participants and Procedures
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Participants and Procedures

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Data Analysis
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Data Analysis

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Q/A Session
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Q/A Session

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Arthur Galimov e-mail: galimov@usc.edu IG: ar_galimov
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Arthur Galimov e-mail: galimov@usc.edu IG: ar_galimov

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