Medical research

• Preparing the Data File

  1. Data Entry

  2. Variable Names and Labels

  3. Data Type and Format

  4. Missing Values

  5. Data Cleaning

  6. Save the Data File

  7. Import Data into SPSS
• SPSS


  Statistical software for data analysis

  
• Before conducting data analysis in SPSS, it's crucial to prepare your data file appropriately
• Data Entry


  1. Enter your data into a spreadsheet program like Microsoft Excel or Google Sheets

  2. Each row represents a case or participant, and each column represents a variable

  
• Variable Names


  In SPSS, variable names can be up to 64 characters long and must start with a letter. Avoid using special characters or spaces.
• Variable Labels


  Assign labels to variables to provide descriptive information about each variable. Labels can be longer and more detailed than variable names.
• Data Type and Format


  1. Determine the data type for each variable (e.g., numeric, string, date) and assign an appropriate format

  2. Numeric variables can be integers or decimals, while string variables are used for text data

  3. Date variables should be formatted in a standard date format (e.g., DD/MM/YYYY or MM/DD/YYYY)
• Missing Values


  1. Identify any missing or incomplete data in your dataset and decide how to handle them

  2. In SPSS, missing values can be coded as a specific value (e.g., -999) or left blank
• Data Cleaning


  1. Check for and correct any errors or inconsistencies in the data, such as outliers, duplicates, or data entry mistakes

  2. Remove unnecessary variables or cases that are not relevant to your analysis
• Save the Data File


  1. Save it in a format that SPSS can read, such as .sav (SPSS data file format) or .csv (comma-separated values)

  2. Choose a descriptive file name that reflects the content of your dataset and indicates the version or date of data collection
• Import Data into SPSS


  1. Open SPSS and create a new data file

  2. Import your prepared data file into SPSS using the File > Open > Data menu option or by dragging and dropping the file into the SPSS interface
• Parametric statistics


  More powerful than non-parametric statistics, used for real numbers, e.g., T-test
• Non-parametric statistics


  Not as powerful as parametric statistics, good for category variables, e.g., Mann-Whitney U (Likert)
• SPSS application


  • The default window will have the data editor
  • There are two sheets in the window: Data view and Variable view

  
• Data Entry (by hand)


  1. Click Variable View

  2. Enter variable names and labels

  3. Assign data types and value labels

  4. Click Data View to start entering the data

  
• Data Entry (import from Excel)


  1. Click Open- Data...

  2. Change Files of type to Excel, then browse and open the file

  3. Select the worksheet, the range (if desired), and if to read variable names
• Basic analysis of SPSS that will be introduced in this class


  • Frequencies
  • Descriptives
  • Linear regression analysis
• Preliminary analysis


  • Descriptive statistics
  • Categorical variables
  • Continuous variables
  • Assessing normality
  • Manipulating the data
  • Checking the reliability of a scale
  • Statistical techniques to explore relationships among variables (correlations)
• Descriptive statistics


  Summarize and describe the main features of a dataset, including measures of central tendency (e.g., mean, median, mode) and measures of variability (e.g., standard deviation, range)
• Categorical variables


  Qualitative variables that represent categories or groups, cannot be measured numerically
• Continuous variables


  Quantitative variables that can take on any value within a certain range, typically measured on a continuous scale
• Assessing normality


  Examining the distribution of data to determine if it follows a normal (bell-shaped) distribution
• Data manipulation


  Transforming or reorganizing the dataset to prepare it for analysis, such as recoding variables, creating new variables, or filtering cases
• Checking the reliability of a scale


  Assessing the internal consistency or stability of a measurement instrument (e.g., questionnaire, scale)
• Correlation analysis


  Examines the strength and direction of the relationship between two or more variables
• Frequencies


  Click 'Analyze,' 'Descriptive statistics,' then click 'Frequencies'
• Descriptives


  1. Click 'Analyze,' 'Descriptive statistics,' then 'Descriptives'

  2. Select the options to analyze other descriptive statistics besides the mean and standard deviation
• Graphs


  Click 'Graphs,' 'Legacy Dialogs,' 'Interactive,' and 'Scatter plot' from the main menu
• Parametric statistics in SPSS


  • Independent-samples t-test
  • Paired samples t-test
  • One-way analysis of variance
  • One-way between groups ANOVA with post hoc-tests
• Non-parametric statistics in SPSS


  • Chi-square
  • Mann-Whitney U test
  • Kruskal-Wallis Test
  • Spearman's Rank Order Correlation
• Chi-square Test


  Assesses the association between two categorical variables
• Mann-Whitney U Test


  Compares the distributions of two independent groups when the dependent variable is ordinal or continuous but not normally distributed
• Kruskal-Wallis Test


  A non-parametric alternative to one-way ANOVA, compares the distributions of three or more independent groups
• Spearman's Rank Order Correlation


  Assesses the strength and direction of the relationship between two ordinal or continuous variables
• Parametric statistics


  Used when data meet certain assumptions about the population distribution, such as normality and homogeneity of variances
• Independent-samples t-test


  Used to compare the means of two independent groups to determine whether there are statistically significant differences between them
• Paired Samples t-test


  Used to compare the means of two related groups or the same group measured at two different time points
• 1. test


  Used to compare the means of two independent groups to determine whether there are statistically significant differences between them
• Application of t-test in SPSS


  1. Go to "Analyse" > "Compare Means" > "Independent-Samples T Test"

  2. Select the variable representing the dependent variable and the variable representing the grouping variable (e.g., treatment vs. control group)

  3. Click "OK" to generate the results, including the t-value, degrees of freedom, and p-value
• Research Question


  Does a new medication result in significantly lower blood pressure compared to a placebo?