data analysis

Introduction to Data Analysis in Quantitative Studies

  • Objective for today: Understand methods of data analysis in quantitative research

  • Instructor's self-explanation:

    • Not a statistician

    • Relies on resources (Google, YouTube, research texts)

  • Importance of being able to research statistics and understand tools used in studies

    • Emphasizes understanding over memorization

  • Importance of statistical analysis in various projects, such as:

    • Quality Improvement (QI) projects

    • Evidence-based Practice (EBP) projects

Understanding Quantitative Data Analysis

  • Key Concepts in Analyzing Quantitative Data

    • Statistical methods help:

    • Explain findings

    • Assess intervention effectiveness

    • Identify relationships between variables

    • Draw meaningful conclusions

Factors Affecting Data Analysis

  • Researchers consider:

    • Information desired

    • Data collection methods

    • Data analysis methods

  • Key considerations:

    • Normal Distribution

    • Whether data are normally distributed affects analysis methods

    • Levels of Measurement

Levels of Measurement in Statistics

  • Four levels of measurement:

    1. Nominal Data

    • Lowest level, categorized data

    • Examples: marital status, political affiliation, medical record numbers

    • Can often be identified by yes/no responses

    1. Ordinal Data

    • Data ranked in order

    • Examples: Likert scales (agree/disagree), pain scales, class ranks

    1. Interval Data

    • Ordered data with equal distance between values

    • No true zero point

    • Example: temperature

    1. Ratio Data

    • Highest measurement level with a true zero

    • Examples: blood pressure, heart rate, academic scores

Implications of Measurement Levels

  • Higher measurement levels allow for more sophisticated statistical analysis

  • Types of statistical tests applicable depend on distribution and measurement level

Statistical Methods Overview

  • Types of statistics:

    1. Descriptive Statistics

    • Used to describe or summarize data

    • Includes frequency distributions, measures of central tendency (mean, median, mode), measures of variability (range, standard deviation)

    1. Inferential Statistics

    • Draws conclusions about a population based on a sample

    • Tests hypotheses, evaluates differences between groups, examines significance of results

Measures of Central Tendency

  • Mean: average

    • Calculated by summing all scores and dividing by the number of scores

  • Median: middle score that divides data into two halves

  • Mode: most frequently occurring score

  • When data is normally distributed, mean, median, and mode are similar

Statistical Distribution Characteristics

  • Normal Distribution

    • Bell-shaped curve, symmetrical

    • One peak (unimodal)

  • Skewed Distribution

    • Positive Skew: tail longer to the right

    • Negative Skew: tail longer to the left

Variability Measures in Statistics

  • Range: Difference between the highest and lowest values

  • Percentiles: Specific data points that partition the data into segments

  • Standard Deviation: Indicates how much scores deviate from the mean; a measure of dispersion

Inferential Statistics and Hypothesis Testing

  • Importance of hypothesis testing to determine if research findings are due to chance

  • Type I Error: Rejecting the null hypothesis when it is true

  • Type II Error: Accepting the null hypothesis when it is false

  • Statistical significance tested using p-values

    • Common threshold: p ≤ 0.05 indicates significance

Correlation in Statistics

  • Explains relationships between two variables

  • Pearson's r: coefficient value between -1 and 1 indicating strength of correlation

    • Closer to 1 or -1 indicates stronger relationships

    • Zero indicates no correlation

Practical Application of Statistics in Research

  • Examining evidence in research articles:

    • Determining if findings are actionable or significant

    • Consideration of measurement levels, statistical tests, and p-values to evaluate research

Conclusion

  • Encouragement to review OVC risk knowledge article in relation to today’s lecture content

  • Reminder about understanding statistical principles to better analyze research

  • Next meeting scheduled to cover additional content and revisit previously discussed articles