Psychology A Level
Research Methods (Part 2)

What is Content Analysis?

Content analysis involves a researcher establishing coding units before they look through their qualitative data. They then go through the data and count up the number of times each coding unit appears in the data. As a result, the qualitative data is turned into quantitative, nominal data! And, unlike thematic analysis, content analysis can be done on any form of qualitative data!

 

Transcript

Lesson overview

Types of Data

  1. Types of Data – Introduction
  2. Quantitative vs Qualitative Data – Part 1
  3. Quantitative vs Qualitative Data – Part 2
  4. Frequency Tables
  5. Nominal vs Ordinal Data
  6. Ratio Data
  7. Ratio vs Interval Data
  8. Continuous vs Discrete Data – Part 1
  9. Continuous vs Discrete Data – Part 2

Data Presentation

  1. Data Presentation – Introduction
  2. Frequency Graphs
  3. What Is a Data Distribution?
  4. Positive and Negative Skew
  5. Histograms
  6. Bar Charts

Descriptive Statistics

  1. Descriptive Statistics – Introduction
  2. Measuring Central Tendency
  3. Measuring Dispersion
  4. Describing a Distribution Without a Frequency Graph
  5. The Mode – Part 1
  6. The Mode – Part 2
  7. The Range – Part 1
  8. The Range – Part 2
  9. The Mean – Part 1
  10. The Mean – Part 2
  11. The Median
  12. Calculating the Median – Part 1
  13. Calculating the Median – Part 2
  14. Measures of Central Tendency and Skew
  15. Standard Deviation
  16. Calculating Standard Deviation – Part 1
  17. Calculating Standard Deviation – Part 2
  18. Comparing Measures of Central Tendency
  19. Comparing Measures of Central Tendency: Number of Values
  20. Comparing Measures of Central Tendency: Skew
  21. Comparing Measures of Central Tendency: Representativeness
  22. Comparing Measures of Central Tendency: Types of Data
  23. Comparing Measures of Dispersion: Ease of Calculation
  24. Comparing Measures of Dispersion: Representativeness

Percentages

  1. Percentages – Introduction
  2. What Is a Percentage?
  3. Percentages to Fractions
  4. Percentages to Decimals
  5. Decimals to Percentages
  6. Fractions to Percentages
  7. Calculating Percentages – Part 1
  8. Calculating Percentages – Part 2
  9. Percentage Change – Part 1
  10. Percentage Change – Part 2
  11. Percentage Change – Part 3
  12. Percentage Change – Part 4
  13. Pie Charts – Part 1
  14. Pie Charts – Part 2
  15. Pie Charts – Part 3
  16. Pie Charts – Part 4
  17. Pie Charts V Bar Charts

Probability

  1. Probability – Introduction
  2. What Is Probability?
  3. Numbers Instead of Words
  4. Representing Probabilities as Fractions
  5. Representing Probabilities as Percentages
  6. Comparing Probabilities
  7. Probability and Populations
  8. Probability and the Normal Distribution – Part 1
  9. Probability and the Normal Distribution – Part 2

Inferential Statistics – Interval Data

  1. Interval Data – Introduction
  2. Making Inferences From Populations
  3. Sampling Error
  4. Assumption of No Difference
  5. The Null Hypothesis
  6. The t-Value
  7. Factors Affecting the Size of the t-Value – Part 1
  8. Factors Affecting the Size of the t-Value – Part 2
  9. Factors Affecting the Size of the t-Value – Part 3
  10. p-Values
  11. Accepting and Rejecting the Null Hypothesis
  12. Significance Levels
  13. Type 1 Error
  14. Type 1 Error and Significance Levels
  15. Type 2 Error
  16. Balancing Type 1 and Type 2 Errors
  17. Introduction to Using Tables to Test the Null Hypothesis
  18. Critical t-Values
  19. Using Tables of Critical Values to Test the Null Hypothesis – Part 1
  20. Using Tables of Critical Values to Test the Null Hypothesis – Part 2
  21. Degrees of Freedom
  22. The Alternative Hypothesis
  23. Directional and Non-Directional Alternative Hypotheses
  24. Using the t-Value Table for Directional and Non-Directional Hypotheses
  25. One-Tailed and Two-Tailed Tests
  26. Independent Groups vs Repeated Measures t-Test
  27. Reading the Table for Related and Unrelated t-Tests

Inferential Statistics – Ordinal Data

  1. Inferential Statistics – Ordinal Data – Introduction
  2. Inferential Statistics on Ordinal Data
  3. The Mann-Whitney U Test
  4. The Mann-Whitney U Test: The U Value
  5. Factors Affecting the Size of the U Value
  6. The Mann-Whitney U Test: Reading the Table
  7. The Mann-Whitney U Test: The Two Tailed Test
  8. The Mann-Whitney U Test: Joint Rankings
  9. The Wilcoxon Test
  10. The Wilcoxon Test: The t-Value
  11. The Wilcoxon Test: Reading the Table – Part 1
  12. The Wilcoxon Test: Reading the Table – Part 2
  13. The Wilcoxon Test: The Two Tailed Test

Inferential Statistics – Nominal Data

  1. Nominal Data – Introduction
  2. Inferential Statistics on Nominal Data
  3. The Chi-Squared Test: Contingency Tables
  4. The Chi-Squared Test: The x Value – Part 1
  5. The Chi-Squared Test: The x Value – Part 2
  6. The Chi-Squared Test: Degrees of Freedom
  7. The Chi-Squared Test: Reading the Table
  8. The Chi-Squared Test: Two Tailed Test
  9. Nominal Data With a Repeated Measures Design

Inferential Statistics – The Sign Test

  1. The Sign Test – Introduction
  2. The Sign Test
  3. The Sign Value
  4. The Sign Test: Reading the Table
  5. The Sign Test: Two Tailed Test
  6. Nominal Data and the Sign Test
  7. The Sign Test: Transforming Data Into Nominal Data

Inferential Statistics – Correlation

  1. Correlation – Introduction
  2. Scattergrams
  3. Correlations and Hypothesis Testing – Part 1
  4. Correlations and Hypothesis Testing – Part 2
  5. Pearson’s r
  6. Pearson’s r: Reading the Table – Part 1
  7. Pearson’s r: Reading the Table – Part 2
  8. Pearson’s r: Two-Tailed Tests
  9. Correlational Studies With Ordinal Data
  10. Spearman’s Rho
  11. Spearman’s Rho: Reading the Table
  12. Spearman’s Rho: the Two Tailed Test
  13. Correlational Studies With Nominal Data

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