Up Learn – A Level Psychology (AQA) – 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!
A*/A guaranteed or your money back
More informationWant to see the whole course?
No payment info required!
More videos on Research Methods (Part 2):
Themes in Qualitative Data (free trial)
Thematic Analysis (free trial)
Thematic vs Content Analysis: How Useful? (free trial)
Research Methods (Part 2)
2. Quantitative vs Qualitative Data – Part 1 (free trial)
3. Quantitative vs Qualitative Data – Part 2 (free trial)
4. Frequency Tables (free trial)
5. Nominal vs Ordinal Data (free trial)
6. Ratio Data (free trial)
7. Ratio vs Interval Data (free trial)
8. Continuous vs Discrete Data – Part 1 (free trial)
9. Continuous vs Discrete Data – Part 2 (free trial)
2. Measuring Central Tendency (free trial)
3. Measuring Dispersion (free trial)
4. Describing a Distribution Without a Frequency Graph (free trial)
5. The Mode – Part 1 (free trial)
6. The Mode – Part 2 (free trial)
7. The Range – Part 1 (free trial)
8. The Range – Part 2 (free trial)
9. The Mean – Part 1 (free trial)
10. The Mean – Part 2 (free trial)
11. The Median (free trial)
12. Calculating the Median – Part 1 (free trial)
13. Calculating the Median – Part 2 (free trial)
14. Measures of Central Tendency and Skew (free trial)
15. Standard Deviation (free trial)
16. Calculating Standard Deviation – Part 1 (free trial)
17. Calculating Standard Deviation – Part 2 (free trial)
18. Comparing Measures of Central Tendency (free trial)
19. Comparing Measures of Central Tendency: Number of Values (free trial)
20. Comparing Measures of Central Tendency: Skew (free trial)
21. Comparing Measures of Central Tendency: Representativeness (free trial)
22. Comparing Measures of Central Tendency: Types of Data (free trial)
23. Comparing Measures of Dispersion: Ease of Calculation (free trial)
24. Comparing Measures of Dispersion: Representativeness (free trial)
2. What Is a Percentage? (free trial)
3. Percentages to Fractions (free trial)
4. Percentages to Decimals (free trial)
5. Decimals to Percentages (free trial)
6. Fractions to Percentages (free trial)
7. Calculating Percentages – Part 1 (free trial)
8. Calculating Percentages – Part 2 (free trial)
9. Percentage Change – Part 1 (free trial)
10. Percentage Change – Part 2 (free trial)
11. Percentage Change – Part 3 (free trial)
12. Percentage Change – Part 4 (free trial)
13. Pie Charts – Part 1 (free trial)
14. Pie Charts – Part 2 (free trial)
15. Pie Charts – Part 3 (free trial)
16. Pie Charts – Part 4 (free trial)
17. Pie Charts V Bar Charts (free trial)
2. What Is Probability? (free trial)
3. Numbers Instead of Words (free trial)
4. Representing Probabilities as Fractions (free trial)
5. Representing Probabilities as Percentages (free trial)
6. Comparing Probabilities (free trial)
7. Probability and Populations (free trial)
8. Probability and the Normal Distribution – Part 1 (free trial)
9. Probability and the Normal Distribution – Part 2 (free trial)
2. Making Inferences From Populations (free trial)
3. Sampling Error (free trial)
4. Assumption of No Difference (free trial)
5. The Null Hypothesis (free trial)
6. The t-Value (free trial)
7. Factors Affecting the Size of the t-Value – Part 1 (free trial)
8. Factors Affecting the Size of the t-Value – Part 2 (free trial)
9. Factors Affecting the Size of the t-Value – Part 3 (free trial)
10. p-Values (free trial)
11. Accepting and Rejecting the Null Hypothesis (free trial)
12. Significance Levels (free trial)
13. Type 1 Error (free trial)
14. Type 1 Error and Significance Levels (free trial)
15. Type 2 Error (free trial)
16. Balancing Type 1 and Type 2 Errors (free trial)
17. Introduction to Using Tables to Test the Null Hypothesis (free trial)
18. Critical t-Values (free trial)
19. Using Tables of Critical Values to Test the Null Hypothesis – Part 1 (free trial)
20. Using Tables of Critical Values to Test the Null Hypothesis – Part 2 (free trial)
21. Degrees of Freedom (free trial)
22. The Alternative Hypothesis (free trial)
23. Directional and Non-Directional Alternative Hypotheses (free trial)
24. Using the t-Value Table for Directional and Non-Directional Hypotheses (free trial)
25. One-Tailed and Two-Tailed Tests (free trial)
26. Independent Groups vs Repeated Measures t-Test (free trial)
27. Reading the Table for Related and Unrelated t-Tests (free trial)
2. Inferential Statistics on Ordinal Data (free trial)
3. The Mann-Whitney U Test (free trial)
4. The Mann-Whitney U Test: The U Value (free trial)
5. Factors Affecting the Size of the U Value (free trial)
6. The Mann-Whitney U Test: Reading the Table (free trial)
7. The Mann-Whitney U Test: The Two Tailed Test (free trial)
8. The Mann-Whitney U Test: Joint Rankings (free trial)
9. The Wilcoxon Test (free trial)
10. The Wilcoxon Test: The t-Value (free trial)
11. The Wilcoxon Test: Reading the Table – Part 1 (free trial)
12. The Wilcoxon Test: Reading the Table – Part 2 (free trial)
13. The Wilcoxon Test: The Two Tailed Test (free trial)
2. Inferential Statistics on Nominal Data (free trial)
3. The Chi-Squared Test: Contingency Tables (free trial)
4. The Chi-Squared Test: The x Value – Part 1 (free trial)
5. The Chi-Squared Test: The x Value – Part 2 (free trial)
6. The Chi-Squared Test: Degrees of Freedom (free trial)
7. The Chi-Squared Test: Reading the Table (free trial)
8. The Chi-Squared Test: Two Tailed Test (free trial)
9. Nominal Data With a Repeated Measures Design (free trial)
2. The Sign Test (free trial)
3. The Sign Value (free trial)
4. The Sign Test: Reading the Table (free trial)
5. The Sign Test: Two Tailed Test (free trial)
6. Nominal Data and the Sign Test (free trial)
7. The Sign Test: Transforming Data Into Nominal Data (free trial)
2. Scattergrams (free trial)
3. Correlations and Hypothesis Testing – Part 1 (free trial)
4. Correlations and Hypothesis Testing – Part 2 (free trial)
5. Pearson’s r (free trial)
6. Pearson’s r: Reading the Table – Part 1 (free trial)
7. Pearson’s r: Reading the Table – Part 2 (free trial)
8. Pearson’s r: Two-Tailed Tests (free trial)
9. Correlational Studies With Ordinal Data (free trial)
10. Spearman’s Rho (free trial)
11. Spearman’s Rho: Reading the Table (free trial)
12. Spearman’s Rho: the Two Tailed Test (free trial)
13. Correlational Studies With Nominal Data (free trial)
Last time, we looked at the method of thematic analysis.
To conduct thematic analysis:
First, a researcher converts their data into written form.
Second, they familiarise themselves with the data.
Third, they label the data using codes.
Fourth, they categorise their codes into themes.
And, finally, they write a report interpreting their themes, and relating them back to the research topic.
So: thematic analysis is one way of analysing qualitative data.
A second type of qualitative data analysis is called content analysis.
For instance: Iris’ colleague Isla wants to carry out content analysis on Iris’ data about how people’s lives change after a brain injury.
Isla decides ahead of time that she wants to look for instances of anxiety, forgetting, and difficulty focusing.
Next, Isla listens to the audio recordings of the interview, and counts up how many times the participants comment on each of these categories.
For instance, when she hears a participant say “I keep struggling to remember where I’ve put my keys”, she’ll add one to the forgetting row.
And if she hears a participant say “I feel anxious about starting work again…”, she’ll add one to the anxiety row.
And we call this method content analysis
Now, there are four key differences between content analysis and thematic analysis.
First, if researchers are conducting thematic analysis, researchers must put their data in written form
Whereas, if they are conducting content analysis, researchers can use written data, but they can also use other forms of data, like audio recordings.
Second, in thematic analysis…
In thematic analysis, researchers label their data using codes, and then categorise their codes into themes.
Whereas, in content analysis, we call these categories coding units. And content analysis involves counting up instances of each coding unit….which is actually easy to remember, because ‘count’ is hidden in the term ‘coding unit’.
Third, we saw that, when conducting thematic analysis, Iris read through her data over and over again….and she came up with her codes only once she was very familiar with her data.
On the other hand, when conducting content analysis, Isla…
When conducting content analysis, Isla came up with her coding units before she went through her data even once!
Fourth and finally, when Iris conducted thematic analysis, she wrote a report with an interpretation of the themes in her data.
Whereas, when Isla conducted content analysis, she ended up with a count of the number of times each coding unit appeared, which is…
When Isla conducted content analysis, she ended up with a count of the number of times each coding unit appeared, which is quantitative, nominal data.
To sum up, 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!