_{Up Learn – A Level Psychology (AQA) – research methods (part 2)}

_{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!**

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### More videos on Research Methods (Part 2):

^{Introduction (free trial)}

^{Themes in Qualitative Data (free trial)}

^{Thematic Analysis (free trial)}

^{Content Analysis}

^{Thematic vs Content Analysis: How Useful? (free trial)}

^{Subjectivity in Thematic Analysis (free trial)}

^{Subjectivity in Content Analysis (free trial)}

## Research Methods (Part 2)

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2. Quantitative vs Qualitative Data – Part 1

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3. Quantitative vs Qualitative Data – Part 2

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4. Frequency Tables

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5. Nominal vs Ordinal Data

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6. Ratio Data

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7. Ratio vs Interval Data

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8. Continuous vs Discrete Data – Part 1

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9. Continuous vs Discrete Data – Part 2

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2. Measuring Central Tendency

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3. Measuring Dispersion (free trial)

4. Describing a Distribution Without a Frequency Graph

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5. The Mode – Part 1

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6. The Mode – Part 2

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7. The Range – Part 1

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8. The Range – Part 2

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9. The Mean – Part 1

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10. The Mean – Part 2

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11. The Median

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12. Calculating the Median – Part 1

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13. Calculating the Median – Part 2

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14. Measures of Central Tendency and Skew

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15. Standard Deviation

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16. Calculating Standard Deviation – Part 1

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17. Calculating Standard Deviation – Part 2

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18. Comparing Measures of Central Tendency

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19. Comparing Measures of Central Tendency: Number of Values

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20. Comparing Measures of Central Tendency: Skew

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21. Comparing Measures of Central Tendency: Representativeness

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22. Comparing Measures of Central Tendency: Types of Data

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23. Comparing Measures of Dispersion: Ease of Calculation

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24. Comparing Measures of Dispersion: Representativeness

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2. What Is a Percentage?

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3. Percentages to Fractions

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4. Percentages to Decimals

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5. Decimals to Percentages

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6. Fractions to Percentages

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7. Calculating Percentages – Part 1

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8. Calculating Percentages – Part 2

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9. Percentage Change – Part 1

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10. Percentage Change – Part 2

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11. Percentage Change – Part 3

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12. Percentage Change – Part 4

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13. Pie Charts – Part 1

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14. Pie Charts – Part 2

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15. Pie Charts – Part 3

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16. Pie Charts – Part 4

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17. Pie Charts V Bar Charts

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2. What Is Probability?

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3. Numbers Instead of Words

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4. Representing Probabilities as Fractions

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5. Representing Probabilities as Percentages

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6. Comparing Probabilities

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7. Probability and Populations

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8. Probability and the Normal Distribution – Part 1

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9. Probability and the Normal Distribution – Part 2

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2. Making Inferences From Populations

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3. Sampling Error

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4. Assumption of No Difference

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5. The Null Hypothesis

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6. The t-Value

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7. Factors Affecting the Size of the t-Value – Part 1

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8. Factors Affecting the Size of the t-Value – Part 2

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9. Factors Affecting the Size of the t-Value – Part 3

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10. p-Values

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11. Accepting and Rejecting the Null Hypothesis

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12. Significance Levels

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13. Type 1 Error

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14. Type 1 Error and Significance Levels

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15. Type 2 Error

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16. Balancing Type 1 and Type 2 Errors

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17. Introduction to Using Tables to Test the Null Hypothesis

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18. Critical t-Values

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19. Using Tables of Critical Values to Test the Null Hypothesis – Part 1

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20. Using Tables of Critical Values to Test the Null Hypothesis – Part 2

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21. Degrees of Freedom

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22. The Alternative Hypothesis

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23. Directional and Non-Directional Alternative Hypotheses

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24. Using the t-Value Table for Directional and Non-Directional Hypotheses

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25. One-Tailed and Two-Tailed Tests

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26. Independent Groups vs Repeated Measures t-Test

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27. Reading the Table for Related and Unrelated t-Tests

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2. Inferential Statistics on Ordinal Data

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3. The Mann-Whitney U Test

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4. The Mann-Whitney U Test: The U Value

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5. Factors Affecting the Size of the U Value

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6. The Mann-Whitney U Test: Reading the Table

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7. The Mann-Whitney U Test: The Two Tailed Test

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8. The Mann-Whitney U Test: Joint Rankings

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9. The Wilcoxon Test

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10. The Wilcoxon Test: The t-Value

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11. The Wilcoxon Test: Reading the Table – Part 1

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12. The Wilcoxon Test: Reading the Table – Part 2

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13. The Wilcoxon Test: The Two Tailed Test

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2. Inferential Statistics on Nominal Data

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3. The Chi-Squared Test: Contingency Tables

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4. The Chi-Squared Test: The x Value – Part 1

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5. The Chi-Squared Test: The x Value – Part 2

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6. The Chi-Squared Test: Degrees of Freedom

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7. The Chi-Squared Test: Reading the Table

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8. The Chi-Squared Test: Two Tailed Test

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9. Nominal Data With a Repeated Measures Design

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2. The Sign Test

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3. The Sign Value

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4. The Sign Test: Reading the Table

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5. The Sign Test: Two Tailed Test

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6. Nominal Data and the Sign Test

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7. The Sign Test: Transforming Data Into Nominal Data

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2. Scattergrams

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3. Correlations and Hypothesis Testing – Part 1

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4. Correlations and Hypothesis Testing – Part 2

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5. Pearson’s r

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6. Pearson’s r: Reading the Table – Part 1

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7. Pearson’s r: Reading the Table – Part 2

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8. Pearson’s r: Two-Tailed Tests

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9. Correlational Studies With Ordinal Data

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10. Spearman’s Rho

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11. Spearman’s Rho: Reading the Table

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12. Spearman’s Rho: the Two Tailed Test

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13. Correlational Studies With Nominal Data

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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!