Introduction: Solving the Psychological Mystery
Imagine you are a detective trying to solve a mystery. You would look for different types of clues: fingerprints (numbers) and witness statements (descriptions). In Psychology, we do the exact same thing! When psychologists conduct research, the "clues" they collect are called data.
In this chapter, we are going to look at the different ways psychologists collect and label information. Understanding the types of data is vital because it changes how we understand human behavior and how we prove our theories are correct. Don't worry if it seems like a lot of terms at first—we will break it down step-by-step!
1. Quantitative Data: The Power of Numbers
Quantitative data is information that is in the form of numbers. It focuses on "how much," "how many," or "how often."
What does it look like?
In your 9990 core studies, you will see quantitative data everywhere! For example:
• In Milgram (obedience), the data included the maximum voltage given by participants (e.g., \( 450 \) volts).
• In Piliavin et al. (subway Samaritans), the researchers counted the number of people who helped the victim.
• In Baron-Cohen et al. (eyes test), the scores on the "Reading the Mind in the Eyes" test were recorded as numbers.
Strengths of Quantitative Data
• Easy to Analyze: Because the data is numerical, it is very easy to put into tables or graphs (like bar charts).
• Objective: It is less likely to be influenced by what the researcher thinks happened; the number is just the number.
• Comparison: It allows researchers to easily compare different groups, such as comparing the scores of males versus females.
Weaknesses of Quantitative Data
• Lacks Depth: Numbers don't tell us why a participant behaved that way. It misses out on the "human" element.
• Reductionist: It can oversimplify complex behaviors into a single score, which might not show the whole picture.
Quick Tip: Think of QUANtitative as QUANtity (amount).
2. Qualitative Data: The Power of Words
Qualitative data is descriptive information. Instead of numbers, it uses words, sentences, and descriptions of what participants said or did.
What does it look like?
• In Saavedra and Silverman (button phobia), the researchers recorded how the boy described his feelings about buttons and how his distress changed during therapy.
• In Milgram, the researchers recorded the comments made by participants, such as "I can't go on" or descriptions of them "digging their fingernails into their flesh."
Strengths of Qualitative Data
• Rich Detail: It provides a deep, "rich" insight into why people feel or act the way they do.
• High Validity: Because it allows participants to explain themselves in their own words, the data is often more "true to life."
Weaknesses of Qualitative Data
• Hard to Analyze: It is very difficult to turn a long interview into a graph! It takes a lot of time to summarize.
• Subjective: The researcher might interpret the participant's words based on their own personal opinion (researcher bias).
Quick Tip: Think of QUALitative as QUALity (description).
Key Takeaway:
Most good psychological research (like many of your core studies) uses both types of data to get the best of both worlds!
3. Objective vs. Subjective Data
Psychologists also categorize data based on how much "personal opinion" is involved in collecting it.
Objective Data
Objective data is fact-based and not influenced by personal feelings or interpretations. It is the same no matter who is looking at it.
Example: In Dement and Kleitman, the researchers used an EEG machine to measure brain waves. The machine provides a clear, factual reading that isn't up for debate.
Subjective Data
Subjective data is based on personal perspective, feelings, or opinions. This data can vary depending on who is providing it or who is interpreting it.
Example: In Saavedra and Silverman, the boy's "disgust ratings" were subjective because they were based on his own personal feelings about buttons.
Did you know? Data collected through self-reports (like questionnaires and interviews) is almost always subjective because it relies on the participant's own view of themselves.
4. Common Mistakes to Avoid
Mistake 1: Thinking Quantitative is always "better."
While numbers are great for science, they can be "narrow." Without qualitative data, we wouldn't know the thoughts behind the actions!
Mistake 2: Mixing up "Method" and "Data."
An observation is a method (how you get the info). The tally chart you make during that observation is quantitative data. The notes you write about the participant's facial expressions are qualitative data.
5. Quick Review: Which is which?
To test yourself, look at these examples and decide if they are Quantitative or Qualitative:
1. A score of \( 25/30 \) on a memory test. (Answer: Quantitative)
2. A participant saying, "I felt very nervous when the lights went out." (Answer: Qualitative)
3. The number of seconds it took for a participant to help someone. (Answer: Quantitative)
4. A written description of a child's play behavior. (Answer: Qualitative)
For more information on how we display this data, see the chapter on "Data analysis, tables and graphs." For more on the tools used to collect this data, check out "Self-reports" and "Observations."