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HDFS 2320 - Research Methods in Human Development and Family Studies

Learn how to locate and evaluate research across the human lifespan with an emphasis on families

Understanding Data

Data is defined as facts or information that can be used for reporting, calculations, planning, or analysis. Data can be analyzed and interpreted using statistical procedures to answer “why” or “how.” Data is used to create new information and knowledge, and has the following characteristics:

  • "Disaggregated" collection of observations with one or more characteristics
  • Generally requires manipulation or extraction using utilities
  • Can be values or observations of characteristics

Statistics are the results of data analysis. They usually come in the form of a table or chart. They are defined as

  • "Aggregated" and tabulated or cross-tabulated tallies based on data
  • Counts, tallies, totals, "averages"
  • Can be found in print or electronically
  • Normally used as they are presented or retrieved
  • "Facts numerically represented"

Qualitative data describes qualities or characteristics of something. It is non-numerical and often collected through interviews, participant observation, and focus groups. It can be subjective and typically describes a perception or point of view. It is particularly useful for gaining cultural insight into social contexts and beliefs of a particular population. Qualitative data can take the form of field notes, audio, transcripts, and video.

Common qualitative collection methods are:

  • Individual interviews
  • Focus groups
  • Observations (e.g. watching how people interact with a website)
  • Open-ended questions on surveys

Quantitative data attempts to quantify an answer to a question(s). It is numerical and often collected through measurements, surveys, observations. Quantitative data is statistically analyzed usually in programs such as Excel, R, SPSS, STATA, and more.

Common quantitative collection methods are: 

  • Experiments 
  • Systematic observations (e.g. using a thermometer to measure temperature each day)
  • Number-based questions on surveys (e.g. how many tacos did you eat last night?)
  • Number-based questions in Interviews 

Here is a list of basic terminologies in statistics.

  • Independent variable: It is a variable that stands alone and isn't changed by the other variables you are trying to measure. For example, someone's age might be an independent variable.
  • Dependent variable:It is something that depends on other factors. For example, a test score could be a dependent variable because it could change depending on several factors such as how much you studied, or how much sleep you got the night before you took the test.
  • percentage:A number or ratio expressed as a fraction of 100. It is often denoted using the percent sign, "%".
  • Rate: The ratio between two related quantities in different units.
  • Mode:A set of data values is the value that appears most often.
  • Median:A value separating the higher half from the lower half of a data sample, a population or a probability distribution.
  • Mean:The average of the numbers. 
  • Margin of error: A statistic expressing the amount of random sampling error in the results of a survey. The larger the margin of error, the less confidence one should have that a poll result would reflect the result of a survey of the entire population. 
  • Significance:The ability to say that a reported result would only happen x% of the time by chance.

For more information, read this chapter on introduction to statistical literacy or watch the video below.

A correlation describes the connection between two variables. It represents the strength and direction of the relationship between them. Correlations can be either positive or negative. A positive correlation means that higher values on the first variable are related to higher values on the second. A positive correlation also means that lower values on the first are related to lower values on the second. A negative correlation means that as one variable increases, the other variable decreases. 

A correlation or association between two variables does not mean they are causally related. Divorce rates in Maine correlates with per capita consumption of margarine does not mean the consumption of margarine causes divorce. If you want to explore more random correlations, look at this website on spurious-correlations.

You can also watch this video on correlation does not imply causation.