Quantitative Research

1. Quantitative Research Hypothesizing, counting, and reporting

2. Quantitative Research • Numbers-based – Quantitative research refers to the manipulation of numbers to make claims, provide evidence, describe phenomena, determine relationships, or determine causation. • Deductive – usually tests a hypothesis based on previous research. Numbers are important to determine when a hypothesis has been confirmed or not. You are looking FOR something. • Generalizable – through statistical or mathematical modeling, can make predictions about future events.

3. Quantitative research • Quantitative research often starts with an expectation about what you are going to find and then tests that expectation. • Follows a scientific method: • Define the question • Gather information and resources • Form hypothesis • Design experiment • Perform experiment and collect data • Analyze data • Interpret data and draw conclusions that serve as a starting point for new hypotheses • Publish results

4. Research Plan • As we follow this scientific method, recognize that it really is just a research plan, but in a more focused manner. You would still benefit much from working out the following BEFORE you conduct your study • Research Question • Method • Plan • Timeline

5. Define the Question • Defining the question, often called your research question, determines the scope of what you are able to research. A good research question should be (FINER): • Feasible – is it a realistic question to ask? • Interesting – will we learn something from it? • Novel – have very few people done it? • Ethical – does it respect the participants? • Relevant – will we be able to do something with the findings? Hulley S, Cummings S. (Eds ) Designing Clinical Research. Willimas & Wilkins: Baltimore, 1988

6. Defining the Question • To create a Quantitative Research Question • Define your participants • Define your issue • Define the variables of that issue • Ask a question of the participants, issue, and variable • Do DU students have part-time jobs that they enjoy? • Are college major and character class in World of Warcraft players correlated?

7. Gather Information and Resources • Text-Based Research is useful in helping you define your expectation (hypothesis). You want to find what has come before in the topic or related topics. You will rarely find your exact study (if you do, then your research question isn’t novel). You are looking for elements, pieces of your topic that have come before. • Previous studies have determined that experienced female MMORPG players have a primary motivation to play for social reasons (Yee, 2007). Other studies have shown no gender differences when looking at more than a single primary motivation (Tychsen, Hitchens, & Brolund, 2008). In these studies, the intention was to look at motivation in experienced RPG/MMORPG players—in my current study, I intend to look at motivation to play MMORPG World of Warcraft by non-experienced gamers.

8. Gather Information and Resources • Your experiences as well as those experiences of your friends can also be useful in helping you gather information and resources. • I have spent many hours in arenas with a rating of 2266 in 3v3. I have noticed in the past that my team tends to win more before noon server time and after 10 PM server time. Assuming Blizzard’s matchmaking for 3v3 arena matches remains consistent, it seems that the level of player is better during the afternoon and evening than it is at other times.

9. Form Hypothesis • What is your best guess as to the outcome of your Research Question • The hypothesis is based on your Research Question, but it is not phrased as a question – it is phrased as your best guess as to the outcome of that question • DU students do not enjoy their part-time work • College major and character class in World of Warcraft are not related. • You might create sub-hypotheses to account for other variables that you might consider relevant (e.g. gender, class-standing). You tag these on to the end of your initial hypothesis. • Seniors tend to enjoy their part-time work more than first-year students enjoy their work. • Female players tend to pick primarily caster classes whereas male students tend to pick non-caster classes.

10. Design Experiment • Determine what best would address the research question you are asking. In quantitative studies, a survey works the best because you can control responses. • Determine triangulation questions or observations for two reasons: • You don’t want it to be obvious what you are asking • Other variables may be affecting the outcome. • Refer to Chapter 8 in Situating Research (or the handout Conducting Surveys) when coming up with your Survey Questions • It’s a good idea to playtest your survey with one or more people so that they can give you feedback about what questions might be confusing.

11. Design Experiment • Likert-type scale (1-5) will allow you to “quantify” human beliefs, attitudes, and experiences. Likert-type scales are used often in social science, descriptive studies. • Usually ask positive questions, and then follow with whether the person agrees or disagrees. • Usually scaled so that higher numbers are positive/agreement, lower numbers negative/disagreement “A good writing class should consist of lectures on grammar” 1-strongly disagree, 2-disagree, 3-agree, 4-strongly agree “How satisfied are you with University of Denver’s dorms” 1-very unsatisfied, 2-unsatisfied, 3-satisfied, 4-very satisfied

12. Design Experiment • Planning your survey, your hypothesis, your plan is vital BEFORE you conduct your survey because you only get one shot at the survey.

13. The Final Four • Perform Experiment – remember to be professional, take notes (you never know what might effect your results), and ethical. • Analyze Data – Keep track of your data, put it in a spreadsheet, and work the numbers. We will be talking a bit about statistics here, but really, all you will be expected to do is descriptive analysis • Interpret Data and Draw Conclusions • Publish Results (see IMRAD PowerPoint)

14. Quantitative Research Ideas and Controversy

15. Quantitative Research • Experimental – testing whether a “thing” (independent variable) applied to a subject/group has an effect (dependant variable) • Two equal groups, one control, one experimental • Pre-test, post-test • Quasi-experimental – testing whether a “thing” applied to a subject/group has an effect but without being able to • Actively apply the “thing” • Control for other variables • Descriptive – testing whether an effect is apparent or not • Inferential – testing whether two or more sets of already determined data are related or not; testing whether two or more sets of data can provide new data.

16. Quantitative Controversy • Some scholars believe that human experience, attitudes and beliefs cannot be quantified. • A person’s “feelings” can change from day to day with little conscious thought of the fact • Some scholars believe that quantitative research is trusted more than it should be • Bridges still collapse, spaceships still get lost, new cars still break down, pharmaceutical companies still produce harmful drugs, computers still crash • Some scholars believe that quantitative research is reductive • Statistically speaking, your SAT has already predicted what your final college GPA is going to be. Will you only ever be 1400 or 3.6 smart? • Some scholars believe that quantitative research misses important nuances • In a ChangeWave survey, the iPhone had the highest customer satisfaction with 79% of the sample “very satisfied.”

17. Very Short Guide to Stats for SGR Basics of aggregate and statistical data

18. Inferential v. Descriptive • Descriptive statistics “describe” the data of a sample or population. They are usually aggregate data • Average (Mean) GPA • Standard Deviation of SAT score • Inferential statistics “infer” (i.e. conclude) relationships between a sample AND a population, or “infer” past, present or future results of a sample/population based on its data. • Regression/correlation analysis of GPA and SAT (relationship between SAT and GPA, and SAT can be used to predict GPA)

19. N = number of participations • In inferential statistics, you would refer to the number of participants in your survey as N. If it is a sample or part of a whole, it is n (lowercase), and if it is a total population, it is N (uppercase). • Population: N = 4,432 • Sample: n = 100 • In descriptive studies and descriptive statistics, it is common to refer to participants as N, subgroups of those participants as n • Of the total students surveyed (N = 100), only 10% (n = 10) were male.

20. Measures of central tendency • Central Tendency measures common “middles” • Mean is the arithmetic average of items or values • Mode is the most occurring item or value • Median is the item or value of which 50% are greater and 50% are less. • Sometimes GPA or time can be used as a measure, but another measure is one of attitudes and beliefs using a Likert-type scale. • Standard Deviation is a measure of the spread of items or values in a series. Understanding the variation can help you see how close a particular item or value is to other numbers. • Distribution (Histogram) is a visual representation of the number of a particular result in an array of numbers. In this series (number of hours I played WoW over break):8, 0, 0, 3, 2, 10, 0 • Mean = 3.29, Mode = 0, Median = 2, SD = 4.11 In this series (number of hours I worked this week):8, 8, 8, 8, 6, 6, 5 • Mean = 7, Mode = 8, Median = 8, SD = 1.29

21. Using Excel to do your stats • Mean { =average(range) } • You can compute mode { =mode(range) } or median {=median(range) }, but they might not be as useful in this project. • Standard Deviation { =stdev(range) } • You can also count the number of instances of a value including instances of text: { =countif(range,”value”) } • The following example would count every instance of “male” in the range: =countif(A2:A7,”male”) • You can create frequency distribution histograms by using Tools -> Data Analysis, then Historgram. Histograms count the number of instances of a result in a given array. You can also find these commands by using Insert -> Function. There are also far more complex inferential statistics available in Excel • You can do a complete Descriptive Stats Summary by selecting Tools > Data Analysis (If you don’t see a Data Analysis, then (Excel 2003) Tools > Add-ins > Analysis ToolPak; (Excel 2007) Excel Options > Add-ins > Manage Add-ins > Analysis ToolPak

22. Writing Stats in APA • Standard Deviation = SD • Mean = M • Descriptive statistics are often written in parentheses after an item that the statistic refers to, and symbols and numbers should be separated by a space • In a survey of DU students, participants (N = 100) responded that money was more important (M = 4.2, SD = .9) than experience (M = 3.5, SD = .76) in selecting a summer job. • In a survey of computer game addicts, females (n = 15) were more likely to be depressed during withdrawal (M = 5.2, SD = .45) than males were (n = 78, M = 3.2, SD = .98) • Chapter 8 in Situating Research has more about this.

23. Charts and Graphs It’s important in doing graphs that you compute an aggregate (sum, average, SD, something) before graphing information. You cannot just Select All of data and make a graph out of it. • Pie graphs – good for showing distributions of a total population (you will have to compute aggregates first) • Line graphs – good for showing time-based, linear progression • Column/Bar graphs – good for showing distribution of individual responses (you will have to create aggregates first) • Y-Axis (vertical) for variables, X-Axis (horizontal) for participants.