Action Research Introduction

1. Action ResearchIntroduction INFO 515 Glenn Booker Lecture #1

2. Course Scope • This class focuses on understanding common types of analysis techniques which may be used to support research projects • We will use the statistics program SPSS to manipulate data and generate graphs • There will be weekly homework assignments for much of the term Lecture #1

3. Who cares… • …about statistics and research methods? • Commonly accepted techniques need to be used to ensure that valid comparisons and analyses are being made • Statistics is a common language to express results • Helps ensure that objective conclusions are reached Lecture #1

4. Why use SPSS? • Microsoft Excel is adequate for simple math (arithmetic, averages, etc.) • But Excel fails some standard tests for performing more advanced calculations (regression analysis, etc.) • SPSS was chosen for its widespread usage and low cost student version Lecture #1

5. My Background • Eighteen years of industry experience • DOD (Department of Defense) and FAA (Federal Aviation Administration) work, primarily involved in software development, systems engineering, and project management • Also teach statistical process control for high process maturity organizations • Have been teaching for Drexel since 1998 Lecture #1

6. For the REAL serious student • Get the ISO Standards Handbook “ISO Statistical methods for quality control”, 5th ed., 2000 • It runs $418 for both 700+ page volumes • No, I don’t expect you to buy this! • If you do find someone to buy it for you, search for its title at http://global.ihs.com/ • IHS is a great, if terribly expensive, source for military (MIL, DOD), industry (IEEE, ASTM), national (ANSI, DIN*), and international (ISO) standards * DIN is the German equivalent of ANSI Lecture #1

7. Other References • More realistically, see my handout “Statistics for Software Process Improvement” • It summarizes statistical terms, hypothesis testing, SPSS tips, and other stuff we’ll be using • We’ll use it a lot Lecture #1

8. Definitions • Data - observations collected in order to measure or describe a situation or problem of interest • Data describes a variable • Variables - are objects or concepts that must have a value or a definition assigned to them in order that they can be measured and analyzed • They take on different values for individuals and groups Lecture #1

9. Discrete vs. Continuous Data • Discrete data can take on only a finite number of values. It is often characterized by counting units (integers), or only specific values, like grades • Continuous data can take on an infinite number of possible values and is characterized by some type of measurement, instrument, or scale • You measure height, weight (Does anyone ever know exactly how much they weigh?), speed, etc. Lecture #1

10. Definitions • Theory is a possible explanation of the relationships among variables • Research Hypothesis – as a consequence of our theory, the hypothesis is the statement we submit to testing • Often states there is a pattern, or difference, or trend among the variables • Null hypothesis is the opposite of the research hypothesis • States there is no trend or difference Lecture #1

11. Research • Research describes what or explains why • It is a method for finding answers to questions or a strategy for explanation • Research is: • Empirical, because it is based on evidence or data • Systematic, because it uses a method • Objective, because it is presumably conducted and interpreted by the researcher without bias Lecture #1

12. Basic vs. Applied Research • Basic research usually refers to laboratory research, such as experimental psychology • In basic research, the researcher is testing theory and ideas without necessarily applying the results to practical problems Lecture #1

13. Basic vs. Applied Research • Applied research is also called field research, evaluation research, or action research • This type of research is often used to influence policy and decision-making, and is conducted to solve problems (often immediate problems), sometimes only within one organization (hence its results are only applicable to that organization) Lecture #1

14. Quantitative vs. Qualitative • Quantitative Research tends to deal with variables that have numeric values • How far do you commute to work? • How tall are you? • Qualitative Research looks at variables which are binary (Yes/No), have non-numeric values, or are free-form text • What is your favorite football team? • How could I improve this slide? Lecture #1

15. The Nature of Qualitative and Quantitative Research Strategies: • Difference is the type of data you collect and the tools you employ • Specifically— • The same data collection strategies can be qualitative or quantitative • Qualitative data can become quantitative • Pure quantitative data cannot become qualitative • Often in research, it is good to use qualitative and quantitative in the same study Lecture #1

16. Research Methods • There are many different ways to conduct research • Exactly how many ways depends on your field of study and how you wish to define them • Here we break them into nine different methods (see narrative lecture notes too) Lecture #1

17. 1. Historical Research • Reconstruct the past to support a hypothesis or theme, while remaining objective and true to the actual events which occurred • Example: study past software projects to see if it’s true that: “if a project was at least 10% behind schedule halfway through, it will finish at least 10% late” Lecture #1

18. 2. Descriptive Research • This is a non-judgmental type of research • Examine a situation or area systematically and describe it • Example: study how library patrons navigate when looking for a particular book Lecture #1

19. 3. Developmental Research • Examine how something grows or changes over time; is also non-judgmental • Often looking for processes, patterns, or sequences • Example: study the number of software requirements which have been described during a project, and look for that number stabilizing (not changing much) Lecture #1

20. 4. Case and Field Research • Study a given organization to understand how it faces its environment • Often used for understanding business management decisions – in a given business environment, how did they choose among product development options? Lecture #1

21. 5. Correlational Research • Study how one variable is affected by one or more other variables • Example: how is customer satisfaction affected by product reliability? • Another example: how is productivity affected by the level of experience of the workers? Lecture #1

22. 6. Causal Comparative • A.k.a ex post facto (after the fact) research • Study some outcome by looking for possible causes • Example: determine if listening to classical music leads to criminal activity • Or: determine if being short increases your chance of having a heart attack Lecture #1

23. 7. True Experimental Research • Examine the effect of some treatment on an experimental group by comparing it to a control group which receives no treatment (e.g. a placebo) • Example: drug studies are done this way to prove whether the drug really had a noticeable effect on the patients Lecture #1

24. Experimental Study “Blindness” • A single blind study means the testers know which subjects receive the real treatment, but the subjects don’t know • A double blind study means neither side knows who received the real treatment – the information is coded so that only the analysts can figure out who received what • Side note: If the subjects know what they are receiving, the study isn’t blind at all Lecture #1

25. 8. Quasi-Experimental Research • This is like True Experimental Research, but is done where you can’t control all of the variables (such as the real world) • Much software development research is in this category • Much qualitative research is in this category too Lecture #1

26. 9. Action Research • Develop new ways to solve problems with direct application to the real world • This tends to focus on your own organization: study what’s happening, and see how to improve it Lecture #1

27. Action Research • A strategy in Educational Research • Enables problem solving in the natural setting • Participatory action research • Connect theory with practice Lecture #1

28. Action Research Questions in Library and Information Science • How much does the library spend? • How much do potential users actually use the library? • How productive is the library staff? • Is the staff the right size? • How are users served by the library? Lecture #1

29. Statistics • Statistics describes a likely range for predicting something, not a fixed point • For example, instead of saying it will take “a week” to perform a task, describe a time period in which you are likely to finish the task, such as 7 days +/- 2 days • Most people don’t like to think this way - uncertainty makes people uncomfortable Lecture #1

30. General Function of Statistics • Descriptive Statistics describes the characteristics of one or more variables • We describe the traits of that variable • Inferential Statistics is used when we develop a hypothesis, and analyze data to make decisions or draw conclusions about that hypothesis • We infer some larger perspective or understanding, based on our limited data Lecture #1

31. General Function of Statistics • Descriptive • Numbers that describe situation of interest • Value: efficient summary of data • Interpretive (Inferential) • More power, but certain amount of risk • Hypothesize, then collect data and analyze it • Accept or reject the hypothesis Lecture #1

32. Y X Definitions • Independent Variable - A variable which is thought to influence another variable • Often plotted as the ‘X’ axis on a graph • Might have many independent variables • Dependent Variable - A variable which is influenced by or is the consequence of the independent variable • Often plotted as the ‘Y’ axis on a graph Lecture #1

33. Independent vs. Dependent • Generally speaking, we want to be able to understand and/or predict the dependent variable in a problem • Often a hypothesis will try to use one or more independent variable(s) to explain the behavior of the dependent variable • We want to understand IQ (dep variable); try to see if income predicts it (indep variable) • To improve customer satisfaction (dep), see if a new card catalog (indep event) changes it Lecture #1

34. Cases and Variables • Cases = units of analysis • people, things, records, etc…. • A.k.a.: entities, respondents, subjects, items • Become the rows in your data matrix • Variables = things that vary! (not constant) • Example: Achievement, Intelligence, Attendance, Income, Aggression • A.k.a.: measures, attributes, features • Become the columns in your data matrix Lecture #1

35. Variables • Discrete = Counting Units • Example: Attendance • Continuous = Measurement • Example: Intelligence Tests • Independent Variables • influences other variables • Dependent Variables • influenced by (or consequence of) the independent variable. Lecture #1

36. Definitions • Population (N) is the total group of things under study, such as all voters in an election • Sample (n) is a subset of the population • Basic descriptive statistics include • Maximum is the largest value in a data set • Minimum is the smallest value in a data set • Range is the difference between the Maximum and the Minimum • Range = Maximum - Minimum Lecture #1

37. Sample & Population Variables • Notice that very often, the same variable will have a different symbol for its value for a sample, than its value for the entire population (more examples to follow) • This helps distinguish between what we have measured directly (usually the sample variable), but we want to understand or predict that variable for the whole population Lecture #1

38. Measures of Central Tendency • There are three measures of “central tendency” • Mean • Median • Mode • They convey the average, middle, and most common values in a data set Lecture #1

39. N i=1 Definitions • Mean - The average of a set of data; equal to the sum of their values (Xi), divided by the number of data points (N). Mean is X (X bar) for a sample, or m (Greek mu) for the entire population Mean = S Xi N For some set of data with N values; add them up and divide by N. To be precise, this is the arithmetic mean; there are other kinds, e.g. geometric mean. Lecture #1

40. Definitions • Median is the middle value of a set of data which has been sorted in numeric order (e.g. the median home selling price) • If the set has an even number of data points, average the middle two values • Mode is the value of data which occurs the most often (generally for integer data sets) • There can be one mode or many, resulting in different mode types Lecture #1

41. Mode Types • Unimodal - there is one mode in a data set • Bimodal – there are two modes in the data set • Multimodal - there are many (>2) modes in the data set • If there are no duplicates in the data set (all values are unique), then all its values are modes, hence it would be extremely multimodal! Lecture #1

42. Definitions • Standard deviation (s for sample, or s (sigma) for population) represents the average amount data differs from the mean • Standard deviation affects the width or flatness of the bell shaped curve • Variance (s2 or s2) is the standard deviation squared Lecture #1

43. The Normal Distribution • We’ll look at this more later on… Lecture #1

44. SPSS • SPSS is high end statistical analysis software • You can use your Drexel login to download it free from https://software.drexel.edu/ • Log in with drexel\ in front of your login name, e.g. "drexel\abc28" and the same password you use for DrexelOne. Navigate to find SPSS version 16, something like https://software.drexel.edu/Students/PCSoftware/SPSS/SPSS16/. Make sure to save the readme.txt file too - it has the serial number and Authorization Code information. Download and run the executable file. • Version 16 for Mac (~730 MB file) • Version 16 for PC (~ 670 MB files) • Anything version 10 or later is acceptable Lecture #1

45. Limits for Student Edition only SPSS Introduction • SPSS is like a spreadsheet or flat file database • Each variable has its own column (max. of 50) • Each record has its own row (max. of 1500) • Key navigational feature: • Use the Data View tab to see the experimental data • Use the Variable View tab to see the characteristics of each variable and how they’re displayed in the Data View Lecture #1

46. SPSS Data View Lecture #1

47. SPSS Variable View Lecture #1

48. SPSS Introduction • Use the Variable View tab to change the characteristics of each variable, such as • Type of variable (integer, date, text, etc.) • Name of each variable, which was limited to 8 characters, is lower case, and has no spaces • Recent versions finally removed the 8 character limit • Labels for each variable are optional, but they allow a more useful identifier than the Name • When you select or plot a variable, its Label is shown (if there is one), not its Name • Width is how many digits or characters the variable may have Lecture #1

49. SPSS Introduction • Variables can have a limited set of allowable Values, such as {0 = Male}, {1 = Female} • Sort data by selecting Data / Sort Cases… • Then select one or more variables to be the “Sort by:” criteria • If more than one variable is selected, data will be sorted in that order of precedence Lecture #1

50. SPSS Introduction • Can adjust column widths like Excel • In Data View, move cursor between column titles (which are the variable Names), and drag the column width left or right, or • In Variable View, edit the Columns field • SPSS data files have an extension of “sav” • Output is saved separately in files with an extension of “spo” • Tabular output of ***** means the column is too narrow; double click to edit, and drag the right edge of the column to the right Lecture #1