Action Research Introduction

1
Action ResearchIntroduction

• INFO 515
• Glenn Booker

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

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

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

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

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 dont 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
7
Other References

• More realistically, see my handout Statistics
  for Software Process Improvement
• It summarizes statistical terms, hypothesis
  testing, SPSS tips, and other stuff well be
  using
• Well use it a lot

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

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.

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

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

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

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)

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?

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

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)

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
  its true that if a project was at least 10
  behind schedule halfway through, it will finish
  at least 10 late

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

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)

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?

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?

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

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

24
Experimental Study Blindness

• A single blind study means the testers know which
  subjects receive the real treatment, but the
  subjects dont 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 isnt blind at all

25
8. Quasi-Experimental Research

• This is like True Experimental Research, but is
  done where you cant 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

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 whats happening, and see how to improve it

27
Action Research

• A strategy in Educational Research
• Enables problem solving in the natural setting
• Participatory action research
• Connect theory with practice

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?

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 dont like to think this way -
  uncertainty makes people uncomfortable

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

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

32
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

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

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

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.

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

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

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

39
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.
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

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 (gt2) 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!

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

43
The Normal Distribution

• Well look at this more later on

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/Student
  s/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

45
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 theyre
  displayed in the Data View

46
SPSS Data View
47
SPSS Variable View
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

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

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

51
Additional References
From Prof. Val Yonker

• Carpenter, R.L., and Vasu, E.S. (1979).
  Statistical Methods for Librarians. Chicago
  American Library Association.
• Cohen, J. and Cohen, P. (1975). Applied Multiply
  Regression/Correlation Analysis for the
  Behavioral Sciences. Hillsdale, NJ Lawrence
  Erlbaum Assoc.
• Hernon, P. (1989). A Handbook of Statistics for
  Library Decision Making. Norwood, NJ Ablex
  Publishing.
• Isaac, S. and Michael, W.B. (1977). Handbook in
  Research and Evaluation. San Diego Edits
  Publishers.
• Keppel, G. (1973). Design and Analysis A
  Researcher's Handbook. Englewood Cliffs, NJ
  Prentice-Hall.
• Kerlinger, F.N. (1979). Behavioral Research A
  Conceptual Approach. New York Holt, Rinehart,
  and Winston.

52
Additional References

• Loether, H.J. and McTavish, D.G. (1980).
  Descriptive and Inferential Statistics An
  Introduction. Boston Allyn and Bacon.
• Runyon, R.P., and Haber, A. (1984). Fundamentals
  of Behavioral Statistics (2nd ed.). Reading, MA
  Addison-Wesley.
• Selltiz, C. Wrightsman, L.S. and Cook, S.W.
  (1976). Research Methods in Social Relations (3rd
  ed.). New York Holt, Rinehart and Winston.
• Heres my favoriteSalkind, Neil J., (2007)
  Statistics For People Who (Think They) Hate
  Statistics (3rd ed.). Thousand Oaks, CA Sage
  Publications. ISBN 9781412951500