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Action Research Introduction

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Title: 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
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