action research introduction
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Action Research Introduction. INFO 515 Glenn Booker. 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 - PowerPoint PPT PresentationTRANSCRIPT
INFO 515 Lecture #1 1
Action ResearchIntroduction
INFO 515Glenn Booker
INFO 515 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
INFO 515 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
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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
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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
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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
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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
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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
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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.
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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
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Research Research describes what or explains why
It is a method for finding answers to questions or a strategy for explanation
Research is:1. Empirical, because it is based on evidence
or data2. Systematic, because it uses a method3. Objective, because it is presumably
conducted and interpreted by the researcher without bias
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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
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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)
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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?
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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
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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)
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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”
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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
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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)
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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?
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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?
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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
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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
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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
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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
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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
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Action Research A strategy in Educational Research Enables problem solving in the natural
setting Participatory action research Connect theory with practice
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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?
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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
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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
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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
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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
X
Y
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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
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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
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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.
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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
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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
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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
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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 (Greek mu) for the entire population
Mean = Xi
N
Definitions
i=1
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.
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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
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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!
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Definitions Standard deviation (s for sample, or
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 2) is the standard
deviation squared
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The Normal Distribution We’ll look at this more later on…
Normal Distribution for mean = 0, and std dev = 1/2, 1 and 2
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
-8 -6 -4 -2 0 2 4 6 8
X
PD
F
PDF (std dev=1)
PDF (std dev=2)
PDF (std dev=1/2)
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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
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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
Limits for Student
Edition only
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SPSS Data View
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SPSS Variable View
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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
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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
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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
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Additional References 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.
From Prof. Val Yonker
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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.
Here’s my favorite:Salkind, Neil J., (2007) Statistics For People Who (Think They) Hate Statistics (3rd ed.). Thousand Oaks, CA: Sage Publications. ISBN: 9781412951500