info 515lecture #11 action research introduction info 515 glenn booker

INFO 515 Lecture #1 1

Action ResearchIntroduction

INFO 515Glenn 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

There will be weekly homework assignments for much of the term


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


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


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


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


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


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


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.


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


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


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


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)


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?


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


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)


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”


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


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)


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?


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?


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


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


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


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


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


Action Research A strategy in Educational Research Enables problem solving in the natural

setting Participatory action research Connect theory with practice


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?


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


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


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


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


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


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


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.


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


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


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


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.


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


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!


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


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)


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


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


SPSS Data View


SPSS Variable View


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


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


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


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


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

info 515lecture #11 action research introduction info 515 glenn booker

Documents

term slide

lot slide

continuous data discrete

glenn booker slide

use spss

variables research hypothesis

statistical process

grades continuous data