1. RESEARCH & DATA (Part 1)

2. Purpose of this Section of the Research Support Lab
Purpose of This RSL Part:
Make statistics fun!
Make you into a statistician!
Introduce you to basic concepts and procedures in descriptive and inferential statistics
Prepare you for subsequent statistical courses
Overview of These RSL Parts:
Begins with methods for describing and summarizing single-variable (frequency) distributions followed by methods for describing relationships between two (or more) variables.
Then introduce probability theory as background for understanding inferential statistics.
Methods are then presented for drawing inferences from research samples to populations from which the samples were drawn.
Statistical tests covered include z-tests, t-tests, analysis of variance
(F-tests), and nonparametric tests

3. Textbook Credits Textbook
Shavelson, R.J. (1996). Statistical reasoning for the behavioral sciences (3rd Ed.). Boston: Allyn & Bacon.
Supplemental Material
Ruiz-Primo, M.A., Mitchell, M., & Shavelson, R.J. (1996). Student guide for Shavelson statistical reasoning for the behavioral sciences (3rd Ed.). Boston: Allyn & Bacon.

4. Statistical Software
Excel
MegaStat
Minitab
SPSS
JMP
POM/QM
StatCrunch

5. Research Defined
Research is doing one’s damnedest to answer perplexing questions…
Or research is a systematic approach to finding answers to questions
Scientific research, our focus, seeks answers to questions empirically and by inference, ruling out counter-interpretations to the one justified by the data
With the scientific method, problems are formulated, hypotheses are identified, data are collected, inferences are drawn about which hypothesis is more credible
The purpose of empirical research, therefore, is to provide answers to questions about behavior using the scientific method

6. Statistics Defined
Statistics is the science of conducting studies to collect, organize, summarize, analyze, and draw conclusions from data.
Descriptive statistics consists of:
the collection
Organization
Summarization
presentation of data
Inferential statistics consists of:
generalizing from samples to populations
performing estimations
hypothesis testing
determining relationships among variables
making predictions

7. Research Questions/Steps in Conducting Research
What is happening?
Is there a systematic (causal) effect?
Why or how is it happening (“mechanism”)?
Steps in Conducting Research
Identify and define a research problem
Formulate hypothesis based on theory, research, or both
Design the research
Conduct the research
Analyze the data
Interpret the data as they bear on the research question

8. Data Collection and Sampling Techniques
Surveys are the most common method of collecting data. Three methods of surveying are:
Telephone surveys
Mailed questionnaire surveys
Personal interviews
Other methods include historical data gathering (empirical data)

9. Some Terminology
Variable: is a characteristic or attribute that can assume different values(height, ability)
Data are the values that variables can assume.
Random variables have values that are determined by chance.
A population consists of all subjects that are being studied.
A sample is a group of subjects selected from a population.
Random samples are selected using chance methods or random methods.
Independent Variable(Factor/Treatment): A variable that is measured , manipulated (type of instruction), or selected (e.g., sex) to determine its relationship to some other observed variable.
Control Variable: A variable which is held constant (or is “controlled”) to neutralize its effect on the dependent variable because it is not the focus of the study (e.g., control on sex in a reading study)
Intervening Variable: A conceptual or theoretical variable that accounts for the relation between independent and dependent variable; an explanation for the relation or a hypothesized mechanism that accounts for the relation.
Dependent Variable(Response): A variable that is observed and measured to determine its response to the independent variable (i.e., dependent on the independent variable)

10. Measurement Scales
Nominal—classifies data into mutually exclusive (non-overlapping), exhausting categories in which no order or ranking can be imposed on the data.
Ordinal—classifies data into categories that can be ranked; however, precise differences between the ranks do not exist.
Interval—ranks data, and precise differences between units of measure do exist; however, there is no meaningful zero.
Ratio—possesses all the characteristics of interval measurement, and there exists a true zero.

11. Measurement Scales: Classification of Data

12. Some Terminology: Summation Notation
Summation notation is mathematical notation commonly
used in statistics
It’s really simple if you pause, take a deep breath, relax and
enjoy it… a little patience goes a long ways

13. RESEARCH DESIGNS & THREADS TO THEIR VALIDITY

14. Research Designs Pre-experimental Designs Experimental Designs
One-shot Case Study (Treatment group only)
One Group Pretest to Posttest Design—measures of change
Intact Group Comparison at posttest
Experimental Designs
Random assignment to “treatment” & control group
Posttest Only Control Group
Pretest-Posttest Control Group
Factorial
Quasi-experimental Designs
Non-random assignment to “treatment” & control group observed
Nonequivalent-Control Group Design
Time-Series Design
Ex-Post Facto Designs
Statistical controls for comparing alternative “treatments”
Correlational Design
Criterion-Group Design

15. Pre-experimental Designs
One-shot Case Study (Treatment group only)
Example:
“X” is a new personnel policy, a job satisfaction measurement is taken, and then a response is observed
One Group Pretest to Posttest Design—measures change
A job satisfaction measurement is taken before and after treatment “X” is applied
Intact Group Comparison at posttest
G1 receives the treatment, G2 does not; then a job satisfaction measurement is taken and observed
(in this case G1 and G2 may represent two different business units) O X X O2 O1 X
Control O G1 G2

16. Random assignment to “treatment” & control group
Experimental Designs
Random assignment to “treatment” & control group
Posttest Only Control Group • Pretest-Posttest Control Group
Factorial X
Control O X
Control O2 O1 X2 X1 O
Control
Example:
A job satisfaction measurement is taken after treatment “X1” is applied or not and graveyard shift “X2” is implemented

17. Quasi-experimental Designs
Non-random assignment to “treatment” & control group observed.
Include one or more control groups.
Nonequivalent-Control Group Design
Subjects receive a pretest (O1) treatment or non-treatment and then receive a posttest (O2)
Time-Series Design
Multiple observations are taken before and after a treatment is administered. Pretreatment observations establish a control group baseline. Post-treatment observations establish a consistent change in response. X
Control O2 G1 G2 O1 X O2 O1 …

18. Ex-Post Facto Designs
Statistical controls for comparing “treatment” and “control” (relationships between two variables). Called ex-facto because the researcher arrives after the treatment has been administered.
Correlational Design
SAT scores (O1) and GPA (O2) are collected.
Criterion-Group Design
Group 2 is compared to Group 1 O1 O2 O G1 G2

19. Threats to Internal Validity
History:
- something co-occurring with the treatment caused the outcome
Maturation
- maturation, not the treatment, caused the outcome
“Mortality”
- loss of poorly performing subjects from a group caused the outcome
Statistical Regression
- extreme groups are likely to improve on retesting
Selection bias
- the differences in outcomes existed before the treatments were given
Instrumentation
- outcome measure not reliable, valid, or both
Testing
- pretest cued subjects to outcome measure
Stability
- Type I Error

20. History Threat
Occurrence of events other than the independent variable.
Treatment (X)
Control

21. Maturity Threat
Treatment (X)
There may be developmental (physical or mental) changes occurring to the subjects during the time of the experiment

22. Mortality Threat Treatment (X)
Some subjects drop out the study and they have something in common, say, low achievement.

23. Regression Threat Treatment (X)
The groups were selected on the basis of extreme score. (Regression effect: low-extreme tends to increase, high-extreme tends to drop)

24. Selection Threat Treatment (X) Initial difference exist in groups
Control

25. Instrumentation Threat
Treatment (X)
The measuring instruments is not reliable or not valid, therefore, the score obtained by subjects could not be accurate. ?

26. Testing Threat Pretest Treatment (X)
The subject learns from the pretest, therefore, scores better on the posttest
Pretest

27. Testing Threat Type I Error Type II Error Correct Decision H0 True
H0 False
Type I Error
producers risk (a)
Type II Error
consumers risk
(b)
Correct Decision
Reject H0
Do not reject H0
A type I error occurs if one rejects the null hypothesis when it is true.
A type II error occurs if one does not reject the null hypothesis when it is false.

28. Ideal Model  Experimental Design (Control Group + Random Assignment)
Treatment (X)
Control
Randomly Assigned

29. Practice Exercises Select two out of the four major Research Designs.
Support your two selected research designs with original hypothetical examples as outlined in this presentation.
Compare and contrast them with one another.
Indicate all threads to validity that you can document.