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# Brief Intro to Statistics by jennyyingdi

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• pg 1
```									                Brief Introduction to
Statistics with SPSS
Laptop Computer.
Research Consulting Center
User Name:
Domain: ASU.EDU (Kerberos Realm)

browser and go to our website   http://www.west.asu.edu/rcc/
Brief Introduction to
Statistics with SPSS
Joe Ryan,
Research Consulting Center

Rico Rivera,
Cindy Owens,
& Josh Fox,
Statistics Laboratory

http://www.west.asu.edu/rcc/
Workshop materials
   Workshop Packet titled “Introduction to SPSS 11.5”
   Data Files
–   Location
   http://www.west.asu.edu/RCC
–   Click on the Stat Lab button (located on left side)
–   Click on Workshops
–   Click on Data used for Workshops
–   Click on Workshop for David Gonzales’s Class

–   In the folder there are three files
   Tactile_Acuity.xls (excel data file)
   Strength-Injury.sav (SPSS data file)
   Vitamin_C.sav (SPSS data file)

–   Save them to your desktop
Overview of Statistic Workshop (P. 3)

   Functions of the Stat Lab & SPSS accessibility
   Overview of Research Process
   Introduction to SPSS
– Input & importing raw data
– Editing raw data
   Descriptive Statistics
– Central Tendency
– Dispersion
–   Presentation of Data: tables, figures, & scatter plot
   BREAK (10 minutes)
   Inferential Statistics
– Stating Hypotheses
– Relationships
– Paired Sample t-test
– ANOVA
Functions of Statistic Laboratory (p. 4)

   Operated by the Research Consulting Center (RCC)
   Provide access and support for statistical software
   Design to assist students enrolled in statistic
courses, research methods, biology labs, & any
courses that involve the analysis of quantitative data
   The staff assist students with aspects of statistical
software: SPSS & Excel
   Not set up to provide one-on-one tutorial service for
students on a regular basis. However, we have and
frequently do answer general statistic questions
Accessing the Statistic Laboratory
(page 5)

   Location: CLCC 107
   Phone: (602) 543-6117
   Website: http://www.west.asu.edu/RCC
   Operating hours: See website
counter
Interpretation
Overview of the Research Process                                                                    How have we answered the questions?

Data Analysis
Descriptive Statistics
Inferential Statistics

Measurement
Validity - accuracy
Reliability - precision

Observation
What do we to observe?
(What are the variables?)

How do we do the observations?
(What are the instruments and procedures?)

How many and which one’s?
(What’s the population?)
What is the question?
(How do we get the sample?)
What do we want to know about a (an)
- cell
- organism                                                                                                      Research Consulting Center
- group of organisms                                                                                                  Statistics Lab
- interactions among organisms                                                                                          CLCC 104
- relationship of an organism and its environment
www.west.asu.edu/rcc
(602) 543-6117
DISCUSSION
SECTION

Interpretation
Overview of the Research Process                                                                   How have we answered the questions?

RESULTS
(Page 6)
SECTION

Data Analysis
Descriptive Statistics
Inferential Statistics
METHODS
SECTION

Measurement
METHODS                         Validity - accuracy
SECTION                         Reliability - precision

Observation
What do we to observe?
(What are the variables?)

AT THE END OF                    How do we do the observations?
LITERATURE REVIEW                (What are the instruments and procedures?)
OR INTRODUCTION
How many and which one’s?
(What’s the population?)
What is the question?
(How do we get the sample?)
What do we want to know about a (an)
- cell
- organism                                                                                                     Research Consulting Center
- group of organisms                                                                                                 Statistics Lab
- interactions among organism                                                                                          CLCC 107
- relationship of organism and environments
www.west.asu.edu/rcc
(602) 543-6117
Procedures for Data Analysis (p. 7)

Data  StatPak  Output  Interpretation

1.   Collect & organize data
2.   Input & edit the data
3.   Analyze data or create graphs
4.   State results and interpret
Procedures for Data Analysis (p. 7)

Data  StatPak  Output  Interpretation

1.   Collect & organize data
2.   Input & edit the data
3.   Analyze data or create graphs
4.   State results and interpret
Procedures for Data Analysis (p. 7)

Data  SPSS  Output  Interpretation

1.   Collect & organize data
2.   Input & edit the data
3.   Analyze data or create graphs
4.   State results and interpret
Basic Features of SPSS (p 8)

   Statistical Product and Service Solutions (SPSS)
   Software for building and analyzing data files
   Easy to learn the basics of data entry, editing and
how to perform basic statistical analyses.
   After you learn the basics, you can explore a wide
range of techniques on your own or with the “Help”
option
   SPSS can easily import a wide variety of files
Input and edit the data

   Input the raw data
(Attitudes toward Statistics & Research)
   Import an Excel data file
(Sensory Lab Data: Tactile_Acuity.xls)
   Edit raw data in Variable View
Let’s launch SPSS

   SPSS Data Editor (p 9)
–   Data View (for inputting data) (p 10)
–   Variable View (for editing data) (p 11-13)
   Output SPSS Viewer
   SPSS Chart Editor

Using SPSS Data Editor to Input Data
(e.g., Research and Statistics Attitude Survey)

   Response
– Strongly Disagree = 1
– Disagree = 2
– Agree = 3
– Strongly Agree = 4
   Sex
– Male = 1
– Female = 2
   Age
– Under 25 = 1
– 25 – 40 = 2
– Over 40 = 3
   (Back to SPSS)
Using SPSS Data Editor to Input Data
(e.g., Research and Statistics Attitude Survey)

   On the Data View worksheet, enter the
values for each person or case.
   In your case, enter you survey, just like Josh.
   Editing Data
–   Variable names, Variable labels, and Values
Using SPSS Data Editor to Input Data
(e.g., Research and Statistics Attitude Survey)

   On the Data View worksheet, enter the
values for each person or case.
   In your case, enter you survey, just like Josh.
   Next, Josh is going to show you how to
import an excel file into SPSS.
   Minimize the SPSS window, and open the
Using SPSS Data Editor to Import an
Excel data file

   First organize the data in Excel (e.g., Tactile Acuity)
–   The first row can contain the variable names
–   Recall that variable names
   can be up to 8 characters
   & have no symbols.                        save the Tactile Acuity
–   After making adjustments to Excel,             data in SPSS
   Save the file
   Close the file
   Next you can import the Excel data into SPSS
–   (lets go back to SPSS)
–   File > Open > Data
   You get a dialogue box called ‘Open Files’
   Toward the bottom, in ‘Files of Type’, select “All Files”
   Select the file you saved (Tactile Acuity)
Review of what we did

   Inputted the raw data
(Attitude Toward Research and Statistics Data)
   Edited the raw data file in Variable View
   Imported an Excel data file
(Sensory Lab Data: Tactile_Acuity.xls)
Overview: Analyzing Data & Creating Graphs

   Level of Measurement
   Descriptive Analysis
–   Presentation of Data
–   Central Tendency & Variability
   Inferential Analysis
Level of Measurement
(4 scales of variables) (p. 18, 3rd slide)

   Ratio (e.g., lengths, weights, volumes, capacities, rates):
–Different categories
– Rankable categories
– Constant equal-sized Intervals
– Absolute Zero (physical significance) (e.g., temperature in Kelvin,
time)
 Interval (temperature on Celsius and Fahrenheit, age):
– Different categories
– Rankable categories
– Constant equal-sized Intervals (can be expressed numerically)
 Ordinal (e.g.., dominance hierarchy):
– Different Categories
– Categories are rankable
 Nominal (a.k.a. attributes)
– Different Categories (e.g.., sex, species, phylum, location)
Descriptive Statistics (p. 19, 1st slide)

   Presentation of entire distribution
–   Frequency Distribution: organized tabulation of the number (or
percentage) of individuals in each category on the scale of
measurement
–   Can be presented in a table or in a graph
   Measures of Central Tendency
–   Identifies a single score that represents an entire data set
–   Best example of average or most typical score
   Measures of Variability (Dispersion)
–   Provides a description of how spread out the scores are in a
distribution
–   Provides a measure of how accurately a single score selected
from a distribution represents the entire set
Presentation of entire distribution of
a variable

Data Type       Table             Graphs

Continuous                      Histogram or
Frequency
(Interval                   polygon (frequency
& Ratio)     Distribution
or cumulative)

Discrete
Frequency           Bar Graph
(Ordinal &
Nominal)    Distribution    (frequency or %)
Central Tendency and Dispersion
Level of      Central Tendency          Dispersion (Variability):
Measurement      of distribution           spread of distribution

Interval    Mean = the arithmetic      Variance = average squared
average of a set of scores deviation of each number from
on a variable.             its mean.
Ratio                                 Standard Deviation = the
average amount that each
score differs from the mean
(calculated as the square root
of the variance; most
commonly used)
Ordinal     Median = middle of a       Inter-quartile (semi-quartile)
distribution.              range

Nominal     Mode = most frequently     Index of dispersion
occurring score in a
distribution.
Back to SPSS (P. 20; take notes)

 Create table frequency & a chart
 Create measures of central tendency
 Create measures of dispersion
 Create scatter plot
(using Strength_Injury data)
 Editing a scatter plot by using
SPSS Chart editor
How can we summarize the
distribution of the below variable?

Gender
1.   Male
2.   Female
What is the level of
measurement?            Nominal
(page 18, 3rd slide)
Should we use a bar
graph or a histogram?   Bar Graph
(page 19, 2nd slide)
Presentation of an Nominal Variable

   Click Analyze > click Descriptive Statistics > then click
Frequencies.
   You should see a dialog box called Frequencies.
–   On the left side you should see a list of variables and on the right
you should see a Variable(s) box.
–   In the variable list (left side) look for a variable either called
“Gender” or sex.
–   Select this variable by clicking on it, then click ►to place the
variable in the Variable(s) box.
–   Make sure that there is a check mark next to Display frequency
tables.
   Click Charts.
   You will see a dialog box called Frequencies: Charts
–   Click Bar Charts. Click Continue.
   Click OK.
We want to know the central tendency
for the below variables.

   Age of respondent
18 – 89. Actual age in years

What is the level of
measurement?

Which measure of
central tendency
should we use?
Central Tendency & Variability for
Interval & Ratio Variables

   Click Analyze > click Descriptive Statistics > then click
Descriptive.
   You will see a dialog box called Descriptives.
–   Select the variable
 one is called “AGE”.
   Click Options. You will see a dialog box called
Descriptives: Options
–   By default Mean, Std Deviation, Minimum, & Maximum should
be selected
–   click Continue.
   Click OK.
Summary of what we have discussed.

   Presentation of the variable distributions

   Central tendency

   Variability (dispersion)
Creating & Editing a Scatter Plot

   Open a new data file called “Strength_Injury”
   Click on Graphs > Scatterplots
   Choose the simple panel and click on Define
–   X (horizontal) axis: Glute Strength
–   Y (vertical) axis: Abdomen & Lower Back Strength
Break

   Please be back in 10 minutes
Inferential Statistics

   Techniques that allow us to study samples
and then make generalizations about the
population from which they were selected.
–   Sample: a set of individuals selected from a
population, usually intended to represent the
population in a research study.
–   Population: The collection of all individuals
(cases) in which the researcher is interested.
Inferential Statistics

   Statistical Significance (probability)
–   Probability is used to predict what kind of samples
are likely to be obtained from a population.
–   Probability establishes a connection between
populations and samples
–   Relying on this connection, inferences of
populations can based on sample statistics
Research Question

   Actual research is conducted using a sample
   Hypothesis test: a statistical method that uses
sample data to evaluate a hypothesis about a
population parameter.
   Opposing Hypotheses stated in terms of population
parameters.
–   Null Hypothesis (e.g., no difference, no change, no effect, or
no relationship in the population from which the sample is
drawn)
–   Alternative Hypothesis (e.g., there is a difference, a change,
an effect, or a relationship for the general population).
Research Question

   Actual research is conducted using a sample
   Hypothesis test: a statistical method that uses
sample data to evaluate a hypothesis about a
population parameter.
   Opposing Hypotheses stated in terms of population
parameters.
–   Null Hypothesis (e.g., no difference, no change, no effect, or
no relationship in the population from which the sample is
drawn)
–   Alternative Hypothesis (e.g., there is a difference, a change,
an effect, or a relationship for the general population).
Research Questions & the Applicable
Statistical Procedures

   Relationships
–   Correlation
–   Regression
   Differences between 2 sample means
–   Independent samples
   Independent t-test
–   Dependent samples
   Paired-sample t-test
   Differences among 2 or more independent sample
means
–   Analysis of Variance (ANOVA)
Relationships

   Science involves a search for relationships
between variables. For example, is there is
a relationship between the amount of rainfall
and crop growth?
   Sometimes, it is reasonable to expect a
consistent, orderly relation between two
variables: As X changes, Y also changes in a
predictable way.
Linear Relationships with SPSS

   Correlation Coefficient (Strength_Injury data)
Analyze > Correlate > Bivariate …
   Regression Analysis (Strength_Injury data)
Analyze > Regression > Linear …

Regression Equation: Y = a + bx
– a = y intercept
– B = coefficient (slope)
– R square
– If p < .05 then significant
Linear Relationships with SPSS
a
Coe fficients

Unstandardiz ed         Standardized
Coef f icients         Coef f icients
Model                              B          Std. Error       Beta         t       Sig.
1       (Cons tant)                5.198          4.326                     1.201     .232
Glute Strength Measure      .755            .137            .487    5.515     .000
a. Dependent Variable: A bdomen & Low er Back Strength Measure

    Regression Equation Model
Abdomen = 5.20 + .76(Glute)
    Is the slope significant?
Is p < .05?
In SPSS, “Sig.” means p.
R square

Model Sum m ary

Model          R         R Square       R Square       the Estimate
1               .487 a       .237            .229             7.876
a. Predictors: (Constant), Glute Strength Meas ure

R Square can range between 0 to 1
In this example, we have an R Square = .24
What does a low versus a high R Square look like in a scatter
plot?
R square

22                                                  70

20

60
18

16
50
14

12
40

10

8
30

6

4                                                   20
4   6   8   10   12   14   16   18   20   22    60   62   64   66   68   70   72   74
R square

High R Square                                                             Low R Square

22                                                                               70

20

60
18
Dependent Variable (Y)

16
50
14

12
40

10

8                                                                                30

6

4                                                                                20
4   6   8    10      12   14                            16   18   20   22    60   62   64      66      68      70   72   74

Independent Variable (X)                                                    Independent Variable (X)
Two Sample t Tests with SPSS

   Independent-Samples t Test (Tactile_Acuity Data;
Sex & age)
Analyze > Compare Means > Independent-Samples T Test
…
–   t test
–   If p < .05 then significant
   Paired-Samples t Test (usually for pre & post test)
Analyze > Compare Means > Paired-Samples T Tests …
–   t test
–   If p < .05 then significant
Differences among 2 or more
independent sample means with SPSS

   Analysis of Variance (ANOVA; Vitamin Data)
Analyze > Compare Means > One-Way ANOVA …
– F test
– If p < .05 then significant
Review of Workshop
   Overview of Research Process
   Introduction to SPSS
– Input & importing raw data
– Editing raw data
   Analyzing Data & Create Graphs with SPSS
–   Descriptive Statistics
 Central Tendency
 Dispersion
   Presentation of Data: tables, figures, & scatter plot
–   Inferential Statistics
 Stating Hypotheses
 Relationships
 Two sample t-tests: Independent & Paired
 ANOVA
Thanks for having us 

   Please fill out an “SPSS Workshop Evaluation”
which is located at end of packet.