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Lecture 20

MK 370 Lecture 20: SPSS

7 Pages
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Department
Marketing
Course Code
MK 370
Professor
Chowdhury

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SPSS – 9/25/14
Every line represents a person.
Every column represents a variable (aka a question).
String = both alphabets and numbers.
oFine for names.
Numeric = only numbers.
oFine for age.
Label = the question asked.
Data Coding: assigning numbers to responses.
oFor Gender: male is 1, female is 2
oWrite explanation in values.
SPSS – 10/2/14
Data Cleaning: making sure there aren’t any errors in data before you analyze.
oOut of range
EX: 3.5 on a 1-7 scale.
EX: 3 on age when there’s only 2 variables.
Delete the whole individual (row) from the dataset.
oFINDING OUTLIERS (mean – 3sd, mean + 3sd)
Analyze  Descriptive Stats Descriptive
Not for gender and age or demographics
Only for scalable variables
If we have an outlier, delete the individual from the dataset.
oMissing Value: blank
Do not delete the individual from the dataset.
Replace the missing value with the mean.
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REPLACING MISSING VALUES:
Transform  Replace  Missing Value
Get rid of the old variable column.
oNORMAL CURVE: done for scaled variables only.
Analyze  descriptive stats frequencies
Charts  Histograms show normal curves
SPSS – 10/16/14
1. What’s the final sample size?
2. What’s the final mean for performance?
3. What percentage of the sample is male? Female?
FINDING MEANS:
oAnalyze  Descriptive Stats  Descriptives
FINDING PERCENTS:
oAnalyze  Descriptive Stats  Frequencies
4. Frequency distribution for class, MBA program
5. Sort performance in ascending order.
SORTING THE DATA:
oData  Sort Cases  Pick Ascending or Descending  Okay
6. Recode gender into: 0-male; 1-female.
RECODING THE DATA:
oTransform  Recode into the Same Variable  Old and New Values  Add 
Still Missing Remains Still Missing  Okay
oTransform  Recode into Different Variables  Name the Variables  Old and
New Variables  Range and New Values
7. Split individuals into two groups
a. Who has a higher performance mean, males or females?
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Description
SPSS – 9/25/14 • Every line represents a person. • Every column represents a variable (aka a question). • String = both alphabets and numbers. o Fine for names. • Numeric = only numbers. o Fine for age. • Label = the question asked. • Data Coding: assigning numbers to responses. o For Gender: male is 1, female is 2 o Write explanation in values. SPSS – 10/2/14 • Data Cleaning: making sure there aren’t any errors in data before you analyze. o Out of range ▪ EX: 3.5 on a 1-7 scale. ▪ EX: 3 on age when there’s only 2 variables. ▪ Delete the whole individual (row) from the dataset. o FINDING OUTLIERS (mean – 3sd, mean + 3sd) ▪ Analyze  Descriptive Stats Descriptive ▪ Not for gender and age or demographics ▪ Only for scalable variables ▪ If we have an outlier, delete the individual from the dataset. o Missing Value: blank ▪ Do not delete the individual from the dataset. ▪ Replace the missing value with the mean. ▪ REPLACING MISSING VALUES: • Transform  Replace  Missing Value • Get rid of the old variable column. o NORMAL CURVE: done for scaled variables only. ▪ Analyze  descriptive stats frequencies ▪ Charts  Histograms show normal curves SPSS – 10/16/14 1. What’s the final sample size? 2. What’s the final mean for performance? 3. What percentage of the sample is male? Female? • FINDING MEANS: o Analyze  Descriptive Stats  Descriptives • FINDING PERCENTS: o Analyze  Descriptive Stats  Frequencies 4. Frequency distribution for class, MBA program 5. Sort performance in ascending order. • SORTING THE DATA: o Data  Sort Cases  Pick Ascending or Descending  Okay 6. Recode gender into: 0-male; 1-female. • RECODING THE DATA: o Transform  Recode into the Same Variable  Old and New Values  Add  Still Missing Remains Still Missing  Okay o Transform  Recode into Different Variables  Name the Variables  Old and New Variables  Range and New Values 7. Split individuals into two groups a. Who has a higher performance mean, males or females? • SPLIT FILE: o Data  Split File  Organize Output by Groups  Groups Based On … Choose Variable … Define Groups  Okay o To analyze the variable per group: Analyze  Descriptives  Okay o Get the original file back: Data  Split File  Analyze All Cases  Okay 8. Insert Variables • INSERT VARIABLES: o Click a variable to insert the specified one above it  Edit  Insert Variable  Name the Variable  Data View  Add Data SPSS – 10/23/14 We’ll be tested on… • Data Cleaning • Descriptives • Testing Hypotheses o T-Test o ANOVA o Regression What Tests do we Use for Different Types of Hypotheses? • Group Differences: comparing groups. o T-TEST! o EX: what are appropriate age groups for different types of restaurants? o EX: what is the difference between business professionals from MBA program and from BS program? • IV leads to DV: prove an experiment (cause-effect) o REGRESSION! When IV (cause) is a scaled variable. o ANOVA! When IV is a not scaled - categorical variable or a treatment o EX: lack of brand awareness led to decline in Blackberry sales. T-Tests EX: Are males more satisfied than females with the steakhouse restaurant? • Split the data by “familiar restaurant” and then by “gender.” o Data  Split File  Organize Outputs by Groups; Groups Based On  Gender • Run a frequency on “satisfaction” and check the “mean” box in “statistics.” Output shows satisfaction means and frequencies for males and females, for each restaurant separately. o Analyze  Descriptive Statistics  Descriptives  Satisfaction • Unsplit the data. • We have to prove that the difference in means is statistically significant. o Use T-Statistic. • Independent Sample T-Test (compare two groups that are independent, not overlapping). o Split the data, run a frequency, unsplit the data??? o Analyze  Compare Means  Independent Sample T-Test  ▪ Grouping Variable (two groups you’re comparing): gender. • Define groups (use specified values -> enter coding for each group). ▪ Test Variable (what you are comparing among the two groups): satisfaction. o Read the first line of the independent sample t-test: equal variances assumed. ▪ The t-value is 3.197. ▪ We want to be at least 95% confident of the results. ▪ *If the absolute value of “t” is greater than or equal to 1.96, the groups are statistically different in terms of the test variable.* 10/30/14 – T-Test H1: the pepsi attitude mean is 2.1. • One-sample t-test (is a predicted value correct or incorrect?) o Analyze -> compare means -> one-sample t-test o Test value – predicted value o Test variable – the subject you’re predicting (attitude) o One sample statistics chart shows the actual mean of the data. o The one sample test chart shows the t-statistic, which is 19.97 in this case (and because the absolute value of t is greater than or equal to 1.96, there is indeed a significant statistical difference. o REJECT H1. ▪ I ran a one-sample t-test, and my t-value is 19.97. Because absolute value of the t-value is greater than
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