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Doing Quantitative Research 26E02900, 6 ECTS Cr. Olli-Pekka Kauppila Daria Volchek. Lecture II - May 14, 2014. Today’s lecture. AM session Descriptive statistics, assumptions for regression analyses PM session Introduction to regression analysis. Learning objectives – AM session.

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Doing quantitative research 26e02900 6 ects cr

Doing Quantitative Research26E02900, 6 ECTS Cr.

Olli-Pekka Kauppila

Daria Volchek

Lecture II - May 14, 2014


Today s lecture
Today’slecture

AM session

  • Descriptive statistics, assumptions for regression analyses

    PM session

  • Introduction to regression analysis


Learning objectives am session
Learning objectives – AM session

Deepen the understanding of research design and measures

Improve skills at using SPSS

Understand different ways of dealing with missing values

Learn more about computing variables

Learn the assumptions for multivariate analyses

Learn to make graphs to examine and illustrate your data

Learn to identify and deal with outlier observations

Learn more about interpreting correlations


Opening an excel file in spss
Opening an excelfile in SPSS

Open SPSS software

File → Open → Data

Find and open your excel file

When the dataset is open, save it in .sav form

  • File → Save as


How to deal with missing values
How to deal with missingvalues?

No matter where and how you collect your data, you are likely to have missing values

Missing values are not a problem, provided that there are not too many of them, and you deal with them appropriately

  • Remove any cases with a high number of missing values

  • Do not use variables with a high number of missing values

  • Common remedies

  • Deletion: if missing values are few and randomly distributed

  • Replace with means: if missing values are relatively few

  • Multiple imputation: use software to estimate the missing values based on what is known about the case


Computing variables to make them analyzable
Computing variables to make them analyzable

Before analyses, you usually need to modify your variables

Examples:

  • Transform reversely coded items: item 4B on job satisfaction scale is reversely coded. Thus, 4B_re: 5→1; 4→2; 3→3; 2→4; 1→5

  • Compute summated scales: Job satisfaction = (4A + 4B_re + 4C + 4D) / 4

  • Create dummies: e.g. Firm 1: Firm 1 = 1; others = 0

  • Transform other variables: e.g. Employee age = data collection year - year of birth

    SPSS: Transform → Compute variable


Focus on variables that you use in your analyses
Focus on variables that you use in your analyses

We need to use the following primary variables

  • Job satisfaction (4A, 4B*, 4C, 4D)

  • Risk avoidance (11A*, 11B*, 11C*, 11D*, 11F)

  • Perceived managerial support (16A, 16B, 16C, 16D, 16E, 16F)

    * = Reverse-coded items

  • And the following background variables

  • Age (= Data collection year - Birth year)

  • Gender (1 = Female, 0 = others)

  • Firm membership (three different firms → Create 3 dummy variables)


Analyze reliability of the perceptual measures
Analyze reliability of the perceptual measures

Analyze → Scale → Reliability analysis

Go to “statistics;” select all items from “descriptives for”

  • Job satisfaction (4A, 4B_re, 4C, 4D): Alpha = .79 → ok

  • Risk avoidance (11A_re, 11B_re, 11C_re, 11D_re, 11F): Alpha = .65; but .82 ifwedropitem 11F → dropit→ ok

  • Perceived managerial support (16A, 16B, 16C, 16D, 16E, 16F): Alpha = .96 → ok

  • Transform → Computevariable

  • E.g. Target variable: Jobsat; Numericexpression: (@[email protected][email protected][email protected])/4


Assumptions for multivariate analyses
Assumptions for multivariate analyses

Normal distribution

Homoscedasticity

  • I.e. equal levels of variance across the range of predictor variables

    Linearity

    Absence of uncorrelated errors

  • I.e. relevant but unmeasured variables do not bias the results


Graphical examination of data
Graphical examination of data

SPSS: Graphs → Chart builder

Like in Excel, you find bar, line, area, and pie charts that you may use to depict your data

Particularly useful graphs:

  • Scatter plots

  • Histograms

  • Boxplots

  • Correlation analysis gives you an overview of how different variables are related one another


Scatterplot
Scatterplot

Linear regression line

Locally weighted regression line

How employee age relates to role clarity?



How job satisfaction is related to managerial support1
How job satisfaction is related to managerial support?

Or, perhaps the effect is not linear after all…


Histograms
Histograms

How values for employee role clarity are distributed?


Boxplots
Boxplots

Are distributions of role clarity any different for male and female employees?


Skewness and kurtosis
Skewness and kurtosis

Analyze → Descriptive statistics → Descriptives → Options → Skewness and kurtosis

When there is no skewness or kurtosis, the variable is normally distributed


Skewness
Skewness

Positively skewed distribution

Negatively skewed distribution

Common remedies; transform the variable by taking:

Logarithm or squared term

Squared or cubed terms


Kurtosis
Kurtosis

Peaked distribution - positive value

Flat distribution - negative value

Common remedies; transform the variable by taking:

Try all transformations

Inverse of the variable (1 / X or Y)


How risk avoidance is related to employee age?What can you tell about the distribution of job satisfaction?Does the level of perceived managerial support vary between firms?

Classroom exercise


How risk avoidance is related to employee age
How risk avoidance is related to employee age?

Older employees tend to be more risk averse than younger employees




Outliers
Outliers firms?

Outliers are observations that deviate substantially from other observations

The key question is: why is it that the outlier observation is so different?

In general, if the outlier observation seems to be caused by a mistake, then it should be deleted

  • I.e. a respondent’s birth year is marked as 1776

    If the outlier observation is substantially different from other observations, but nevertheless a “legitimate member” of the sample, it should be retained

  • I.e. annual salaries of some (very few) individuals are millions of euros


Outliers1
Outliers firms?

Why these three individuals have such a low level of role clarity?

Should we remove these outliers from the analysis?


Outliers2
Outliers firms?


Correlation analysis
Correlation firms?analysis

Correlation analysis shows you how different variables are related to one another

When the sample size increases, even relatively weak correlations become statistically significant

Because of multicollinearity, you do not want to include strongly correlated independent variables into the same model

Note: correlation does not imply causation!

SPSS: Analyze → Correlate → Bivariate


Model
Model firms?

Dependent variable:

  • Job satisfaction

    Independent variables:

  • Risk avoidance

  • Perceived managerial support

  • Control variables:

  • Gender

  • Age

  • Firm affiliation


Correlation table output
Correlation firms?table - output


Correlation table output1
Correlation firms?table - output

In most datasets, correlations above .2 are statistically significant. Correlations

above .5 are very strong

Usual cutoff values:

p < .001

p < .01

p < .05


Correlation table output2

This correlation is not significant. Will that be a problem? firms?

Correlationtable - output

What do these correlations tell us?

Should we exclude a firm dummy from the regression model?


Correlation table output3
Correlation firms?table - output

Will risk avoidance cause job satisfaction?

What is the role of employee age?


Key descriptives
Key firms?descriptives

Besides correlations, researchers usually report means and standard deviations of the variables

  • Mean is informative as it gives you a fairly good understanding of the overall level of variables.

    I.e. It is quite different if the mean value of job satisfaction is 2.2, rather than 3.9 (on a 5-point Likert scale)

  • Standard deviation is informative, because it helps you interpret high and low values. Values one standard deviation above the mean value are usually considered as “high,” and values one standard deviation below the mean value are “low”

    I.e. If job satisfaction’s mean value is 3.9 and standard deviation 0.4. Thus, job satisfaction of 4.3 is “high” and 3.5 is “low.”


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