Facilitator: Chuah Soo Cheng (PhD)
DAT E : 1 9 JUN E 2 0 2 1
T I ME: 9 3 0 AM TO 1 2 3 0PM
Theory
Research Model
Develop Hypothesis
Collect & Analysis Data
Accept/Reject Hypothesis
Data Screening
Blank Responses
Straight Lining
Data Entry Errors
◦Detect using SPSS
Data
Transformation
Compute
Recode
•Recoding Continuous
Data
•Combining Categories
•Reverse Coding
Descriptive analysis
Numerical methods
Graphic methods
Reliability scale : Cronbach Alpha
Choosing the right statistic
Descriptive Analysis
Categorical Variables
Continuous Variables
Missing Data
◦ Exclude cases listwise
◦ Exclude cases pairwise
◦ Replace with mean
Normality
Skewness and Kurtosis
Tests of Normality
◦ Kolmogorov-Smirnov
◦ Shapiro-Wilk
◦ A non-significant result (p > 0.05) normal-normality
◦ A complication that can arise here occurs when the results of the
two tests don’t agree – that is, when one test shows a significant
result and the other doesn’t. In this situation, use the Shapiro-Wilk
result – in most circumstances, it is more reliable.
Histogram
Normal Q-Q Plot
Boxplot
Graphical Methods
Histogram
◦ Shape of the histrogram  distribution of
data (normally distributed, skewed to the
left or right)
Bar graph
Line graph
Scatterplot
◦ Correlation between two variables
Boxplot
◦ Distribution of data
◦ Outliers
Reliability Scale: Cronbach Alpha
Source: Hair, Josept F :, Celsi, mary;Money, Arthur; Samouel, Philip; and Page, Michael, "The Essentials
of Business Research Method, 3rd Edition " (2016).
Choosing the Right Statistic
Type of question
◦ The researcher should consider the technique that he would be using before choosing the
research design and that will also influence the type of data that needs to be collected.
Number of variables
◦ Univariate (one variable)
◦ Bivariate (2 variables)
◦ Multivariate (> 2 variables)
Scale of measurement  Nominal, ordinal, interval, ratio scale data
Parametric vs Non-parametric hypothesis test
◦ Parametric - interval and ratio scale measurement
◦ Assumptions to follow:
◦ The observations must be independent
◦ The observation must be drawn from normally distributed populations
◦ The population should have equal variances
◦ The measurement scales should be at least interval.
◦ Non-parametric – nominal and ordinal scale measurement
Compare Groups
Independent-Samples t-Test
Independent-Samples t-Test
The t test for independent samples is used to determine whether
the means of two groups are significantly different.
It is called a t test for independent samples because you use it
when you are comparing two groups that are composed of
different people (i.e., groups that are independent of each other).
Examples of research questions that would use an independent t
test:
◦ Do men and women have different average amounts of self-esteem?
◦ Does the number of physical symptoms differ among groups of heart disease patients receiving medication
versus receiving a placebo?
Independent-Samples t-Test
“Levene’s T test for Equality of Variances”
◦ The purpose of this t test is to determine whether the variances between the two groups are significantly
different from each other. This is important because one of the assumptions of a standard independent test
is that the 2 groups have equal variance.
◦ The null hypothesis for Levene’s t test is that the variances are equal, so:
◦ If the p-value (the number listed in the “Sig.” column) is greater than .05, you can assume your variances are indeed equal.
◦ If the p-value is less than .05 for this t test, this suggests that the variances are not equal and that the data violates the assumption.
◦ If the p-value (Sig.) for Levene’s T test was greater than .05 -> USE THE TOP LINE (equal variances assumed)
◦ if the p-value (Sig.) for Levene’s T test was less than .05 -> USE THE BOTTOM LINE (equal variances not assumed).
If the p-value is less than .05, we reject the null hypothesis and conclude that there is a significant
difference between the groups.
If the p-value is greater than .05, we fail to reject the null hypothesis and conclude that there is not
significant difference between the groups.
𝐻𝑜: 𝜇1 = 𝜇2
𝐻1: 𝜇1 ≠ 𝜇2
Independent-Samples t-Test – Effect size
COHEN’S D
𝐶𝑜ℎ𝑒𝑛′ 𝑠
𝑑=
EFFECT SIZE ETA SQUARED
𝑥1 −𝑥2
𝑠𝑝2
Guidelines
◦ d = 0.2, small effect
◦ d = 0.5, medium effect
◦ d = 0.8, large effect
𝐸𝑡𝑎 𝑠𝑞𝑢𝑎𝑟𝑒𝑑 =
𝑡2
𝑡 2 +(𝑁1+𝑁2 −2)
Guidelines
◦ d = 0.01, small effect
◦ d = 0.06, medium effect
◦ d = 0.14, large effect
https://www.socscistatistics.com/effectsize/default3.aspx
http //web.uccs. edu/lbecker/psy590/escalc3.htm
Cohen, J. (1988), Statistical Power Analysis for the Behavioral Sciences,
2nd Edition. Hillsdale: Lawrence Erlbaum.
One-Way Analysis of Variance (ANOVA)
Non-Parametric: Chi-Square Test of
Independence
Two categorical variables with two or more categories
Contingency/ Crosstabulation Table
Gender & Smoker
Effect size
◦ Phi coefficient
◦ Cramer’s V
◦ For R-1 or C-1 equal to 1 (two categories)  small = 0.01, medium = 0.30, large = 0.50
◦ For R-1 or C-1 equal to 2 (three categories)  small = 0.07, medium = 0.21, large = 0.35
◦ For R-1 or C-1 equal to 3 (four categories)  small = 0.06, medium = 0.17, large = 0.29
Explore Relationship Among
Variables
Correlation
Describe the strength and direction of the linear relationship between
two variables.
Pearson correlations
Spearman rank order correlation
Negative/ positive correlation
Strength of the relationship (Cohen, 1988, p. 79-81)
Presenting correlation results
P
Multiple Regression
used to analysed the relationship between a single dependent (continuous) and
several independent variables (continuous or nominal)
◦ How well a set of variables in predicting a particular outcome
◦ Which variable in a set of variables is the best predictor of an outcome
◦ Whether a particular predictor variable is still able to predict an outcome when
the effects of another variable are controlled for
Assumptions of Multiple Regression
Sample size
Multicollinearity
Outliers
Assumptions of Multiple Regression
Sample size
◦ “for social science research, about 15 subjects per predictor are needed for a reliable equation” (Stevens (1996,
p.72).
◦ N > 50 + 8M (m = number of independent variables) (Tabachnick & Fidell, 2007, p.123).
◦ IVs = 5, N > 50 + 8(5) = 90
Multicollinearity
◦
◦
◦
◦
◦
The relationship between the independent variables
Multicolinearity exists if the independent variables are highly correlated .
Multicollinearity is a problem because it undermines the statistical significance of an independent variable.
Multicollinearity does not affect the model's predictive accuracy
Correlation / VIF  Correlation < 0.8, VIF  5  no multicollinearity problem
Outliers  Normal Probability Plot (P-P)
Normality, linearity, homoscediticity and independence of residuals
◦ The residuals should be normally distributed about the predicted DV.
◦ The residuals should have a straight-line relationship with predicted DV.
◦ The variance of the residuals about predicted DV scores should be the same for all predicted scores
Multiple Regression (Interpretation)
R2  how much of the variance in the dependent variable is explained by the model (IVs)
F-test  The
F-ratio in the ANOVA table tests whether the overall
regression model is a good fit for the data.
Coefficients
◦ Unstandardized beta  represents the amount of change in a dependent variable Y due to a change of 1
unit of independent variable X.
◦ less useful for direct comparison when the measurement scales of the independent
variables are different.
◦ Standardized beta  means IVs be in terms of standard deviations and so easily compared with each other.
◦ Compare the strength of the effect of each individual variable to the dependent
variable
p-value < 0.5  the variable is significantly contributing to the prediction of the dependent variable