Category: SPSS/STATS Assignment Help

SPSS/Statistics (Questions & Answers),

Everything you need to know about:

• Predictor variable
• Probability distribution
• Qualitative methods
• Quantitative methods
• Quartile
• Randomization
• Range
• Ratio variable
• Reliability
• Repeated-measures design
  Second quartile
• Skew
• Systematic variation
• Tertium quid
• Test–retest reliability
• Theory
• Unsystematic variation
• Upper quartile
• Validity
• Variables
• Within-subject design
• z-scores

SPSS/Statistics (Questions & Answers)

Everything you need to know about: 

• α-level
• β-level
• Central limit theorem
• Confidence interval
• Degrees of freedom
• Deviance
• Effect size
• Fit
• Linear model
• Meta-analysis
• One-tailed test
• Population
• Power
• Sample
• Sampling distribution
• Sampling variation
• Standard deviation
• Standard error
• Standard error of the mean (SE)
• Sum of squared errors (SS)
• Test statistic
• Two-tailed test
• Type I error
• Type II error
• Variance

SPSS/Statistics (Questions & Answers)

Anything you need to know about:

• Bootstrap
• Hartley’s FMax
• Homogeneity of variance
• Independence
• Kolmogorov–Smirnov test
• Levene’s test
• Noniles
• Normally distributed data
• Parametric test
• Percentiles
• P–P plot
• Q–Q plot
• Quantiles
• Robust test
• Shapiro–Wilk test
• Transformation
• Trimmed mean
• Variance ratio

Correlation Analysis SPSS/Statistics (Questions & Answers),

Everything you need to know about:

• Biserial correlation
• Bivariate correlation
• Coefficient of determination
• Covariance
• Cross-product deviations
• Kendall’s tau
• Partial correlation
• Pearson correlation coefficient
• Point–biserial correlation
• Semi-partial correlation
• Spearman’s correlation coefficient
• Standardization

Regression Analysis SPSS/Statistics (Questions & Answers)

Everything you need to know about:

• Adjusted predicted value
• Adjusted R2
• βi
• Cook’s distance
• Covariance ratio (CVR)
• Cross-validation
• Deleted residual
• DFBeta
• DFFit
• Dummy variables
• Durbin–Watson test
• F-ratio
• Generalization
• Goodness of fit
• Hat values
• Heteroscedasticity
• Hierarchical regression
• Homoscedasticity
• Independent errors
• Leverage
• Mahalanobis distances
• Mean squares
• Model sum of squares
• Multicollinearity
• Multiple R
• Multiple regression
• Outcome variable
• Perfect collinearity
• Predictor variable
• Residual
• Residual sum of squares
• Shrinkage
• Simple regression
• Standardized DFBeta
• Standardized DFFit
• Standardized residuals
• Stepwise regression
• Studentized deleted residuals
• Studentized residuals
• Suppressor effects
• t-statistic
• Tolerance
• Total sum of squares
• Unstandardized residuals
• Variance inflation factor (VIF)
• Autocorrelation
• bi

Logistic Regression Analysis SPSS/Statistics (Questions & Answers)

Everything you need to know about:

• –2LL
• Binary logistic regression
• Chi-square distribution
• Complete separation
• Cox and Snell’s R2
• CS
• Exp(B)
• Hosmer and Lemeshow’s R2
• L
• Interaction effect
• Likelihood
• Logistic regression
• Log-likelihood
• Main effect
• Maximum-likelihood estimation
• Multinomial logistic regression
• Nagelkerke’s R2
• N
• Odds
• Polychotomous logistic regression
• Roa’s efficient score statistic
• Suppressor eff

Comparing Two Means (SPSS/Statistics Questions & Answers)

Everything you need to know about:

• Dependent t-test
• Grand mean
• Independent t-test
• Standard error of differences
• Variance sum law

Everything you need to know about:

• Analysis of variance (ANOVA)
• Bonferroni correction
• Brown–Forsythe F
• Cubic trend
• Deviation contrast
• Difference contrast (reverse Helmert contrast)
• Eta squared, η2
• Experimentwise error rate
• Familywise error rate
• Grand variance
• Harmonic mean
• Helmert contrast
• Independent ANOVA
• Omega squared, ω2
• Orthogonal
• Pairwise comparisons
• Planned contrasts
• Polynomial contrast
• Post hoc tests
• Quadratic trend
• Quartic trend
• Repeated contrast
• Simple contrast
• Weights
• Welch’s F

Everything you need to know about:

• Adjusted mean
• Analysis of covariance (ANCOVA)
• Covariate
• Homogeneity of regression slopes
• Partial eta squared (partial η2)
• Partial out
• Sidak correction

Everything you need to know about:

• Mixed ANOVA
• Calculating effect sizes
• Output for mixed factorial ANOVA
• Mixed design

Non-parametric Tests, Everything you need to know about:

• Cochran’s Q
• Friedman’s ANOVA
• Jonckheere–Terpstra test
• Kendall’s W
• Kolmogorov–Smirnov Z
• Kruskal–Wallis test
• Mann–Whitney test
• McNemar’s test
• Median test
• Monte Carlo method
• Moses extreme reactions
• Non-parametric tests
• Ranking
• Sign test
• Wald–Wolfowitz runs
• Wilcoxon rank-sum test
• Wilcoxon signed-rank test

Exploratory Factor Analysis (SPSS/Statistics Questions & Answers), Everything you need to know about:

• Alpha factoring
• Anderson–Rubin method
• Common variance
• Communality
• Component matrix
• Confirmatory factor analysis CFA)
• Cronbach’s α
• Direct oblimin
• Equamax
• Extraction
• Factor analysis
• Factor loading
• Factor matrix
• Factor scores
• Factor transformation matrix, 
• Intraclass correlation coefficient (ICC)
• Kaiser–Meyer–Olkin (KMO) measure of sampling adequacy
• Kaiser’s criterion
• Latent variable
• Oblique rotation
• Orthogonal rotation
• Pattern matrix
• Principal component analysis (PCA)
• Promax
• Quartimax
• Random variance
• Rotation
• Scree plot
• Singularity
• Split-half reliability
• Structure matrix
• Unique variance
• Varimax

Everything you need to know about:

• Chi-square test
• Contingency table
• Cramer’s V
• Fisher’s exact test
• Goodman and Kruskal’s λ
• Loglinear analysis
• Odds ratio
• Phi
• Saturated model
• Yates’s continuity correction

Multilevel Linear Models,

Everything you need to know about:

• AIC
• AICC
• AR(1)
• BIC
• CAIC
• Centring
• Diagonal
• Fixed coefficient
• Fixed effect
• Fixed intercept
• Fixed slope
• Fixed variable
• Grand mean centring
• Group mean centring
• Growth curve
• Multilevel linear model
• Polynomial
• Random coefficient
• Random effect
• Random intercept
• Random slope
• Random variable
• Unstructured
• Variance components

1. A sample of 49 sudden infant death syndrome (SIDS) cases had a mean birth weight of 2998 g. Based on other births in the county, we will assume σ = 800 g. Calculate the 95% confidence interval for the mean birth weight of SIDS cases in the county. Interpret your results.
2. True or false? Given that a confidence interval for µ is 13 +

• The value of 13 in this expression is the point estimate.
• The value 5 in this expression is the estimate’s standard error.
• The value 5 in this expression is the estimate’s margin of error.
• The width of the confidence interval is 5.

3. When do we use a t-statistic instead of a z-statistic to help infer a mean?
4. Identify whether the studies described here are based on (1) single samples, (2) paired samples, or (3) independent samples.

• Cardiovascular disease risk factors are compared in husbands and wives
• A nutritional exam is applied to a random sample of individuals. Results are compared to the results of the whole nation.
• An investigator compares vaccination histories in 30 autistic schoolchildren to a simple random sample of non-autistic children from the same school district.

5. Identify two graphical methods that can be used to compare quantitative (continuous) data between two independent groups.
6. A questionnaire measures an index of risk-taking behavior in respondents. Scores are standardized so that 100 represents the population average. The questionnaire is applied to a sample of teenage boys and girls. The data for boys is {72, 73, 86, 95, 95, 95, 96, 97, 99, 125}. The data for girls is {89, 92, 93, 98, 105, 106, 110, 126, 127, 130}. Explore the group differences with side-by-side boxplots. Create the boxplots and then describe how risk taking behavior varies between genders.
7. Which study will require a larger sample size, one done with 80% power or 90% power when alpha (type I error) is set at 0.05 and we use the same population and expected difference and variation for both studies?
8. True or False: When using data from the same sample, the 95% confidence interval for µ will always support the results from a 2-sided, 1 sample t-test.  Explain your reasoning.