Summary Every formula of Quantative historical methods

‭Quantitative history formulas and SPSS functions‬


‭Ratio‬ ‭x/y → divide one by another‬

‭Growth‬ ‭Growth:‬
‭-‬ ‭end value – start value‬
‭Average Growth:‬
‭-‬ ‭(end value – start value) / period in years‬
‭Growth rate:‬
‭-‬ ‭(end value – start value) / start value * 100 → relative‬
‭development/ percentages‬

‭Conditions‬ ‭'Data' → 'Select cases'‬

‭ ogarithmic‬
L ‭SPSS → compute variable → LN(variable) or LG10(variable)‬
‭Transformation‬ ‭-‬ ‭takes proportional differences into account‬

‭Simple Index‬ ‭I = RND(variable/value base year) * 100‬

‭Index‬ ‭Consumer Price/Quantity Index:‬
‭-‬ ‭Laspeyres (quantaties‬‭base‬‭period)‬
‭IpL = (∑ p 1 * q 0 / ∑ p 0 * q 0 ) * 100‬
‭-‬ ‭Paasche (quantities‬‭current‬‭period)‬
‭IpP = (∑ p 1 * q 1 / ∑ p 0 * q 1 ) * 100‬

‭ 0 = prices base year‬
p
‭p 1 = prices current year‬
‭q 0 = quantities base year‬
‭q 1 = quantities current year‬

‭Create variable‬ ‭SPSS: Transform → ‘compute variable’‬

‭Bins/classes‬ ‭SPSS: transform → ‘visual binning’‬

‭Percentile‬ ‭ PSS:‬‭sort ascending‬‭> ‘analyze’ > ‘descriptive statistics’‬‭>‬
S
‭‘frequencies’ > statistics > percentile‬
‭→ fill in the percentile that you want to find‬
‭-‬ ‭Rank/N*100 N→ total number of cases‬

‭Frequency‬ ‭SPSS: ‘analyze’ > ‘descriptive statistics’ > ‘frequencies’‬

‭Crosstabs‬ ‭SPSS: ‘analyze’ > ‘descriptive statistics’ > ‘crosstabs’‬
‭-‬ ‭Compare two variables‬

‭ ustom tables:‬
C ‭SPSS: ‘analyze’ > ‘tables’ > ‘custom tables’‬
‭calculated‬ ‭-‬ ‭mean= average‬
‭values‬ ‭-‬ ‭count= adding up/sum‬

‭Graphs‬ ‭SPSS: ‘Graphs’ → …‬

‭Which graph?‬ ‭Nominal:‬
‭-‬ ‭Absolute/relative → Bar chart‬
‭-‬ ‭Univariate/bivariate → Stacked graph, Pie chart‬
‭Ordinal/scale:‬
‭-‬ ‭Absolute/relative/cumulative → Histogram‬

, ‭-‬ ‭ nivariate/bivariate → Time series (line), Boxplot, Population‬
U
‭pyramid‬

‭ ini coëfficiënt‬
G ‭ ini coefficient: Value between 0 and 1 (or 0 and 100) →‬
G
‭and lorenz curve‬ ‭0 represents total equality, 1 total inequality‬
‭-‬ ‭tells u: the distribution of income or consumption among‬
‭individuals or households‬
‭The Gini coefficient captures how far the Lorenz curve falls from the‬
‭'line of equality'‬
‭-‬ ‭lorenz curve: is the curve from the straight line‬

‭ ne-Sample‬
O ‭ PSS: Analyze > Compare Means:‬
S
‭T-Test‬ ‭One-Sample T-Test‬
‭-‬ ‭look at the two boundaries (lower, upper)‬
‭-‬ ‭tells u: examines whether the mean of a population is‬
‭statistically different from a known or hypothesized value =‬
‭(observed – expected)‬

I‭ndependent-‬ ‭ PSS: Analyze > Compare Means:‬
S
‭Samples T-Test‬ ‭Independent-Samples T-Test‬
‭-‬ ‭look at levrens test smaller than 0.05, choose the second row‬
‭→ look at 2-tailed sig. smaller than 0.05 it is significant‬
‭-‬ ‭tells u: compare 2 sample means to one another‬

‭Z-scores‬ ‭ PSS ‘Analyze’ → ‘Descriptive Statistics’ → ‘Descriptives’ → ‘‬‭save as‬
S
‭variable‬‭’‬
‭-‬ ‭Creates a new variable‬
‭-‬ ‭0 is the average of ditribution→ 0.9= 0.9 standard deviations‬
‭away from the central value‬
‭-‬ ‭z score can be plus or minus → minus=left, plus=right‬
‭-‬ ‭tells u: how far from the mean a data point is (the amount of‬
‭deviations)‬

‭ enter and‬
C ‭ PSS: ‘Analyze’ → ‘Descriptive statistics’ → ‘Statistics’‬
S
‭distribution‬ ‭nominal‬
‭(mode, median,‬ ‭-‬ ‭mode‬
‭mean)‬ ‭ordinal‬
‭-‬ ‭median and quartile deviation. Median, mode and quartiles.‬
‭(quartiles: Q3-Q1/2)‬
‭scale‬
‭-‬ ‭mean with arithmetical average. Median, mode, mean, std‬
‭deviation, skewness, quartiles‬
‭-‬ ‭Median: used when data is skewed a lot (more than 1 or -1) →‬
‭divides the exact middle‬
‭-‬ ‭Mode: value that occurs the most‬
‭-‬ ‭Mean: all the observations counted up/the amount of‬
‭observations‬

‭ uartile‬
Q ‭ PSS: ‘Analyze’ → ‘Descriptive statistics’ → ‘Statistics’ → ‘Quartiles’‬
S
‭distribution‬ ‭Calculate → Percentiles: (Q3(75)-Q1(25))/2= quartile distribution‬

‭Trendline‬ ‭Linear regression line /‬
‭-‬ ‭Straight line‬
‭-‬ ‭‘Fit line’ in SPSS’ Graph Editor‬