statistical analyses. spss statistical analysis program it is an analytical software recognized by...
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Statistical analyses
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SPSS
Statistical analysis program
It is an analytical software recognized by the scientific world (e.g.: the Microsoft Excel program is not recognized by the scientific world)
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SPSS
Let’s start the SPSS software!
Paste the data onto the DATA VIEW window!
It has two windows, one of them contains the data (DATA VIEW), and the types of the variables must be given in the other one (VARIABLES).
Exact coding of variables is the basis of successful SPSS use.
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Basics of computer-based
analysis
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Types of data Measurable data
Differences between data are equal E.g.
interval scale How old are you? How much is your weight?
Ordinal data Data originating from gradation Special type: reletad gradation positions
Nominal scaleThe data are replaced by numbers. E.g. Gender? 1. Male 2. Female The data do not signal order The data cannot be added
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Statistical procedures
Descriptive statistics If we analyze actual persons,
that is population = samples Statistical indicators
Frequencies Central tendency Dispersion Correlation
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Statistical procedures
Mathematical statistics It provides the information whether we may draw
conclusion based on the representative sample referring to the population.
Definition Population: the group which the conclusions
refer to E.g.: university student; German people;
teachers Sample: the ones actually involved in the
surveys Representative sample: when the composition
of the sample mirrors the composition of the population. E.g.: Gallup’s deal with the Public Opinion Office
around the time of the presidential elections in 1936
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Mathematical statistics
Analysis of differences The aims: to show the criteria in
which elements differ from each other
Types of data
Number of samples
Scale Ordinal Nominal
One One-sample t-samples test
Wilcoxon-test Crosstabs analysis,Chi-square test
Two Independent t-sampleF-test
Mann-Whitney-test
Cross database analysis,Chi-square test
Three or more
ANOVA analysis Kruskall-Wallis-test
Cross database analysis,Chi-square test
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Mathematical statistics
Analyzing correlations
Types of data
Number of samples
Scale Ordinal Nominal
Two Correlate Spearmancorrelate
Crosstabs analysis,Chi-square test
Two or more Regression
More than two
Partial correlateFactor analysisCluster analysis
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Descriptive statistics
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Central Tendency
Mean
Modus : (most frequent data)
Median
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Frequency1. Determining the number of categories
An odd number between 10 and 20
If the number of the samples is low (e.g.50 responders) there can be fewer categories (7 categories)
2. Determining the intervals
1, 2, 3, 5, 10 depending on the number of categories
Disjunction: It should be noted that the each item in the sample must be categorized into one particular category, so the groups may not overlap.
E.g.: Bad samples:Age groupsBelow 2020-3030-40…
E.g.: God examples:Age groupsBelow 2020-2930-39…
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Absolute frequency
Def: The number of items belonging to particular category is absolute frequency value.
the subgroup frequencies together create theabsolute frequency distribution of the sample.
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Further frequency indicators
Relative frequency means the quotient of the absolute frequency values and the number of the samples.The relative frequency gives the percent of the responders in one particular category compared to the total number of samples.
Cumulative frequency means how many items of the sample can be found all together below the upper limit of the category.
Cumulative percent means the quotient of the cumulative frequency and the number of the sample.
IT shows what percent of the sample can be found below the upper limit of the category.
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Dispersion indicators
Range: the range of the samples means the difference between the highest and lowest items.R = Xmax - Xmin
Average difference:the average distance (absolute deviation) of the items from the average.
Square sum:Sum of the quadrant of the deviation from the average.
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Variance
Variance the square sum divided by the degree of freedom of the sample
Degree of freedom is the number of the independent elements (the number of the responders) of the sample.
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Standard deviation
Standard deviation is the square root with a positive sign of the variance.
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Theorem
More than 2/3 of the data belong to a 1 standard deviation extending to the positive and negative directions from the mean.
More than 90% of the data belong to a 2 standard deviation taken from the mean.
More than 90% of the data belong to a 3 standard deviation taken from the mean.
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Relative standard deviation
The Relative deviation is an indicator related which provides what percent of the mean is the standard deviation.
standard deviationRelative deviation = mean
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Quartiles
The quartiles are the quartering points of the sample.
Interquartiles half-extension: is the difference between the third and the first quartile: Q3-Q1
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Interrelations
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Interrelations between frequency and mean indicator
Left tendency: Modus > Median > Mean
Right tendency : Modus < Median < Mean
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Normal distribution (bell curve) : All the three indicators coincideModus = Median = Mean
Interrelations between frequency and mean indicatior
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Mathematical statistics
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Relations examinations
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Correlation
Correlation coefficient is the indicator which shows the direction and strength between two data list.
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Correlation
There is correlation between the two samplestáblázatxy rr
táblázatxy rr There is no correlation between the two samples
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Correlation coefficientThe interpretation of the correlation coefficient
0,9 – 1 extremely strong correlation between the two data lists
0,75 – 0,9 strong0,5 – 0,75 detectable0,25 – 0,5 weak0,0 – 0,25 no relationship
Direction
If the correlation coefficient is negative contrasting relationship
E.g. The numbers of hours doing sports – your weight
If the correlation coefficient is positive data changing simultaneously
E.g. The size of your home library – the rate of loving to read
28
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Relationship between/among variables – Crosstabs Crosstabs – illustrating the distribution of
two nominal or ordinal variables on the same chart.
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Crosstabs- Chi-square
It is an indicator which shows whether the correlations in the cross tabs are valid only for the samples or for the population as well.
It cannot be used efficiently if the value is less then 5 in more than 20% of the cells.
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Hypothesis analyses
It is a method to decide whether the differences in data are significant or random.
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Paired-samples T-test
The paired-samples T-test is used when the same people are asked or tested twice (e.g. one-sample experiment)
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zt '
Where:
- mean
s - Standard deviationz
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Match the t-number with the value of the „Critical values of the t-distribution” chart
If t’ > t chart the different is significant
If t’ < t chart the different is random
Paired-samples T-test
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T-test with computer
It is not necessary use the „Critical values of the t-distribution” chart, because most software provides the „p” value (Signif of t, Sig.Level).
The „p” shows what percent is the failure rate.
If „p”<0.05 (5%) then the difference is significant
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Independent t-test
H0: two independent samples taken from the same population.
(H0 definition: the zero hypothesis is that the difference is random )
This type of test can only can be conductived if the variances of the two groups not too different.
The F-test can give the answer.
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F-test
The F-test is the quotient of the variance squares.
If Fnumber < Fchart there is no significant differenceIf Fnumber > Fchart there is a great difference between the variances
the T-test cannot be done. you can try the Welch-test.
22
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sF
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Independent t-test
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The degree of freedom = n+m-2.
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Illustration of result
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Mean = 12,9Std. Dev. = 5,515N = 20
Histogram
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Mean = 12,9Std. Dev. = 5,515N = 20
REL
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24
Missing
Egyéni eredmény
Aim: to make the result look conceivable and visual
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Frequency polygon
Illustrating frequency data with a line diagram.
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Histogram
Illustrating frequency data with a bar diagram.
The title of the X axis is intervals.
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Histogram shapes
Symmetrical, peaked
Symmetrical, normal
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Histogram shapes
bimodal
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Histogram shapes
Right side tendency
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Histogram shapes
Left side tendency
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Interrelations between frequency and mean indicator
Normal distribution: Mean = Median = Modus
Skewness = 0
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Interrelations between frequency and mean indicator
Symmetric with two modes
Bimodul
Skewness = 0
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Interrelations between frequency and mean indicator
Right side tendency
Mode<Median<Mean
Skewness = (-)
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Interrelations between frequency and mean indicator
Right side tendency
Mean < Median < Mode
Skewness = (+)
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Normal distribution with different standard deviation
Kurtosis = 1 normal
If the Kurtosis value is bigger the polygon is flatter