Chapter 11 Descriptives

11.1 Intro

The term ‘descriptives’ is typically used to refer to a set of measures summarizing a distribution, such as central tendency and spread measures.

11.1.1 Example dataset

This example uses the Rosetta Stats example dataset “pp15” (see Chapter 1 for information about the datasets and Chapter 3 for an explanation of how to load datasets).

11.1.2 Variable(s)

From this dataset, this example uses variables xtcUseDoseHigh, highDose_intention, highDose_attitude (all three numeric variables) and hasJob_bi (a dichotomous factor, i.e. a categorical variable).

11.2 Input: jamovi

Figure 11.1: Opening the “Exploration” menu in jamovi.

11.3 Input: R

11.3.1 R: base

Use the following command:

summary(
  dat[,
    c(
      "xtcUseDoseHigh",
      "highDose_intention",
      "highDose_attitude",
      "hasJob_bi"
    )
  ]
);

11.3.2 R: Rosetta

Use the following command:

rosetta::descr(
  dat,
  items = c(
    "xtcUseDoseHigh",
    "highDose_intention",
    "highDose_attitude",
    "hasJob_bi"
  ),
  histogram = TRUE,
  boxplot = TRUE
);

When ordering descriptives for a single variable (and in version 0.3.2 of the rosetta package, also for multiple variables), you can finetune which descriptives you get by passing TRUE or FALSE for arguments mean, meanCI, median, mode, var, sd, se, min, max, q1, q3, IQR, skewness, kurtosis, dip, totalN, missingN, and validN.

In addition, you can order histograms (or bar charts, for factors) and box plots (for numeric variables only) by passing histogram=TRUE and boxplot=TRUE, respectively.

If you omit the items argument, you get descriptives for all variables in the dataframe; and if you don’t pass a dataframe, but a single variable, you will just get the descriptives for that variable.

11.4 Input: SPSS

11.4.1 SPSS: GUI

First activate the pp15 dataset by clicking on it (see 2.4.1).

Figure 11.2: Opening the “descriptives menu” in SPSS

Then select the variables of interest.

Figure 11.3: Selection of variables of interest

11.4.2 SPSS: Syntax

Use the following command (this requires the dat dataset to be the active dataset, see 2.4.1):

DESCRIPTIVES VARIABLES=xtcUseDoseHigh highDose_intention highDose_attitude hasJob_bi 
  /STATISTICS=MEAN STDDEV MIN MAX.

11.5 Output: jamovi

Figure 11.4: The produced descriptives output in jamovi

11.6 Output: R

11.6.1 R: rosetta

11.6.1.1 Descriptives for variables in data frame pp15

	mean	meanCI	median	mode	var	sd	min	max	skewness	kurtosis	dip	totalN	missingN	validN
xtcUseDoseHigh	208.35	[193.2; 223.5]	180.0	200	17000.69	130.39	25.0	880.0	1.84	4.74	0.06	829	542	287
highDose_intention	2.37	[2.22; 2.51]	2.0	2	2.17	1.47	0.5	15.0	3.14	18.41	0.09	829	431	398
highDose_attitude	3.64	[3.52; 3.76]	3.8	4	1.16	1.08	1.0	6.4	-0.43	-0.05	0.04	829	525	304

11.6.1.1.1 Frequencies for hasJob_bi

	Frequencies	Perc.Total	Perc.Valid	Cumulative
No	81	9.8	21.1	21.1
Yes	302	36.4	78.9	100.0
Total valid	383	46.2	100.0
NA (missing)	446	53.8
Total	829	100.0

11.6.1.1.2 Histograms for variables in data frame pp15

Figure 11.5: Histograms for variables in data frame pp15

11.6.1.1.3 Boxplots for variables in data frame pp15

Figure 11.6: Boxplots for variables in data frame pp15

11.7 Output: SPSS

Figure 11.7: The descriptives produced in SPSS.

11.8 Read more

If you would like more background on this topic, you can read more in these sources:

Discovering Statistics with SPSS (closed access):
Learning Statistics with R (Navarro 2018): section XXXXXXXXXX, page XXXXXXXXXX (or follow this link for the Bookdown version)

References

Navarro, Danielle. 2018. Learning Statistics with R. 0.6 ed. New South Wales, Australia. https://learningstatisticswithr.com/.