Calculate Standard Error Spss
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This page shows examples of how to obtain descriptive statistics, with footnotes explaining the output. The data used in these examples were collected on 200 high schools students and are scores on various tests, including science, math, reading and social studies (socst). The variable female is
Standard Error Of Measurement Spss
a dichotomous variable coded 1 if the student was female and 0 if male. In the standard error of estimate spss syntax below, the get file command is used to load the data into SPSS. In quotes, you need to specify where the data file standard error in spss output is located on your computer. Remember that you need to use the .sav extension and that you need to end the command (and all commands) with a period. There are several commands that you can use to get descriptive statistics for
Standard Deviation Spss
a continuous variable. We will show two: descriptives and examine. We have added some options to each of these commands, and we have deleted unnecessary subcommands to make the syntax as short and understandable as possible. You will find that the examine command always produces a lot of output. This can be very helpful if you know what you are looking for, but can be overwhelming if you are not used to it. If you need just a few numbers, you may
Confidence Interval Spss
want to use the descriptives command. Each as shown below. We will use the hsb2.sav data file for our example. get file "c:\hsb2.sav". descriptives write /statistics = mean stddev variance min max semean kurtosis skewness. descriptives write /statistics = mean stddev variance min max semean kurtosis skewness. a. Valid N (listwise) - This is the number of non-missing values. b. N - This is the number of valid observations for the variable. The total number of observations is the sum of N and the number of missing values. c. Minimum - This is the minimum, or smallest, value of the variable. d. Maximum - This is the maximum, or largest, value of the variable. e. Mean - This is the arithmetic mean across the observations. It is the most widely used measure of central tendency. It is commonly called the average. The mean is sensitive to extremely large or small values. f. Std. - Standard deviation is the square root of the variance. It measures the spread of a set of observations. The larger the standard deviation is, the more spread out the observations are. g. Variance - The variance is a measure of variability. It is the sum of the squared distances of data value from the mean divided by the variance divisor. The Corrected SS is the sum of squared distances of data value from the mean. Therefore, the variance is the corrected SS divided by N-1. We don't generally
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T Test Spss
Validated Questions Tags Users Badges Unanswered Ask Question _ Cross Validated is a question and answer site for people interested in statistics, coefficient of variation spss machine learning, data analysis, data mining, and data visualization. Join them; it only takes a minute: Sign up Here's how it works: Anybody can ask a question Anybody can answer The best answers are voted up http://www.ats.ucla.edu/stat/spss/output/descriptives.htm and rise to the top How to compute the standard error of measurement (SEM) from a reliability estimate? up vote 3 down vote favorite 1 SPSS returns lower and upper bounds for Reliability. While calculating the Standard Error of Measurement, should we use the Lower and Upper bounds or continue using the Reliability estimate. I am using the formula : $$\text{SEM}\% =\left(\text{SD}\times\sqrt{1-R_1} \times 1/\text{mean}\right) × 100$$ where SD is the standard deviation, http://stats.stackexchange.com/questions/9312/how-to-compute-the-standard-error-of-measurement-sem-from-a-reliability-estima $R_1$ is the intraclass correlation for a single measure (one-way ICC). spss reliability share|improve this question edited Apr 8 '11 at 1:15 chl♦ 37.4k6124243 asked Apr 7 '11 at 12:36 user4066 You seem to be calculating the coefficient of variation of the measurement, not the standard deviation or standard error. –GaBorgulya Apr 7 '11 at 14:47 @GaBorgulya Usually, SEM is computed in a different way; contrary to SD or SE, it is supposed to account for scores reliability, specific to the measurement instrument. –chl♦ Apr 8 '11 at 1:10 add a comment| 2 Answers 2 active oldest votes up vote 1 down vote You should use the point estimate of the reliability, not the lower bound or whatsoever. I guess by lb/up you mean the 95% CI for the ICC (I don't have SPSS, so I cannot check myself)? It's unfortunate that we also talk of Cronbach's alpha as a "lower bound for reliability" since this might have confused you. It should be noted that this formula is not restricted to the use of an estimate of ICC; in fact, you can plug in any "valid" measure of reliability (most of the times, it is Cronbach's alpha that is being used). Apart from the NCME tutorial that I linked to in m
SPSS Main Statistical Functions Introduction This tutorial walks you through SPSS' main statistical functions. They are mainly used with COMPUTE and IF. Note that these are all within-subjects http://www.spss-tutorials.com/spss-main-statistical-functions/ functions (or “horizontal functions”). For between-subjects (or “vertical”) functions, see AGGREGATE. We recommend you follow along by downloading and opening hospital.sav. Within-subjects versus between-subjects functions. SPSS Statistical Funcions - Missing http://academic.udayton.edu/gregelvers/psy216/spss/descript1.htm Values SPSS statistical functions only return system missing values if all their input values are missing values. If a single input value is valid, the output value will be valid standard error too. This holds for all functions we'll cover in this tutorial. Remember that the opposite holds for SPSS numeric functions: the latter only return a valid value if all their input values are valid. SPSS Statistical Funcions - Dot Operator A minimal number of valid input values can be specified for statistical functions. This is done by suffixing the function with standard error of a period followed by the required number of valid values. For example compute mean_v = mean.3(v1 to v5). means “Compute mean_v only for cases having at least 3 valid values over v1 to v5. Cases with fewer valid values must get a system missing value on mean_v.” The dot operator can be used with all functions covered in this tutorial. Don't overlook it. Although it's little known among SPSS users, it's a terrific time saving feature. Compute mean only for cases with at least 3 valid values on the input variables Data Preparation We'll use only the last 5 variables in our data.Strictly, calculations are not allowed on such ordinal variables. However, see Assumption of Equal Intervals. The functions we'll demonstrate on them may return incorrect values if we fail to specify user missing values. We'll therefore do a quick check by running FREQUENCIES with the syntax below. Note the TO keyword in step 5. *1. Specify folder where data are located.cd 'd:/temp'.*2. Open data file.get file 'hospital.sav'.*3. Show values and value labels in output.set tnumbers both.*4. Inspect frequencies.freq
plots, Tukey box plots, calculate the standard measures of central tendency (mean, median, and mode), calculate the standard measures of dispersion (range, semi-interquartile range, and standard deviation / variance), and calculate measures of kurtosis and skewness. This tutorial assumes that you have: Downloaded the standard class data set (click on the link and save the data file) Started SPSS (click on Start | Programs | SPSS for Windows | SPSS 12.0 for Windows) Loaded the standard data set The Frequency Command The frequencies command can be used to determine quartiles, percentiles, measures of central tendency (mean, median, and mode), measures of dispersion (range, standard deviation, variance, minimum and maximum), measures of kurtosis and skewness, and create histograms. The command is found at Analyze | Descriptive Statistics | Frequencies (this is shorthand for clicking on the Analyze menu item at the top of the window, and then clicking on Descriptive Statistics from the drop down menu, and Frequencies from the pop up menu.): The frequencies dialog box will appear: Select the variable(s) that you want to analyze by clicking on it in the left hand pane of the frequencies dialog box. Then click on the arrow button to move the variable into the Variables pane: Be sure to select "Display frequency tables" if you want a frequency distribution. Specify which statistics you want to perform by clicking on the Statistics button. The Statistics dialog box will appear: From the statistics dialog box, click on the desired statistics that you want to perform. To calculate a given percentile, click in the box to the left of percentile(s). Type in the desired percentile and click on the Add button. When you have selected all the desired statistics (e.g. mean, median, mode, standard deviation, variance, ragne, etc.), click on the Continue button. Specify which chart you want to display by clicking on the Chart button. The chart dialog box will appear: Click on the desired chart (usually Histogram) and click on the Continue button. Click on OK in