How To Find Standard Error In 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,
Standard Error Of Measurement Spss
math, reading and social studies (socst). The variable female is a dichotomous variable coded interpreting mean and standard deviation results 1 if the student was female and 0 if male. In the syntax below, the get file command is used to load how to interpret mean and standard deviation in research the data into SPSS. In quotes, you need to specify where the data file is located on your computer. Remember that you need to use the .sav extension and that you need to end the command
How To Interpret Descriptive Statistics In Spss
(and all commands) with a period. There are several commands that you can use to get descriptive statistics for 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
Interpretation Of Mean And Standard Deviation In Descriptive Statistics
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 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
performs t-tests for one sample, two samples and paired observations. The single-sample t-test compares the mean of the sample to a given number (which you supply). The independent samples t-test compares the difference in the means from the two groups to a how to calculate standard error of measurement in excel given value (usually 0). In other words, it tests whether the difference in the means is
How To Report Descriptive Statistics From Spss
0. The dependent-sample or paired t-test compares the difference in the means from the two variables measured on the same set of subjects to how to interpret mean in spss a given number (usually 0), while taking into account the fact that the scores are not independent. In our examples, we will use the hsb2 data set. Single sample t-test The single sample t-test tests the null hypothesis that the http://www.ats.ucla.edu/stat/spss/output/descriptives.htm population mean is equal to the number specified by the user. SPSS calculates the t-statistic and its p-value under the assumption that the sample comes from an approximately normal distribution. If the p-value associated with the t-test is small (0.05 is often used as the threshold), there is evidence that the mean is different from the hypothesized value. If the p-value associated with the t-test is not small (p > 0.05), then the null hypothesis is not rejected and you http://www.ats.ucla.edu/stat/spss/output/Spss_ttest.htm can conclude that the mean is not different from the hypothesized value. In this example, the t-statistic is 4.140 with 199 degrees of freedom. The corresponding two-tailed p-value is .000, which is less than 0.05. We conclude that the mean of variable write is different from 50. get file "C:\hsb2.sav". t-test /testval=50 variables=write. One-Sample Statistics a. - This is the list of variables. Each variable that was listed on the variables= statement in the above code will have its own line in this part of the output. b. N - This is the number of valid (i.e., non-missing) observations used in calculating the t-test. c. Mean - This is the mean of the variable. d. Std. Deviation - This is the standard deviation of the variable. e. Std. Error Mean - This is the estimated standard deviation of the sample mean. If we drew repeated samples of size 200, we would expect the standard deviation of the sample means to be close to the standard error. The standard deviation of the distribution of sample mean is estimated as the standard deviation of the sample divided by the square root of sample size: 9.47859/(sqrt(200)) = .67024. Test statistics f. - This identifies the variables. Each variable that was listed on the variables= statement will have its own line in this part of the output. If a variables= statement is not specified, t-test will conduct a t-test on all numerical vari
Is It? The standard deviation is a number that indicates the extent to which a set of numbers lie apart. Standard Deviation - Example Five applicants took an IQ test as part of a http://www.spss-tutorials.com/standard-deviation-what-is-it/ job application. Their scores on three IQ components are shown below. Now, let's http://stats.stackexchange.com/questions/9312/how-to-compute-the-standard-error-of-measurement-sem-from-a-reliability-estima take a close look at the scores on the 3 IQ components. Note that all three have a mean of 100 over our 5 applicants. However, the scores on “iq_verbal” lie closer together than the scores on “iq_math”. Furthermore, the scores on “iq_spatial” lie further apart than the scores on the first two components. how to The precise extent to which a number of scores lie apart can be expressed as a number. This number is known as the standard deviation. Standard Deviation - Results In real life, we obviously don't visually inspect raw scores in order to see how far they lie apart. Instead, we'll simply have some software calculate them for us (more on that later). The table below shows the mean and standard standard deviations and some other statistics for our IQ data. Note that the standard deviations confirm the pattern we saw in the raw data. Standard Deviation and Histogram Right, let's make things a bit more visual. The figure below shows the standard deviations and the histograms for our IQ scores. Note that each bar represents the score of 1 applicant on 1 IQ component. Once again, we see that the standard deviations indicate the extent to which the scores lie apart. Standard Deviation - More Histograms When we visualize data on just a handful of observations as in the previous figure, we easily see a clear picture. For a more realistic example, we'll present histograms for 1,000 observations below. Importantly, these histograms have identical scales; for each histogram, one centimeter on the x-axis corresponds to some 40 ‘IQ component points’. Note how the histograms allow for rough estimates of standard deviations. ‘Wider’ histograms indicate larger standard deviations; the scores (x-axis) lie further apart. Since all histograms have identical surface areas (corresponding to 1,000 observations), higher standard deviations are also associated with ‘lower’ histograms. Standard Deviation - Population Formula So how does your software calculate standard deviations? Well, the b
Tour Start here for a quick overview of the site Help Center Detailed answers to any questions you might have Meta Discuss the workings and policies of this site About Us Learn more about Stack Overflow the company Business Learn more about hiring developers or posting ads with us Cross Validated Questions Tags Users Badges Unanswered Ask Question _ Cross Validated is a question and answer site for people interested in statistics, 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 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, $R_1$ 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♦ 37.5k6125243 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 my comment, you might be interested in this recent article: Tighe et al. The standard error of measurement is a more