Error # 1405 error message in spss

Error # 1405 error message in spss

I'm trying to open a data file that was originally created on SPSS v. 17. Someone else cleaned and prepared the data file in v. 18 and resent it back to me. Now I am unable to open the data file in either SPSS v. 17 or SPSS v. 13 (I have both programs). She even tried saving it in a lower level file (if that's possible) and this is the error message that I keep getting:

"Error # 1405 in column 8. Text: C:\(location name of data file is here) 
Error when attempting to get a data file. 
This command not executed."

I saw the previous post re: Error # 1405, but it doesn't seem to apply to our issue because we are both using Intel-based PC computers. 
If you can not find the specific file on you computer, you should download it from the Internet and locate it to the right place.
#2 To fix Runtime error 1405, you also can try to run free registry scan for your computer. In my opinion, it is the most important step for you to fix Runtime error 1405 and avoid other runtime errors effectively. There are various registry repair tools available on the Internet that scan, analyze, and fix your computer. Besides fixing your Runtime error 1405, these tools will remove any registry errors, invalid shortcuts, duplicate files and repair DLL files. Eventually, you computer will be free from system error messages and sluggish performance. What you need to do is to fix Runtime error 1405 as well as other registry errors to prevent your registry from total corruption. If you continue to run this damaged system and don't fix Run-time Error, this potential risk will bring you more troubles, Even system crash.

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Two-way Repeated Measures Analysis of Variance Using SPSS

Two-way Repeated Measures Analysis of Variance Using SPSS 

Overview: The instructions on this sheet cover two procedures: 
1. A one-way within-subjects ANOVA, used when you have one independent variable and one group of subjects measured repeatedly under 3 or more conditions. For example, subjects are measured in a baseline condition, are given a treatment, and are followed up at 3 later points in time. 
2. A 2-way mixed ANOVA, used when you have two independent variables with one within-subjects factor, and one between-subjects factor. The within-subjects factor is the repeated measures factor. On the between-subjects factor, subjects are divided into discrete subgroups, and each subject falls into only one of those subgroups.
To run: From the Data Editor Window 
Click on "Analyze" 
Click on "General Linear Model" 
Click on "Repeated Measures"

The following dialog box will appear:

Begin with the repeated measures factor. The first thing you need to do is to instruct SPSS to treat your repeated measurements not as different variables, but as different levels of the same variable. To do this click the box "Within-Subject Factor Name" and type in a name for your repeated measures variable.

Type in the number of observations you have on each subject in the box labeled, "Number of Levels" 
Click on "Add". The name of your new variable will appear in the box with the number of levels of that variable in parentheses.

Click on "Define" 
The following GLM - Repeated Measures dialog box will appear:

You must now tell SPSS what the different levels of your repeated measures factor are, that is, which variables from the column on the left represent your different levels. Click on each of the variables, i.e. baseline, time2, time3, time4 and click the right arrow key to move those variables to the box labeled, "Within-subjects Variables"

If you are running a one-way repeated measures ANOVA, you are done. 
Click "OK"

If you are running a two-way mixed ANOVA you need to indicate which variable is your between subjects variable. Click on that variable from the column on the left and click the right arrow to move it to the box on the right labeled, "Between-Subjects Factor(s):"

Click on "OK"

Output:

One-way repeated measures ANOVA 
The first table you see lists "Descriptive Statistics" for each of your groups, i.e., mean, standard deviation, and sample size. Note the sample size will be identical for all groups because the same subjects appear in each group.

Look at the table, "Tests of within-subjects effects." 
The first line of this table gives you your F, its degrees of freedom, and the probability of your F.

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Discriminant Analysis with SPSS

Discriminant Analysis with SPSS

Rather than working with pre-existing classifications of subjects, as the other tests in 
Chapter 9 do, a discriminant analysis attempts to create classifications. To conduct a 
discriminant analysis in SPSS, therefore, you cannot use the "General Linear Model" 
function. The following process allows you to use continuous values to predict subjects' 
group placements.
1. Choose the "Classify" option in SPSS Analyze pull-down menu. 
2. Identify your desired type of classification as "Discriminant." Choose "Discriminant" 
from the prompts given. A window entitled a window entitled Discriminant Analysis
should appear. 
FIGURE 9.9 –SPSS DISCRIMINANT ANALYSIS WINDOW
The user identifies the variables involved in a one-way discriminant analysis by selecting their names from 
those listed on the left side of the Discriminant Analysis window. SPSS performs the test using variables with 
names placed into the "Independents" and variables with names placed into the "Grouping Variables" box.The user identifies the variables involved in a one-way discriminant analysis by selecting their names from 
those listed on the left side of the Discriminant Analysis window. SPSS performs the test using variables with 
names placed into the "Independents" and variables with names placed into the "Grouping Variables" box.
3. In this window, you can define the variables involved in the analysis as follows
a. Move the name of the categorical dependent variable from the box on the left to the 
"Grouping Variable" box. You must also click on the "Define Range" button below 
this box and type the values for the lowest and highest dummy-variable values used 
to identify groups. 
b. Identify the continuous measure(s) used to predict subjects' categories by moving 
the names of the predictor(s) to the "Independents" box. 
4. Click OK.
The Discriminant Analysis' "Independents Variable" box allows you to identify more than 
one predictor of subjects' categories. Inputting more than one independent variable leads 
to a multiple discriminant analysis. The analysis presented in Chapter 9's examples, though, 
use a single independent variable.

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Paired Samples t Test in spss by using MedCalc

Paired Samples t Test in spss by using MedCalc

Paired Samples t Test in spss by using MedCalc
Paired-Sample T-Test is also known as dependent T-Test, repeated-measures T-test or within-subjects T-test. A Paired-sample t-test is used to analyse paired scores, specifically, we want to see if there is difference between paired scores.

Example Scenario
A new fitness program is devised for obese people. Each participant's weight was measured before and after the program to see if the fitness program is effective in reducing their weights.

In this example, our null hypothesis is that the program is not effective, i.e., there is no difference between the weight measured before and after the program. The alternative hypothesis is that the program is effective and the weight measured after is less than the weight measured before the program. The dataset can be obtained here.

In the data, the first column is the weight measured before the program and the second column is the weight after.

Step 1
Select "Analyze -> Compare Means -> Paired-Samples T Test".

A new window pops out. Drag the variable "Before" and "After" from the list on the left to the pair 1 variable 1 and variable 2 respectively, as shown below. Then click "OK".

Step 2
The results now pop out in the "Output" window.

Step 4
We can now interpret the result.

From A, since the p-value is 0.472, we reject the alternative hypothesis and conclude that the fitness program is not effective at 5% significant level.

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set up the mediation analysis in SPSS

set up the mediation analysis in SPSS

When thinking of mediation, it is helpful to understand that two models exist: One is theoretical, corresponding to unobservable relations among variables, and the other is empirical, corresponding to statistical analyses of actual data (MacCorquodale & Meehl 1948). The challenging task of research is to infer the true state of mediation from observations. There are qualifications even to this simple dichotomy, and in general, it will take a program of research to justify concluding that a third variable is a mediating variable.
Mediation in Psychological Research

In order to ascertain how often mediation is used in psychology, a search was conducted using the PsycInfo search engine for articles containing the word "mediation" in the title and citing the most widely cited article for mediation methods, Baron & Kenny (1986). This search yielded 291 references. Of these articles, 80 came from American Psychological Association (APA) journals. Publications earlier than the year 2000 were primarily APA sources, but there was a surge in non-APA articles after that time. The majority of these sources (239 citations) examined mediation alone, and 52 investigated both mediation and moderation effects. These studies included a mix of cross-sectional and longitudinal data, and ordinary least squares regression and structural equation modeling were the primary analytic methods. The articles covered a wide range of substantive areas, including social psychology (98 articles) and clinical psychology (70); a complete breakdown is listed in Table 1.
Table 1
Table 1
Subject area coverage in current mediation research

Mediation studies, such as those discussed above, are of two general but overlapping types. One type consists of investigating how a particular effect occurs. These studies usually occur after an observed X → Y relation is found. This approach stems from the elaboration methodologies outlined by Lazarsfeld (1955) and Hyman (1955). In this framework, a third variable is added to the analysis of an X → Y relation in order to improve understanding of the relation or to determine if the relation is spurious. A mediating variable improves understanding of such a relation because it is part of the causal sequence of X → M → Y. For example, physical abuse in early childhood is associated with violence later in life. One explanation of this pattern is that children exposed to physical violence acquire deviant patterns of processing social information that lead to later violent behavior. Dodge et al. (1990) found evidence for this theoretical mediating process because social processing measures explained the relation between early childhood physical abuse and later aggressive behavior.

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