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# SPSS - Science topic

Explore the latest questions and answers in SPSS, and find SPSS experts.

Questions related to SPSS

Hello everyone, I use IBM SPSS Statistics 29.0.2.0 and I have a list of the following dichotomous variables:

- Number of people who are self-employed Recoded,
- Number of people with multiple jobs,
- Number of household members who work full time,
- Number of household members who work part-time,
- Number of unemployed household members,
- Household members who are retired,
- Number of household members who are disabled,
- Members who are not working for some reason.

This is an example of how they are coded: {1.00, No household members who are self-employed} and {2.00, Household members who are self-employed}. Of course, each one has its corresponding values. Now, I want to compute all of these variables into one comprehensive dichotomous variable called: Household economic activity status with the following values: 1.00, Households with at least one Economically Active Member and 2.00, Households with at least one Economically Inactive member.

I tried running different codes on the syntax, but none worked. I would greatly appreciate any help on how to do this.

Thanks and best wishes,

Amina

I analysed reliability test on 5 point likert scale in SPSS, but value of Cronbach's Alpha =.697, not exceed after that when removed variables.

If there is a research model with one IV, one DV and one continuous moderator. So, how to check the parametric assumptions in SPSS?

**Include an Interaction Term:**

Create an interaction term by multiplying the IV and the moderator.

**Run the Moderated Regression Analysis:**

Set up a regression model where the DV is the outcome variable, and the IV, moderator, and interaction term are the predictors.

**Check Parametric Assumptions for the Model**

There is a research model with one IV, one DV and one mediator. So, when checking the parametric assumptions in SPSS, what will be done to the mediator?

Does the following answer is true?

**Path 1: IV to Mediator**

For the path where the IV predicts the mediator, the mediator is treated as the outcome.

- Run a simple regression with the IV as the predictor and the mediator as the dependent variable. Check parametric assumptions for this regression:
**Linearity****Normality of Residuals****Homoscedasticity****Independence of Errors**

**Path 2: Mediator to DV (and IV to DV)**

In this step, the mediator serves as a predictor of the DV, alongside the IV.

- Run a regression with the IV and the mediator as predictors and the DV as the outcome. Check assumptions for this second regression model:
**Linearity****Normality of Residuals****Homoscedasticity****Independence of Errors****Multicollinearity**

I am looking for a way to analyze repeated measurements.

I have data of subjects who have varying number of measurements (from 2 to 10) over fixed time periods (Week 1, week 2, etc)

The subjects are divided into two groups (A and B).

What I want to check in my analysis is:

1. For all subjects together, does Week1 differ from Week 2, Week 2 from Week 3, etc

2. Are the changes over time in group A different from the changes in group B? I expect from my data for the group A to have higher deltas between timepoints in comparison with group B.

Some issues with my data:

1. some values are missing for most individuals;

2. plotting the data over time reveals non-linear trend: There is a trend of increasing values during the first 3 weeks, and from weeks 3 to 10 - a decrease of the values;

3. measurements of different patients are quite variable (think body weight - from 50kg to 120 kg) - a wide variation

- Repeated measures ANOVA is not a good option as I understand because it cannot deal with missing data.

- Linear mixed models (LMM) seems to be a good fit, as it allows for missing data, and allows entering the subjects as a random factor (so each subject has their own intercept).

The problem I see is the slope - it is not linear.

I know SPSS does not have a non-linear mixed effects model at all and I am not skilled in any of the other statistical programs. Is there any other solution for my data or workaround to use LMM?

we need a research scholar who has done factor analysis before.- EFA,CFA,reliability and Validity cheeking of items, and stability in SPSS or SPSS Amose.

Hello everyone,

I hope this message finds you well. I am currently working on my doctoral thesis and have been using IBM SPSS for data analysis. However, my trial version has recently expired, and I am unable to access the software at the moment.

I am looking for alternatives or any possible ways to obtain a free version of SPSS, either through academic institutions or other resources. If anyone has any suggestions or can share experiences regarding accessing SPSS for free, I would greatly appreciate.

Thank you in advance for your help!

Best regards,
[Ferial]

I am comparing 2 groups' effect on 6 minute walk test scores. Now I want to find out if there is any indirect effect of age, gender and laboratory values on scores. But I am confused as scores are DV and gender is IDV. Regression does give OR but it does not define me which gender has the effect.

I collected data using cluster survey using kobo toolbox and imported into SPSS V. 26.

after coding all varibles I weighted them. the total collected sample is 1881. But after weighting the data using SPSS; data-weight by case; the total frequency change to 2420.

What do you recommed for the difference b/n the actual collected and weighted frequency?

I administered pre and post-achievement tests on one sample (N=16), where I obtained two paired variables and intended to run a paired sample T-test. However, while pre-test data are normally distributed, post-test data are not. Should I go for the Wilcoxon signed-rank test on SPSS?

Hello, fellow researchers! I'm hoping to find someone well familiar with Firth's logistic regression. I am trying to analyse whether certain emotions predict behaviour. My outcomes are 'approached', 'withdrew', & 'accepted' - all coded 1/0 & tested individually. However, in some conditions the outcome behaviour is a rare event, leading to extremely low cell frequencies for my 1's, so I decided to use Firth's method instead of standard logistic regression.

However, I can't get the data to converge & get warning messages (see below). I've tried to reduce predictors (from 5 to 2) and increase iterations to 300, but no change. My understanding of logistic regression is superficial so I have felt too uncertain to adjust the step size. I'm also not sure how much I can increase iterations. The warning on NAs introduced by coercion I have ignored (as per advice on the web) as all data looks fine in data view.

My skill-set is only a very 'rusty' python coding, so I can't use other systems. Any SPSS friendly help would be greatly appreciated!

***

Warning messages:

1: In dofirth(dep = "Approach_Binom", indep = list("Resent", "Anger"), :

NAs introduced by coercion

2: In options(stringsAsFactors = TRUE) :

'options(stringsAsFactors = TRUE)' is deprecated and will be disabled

3: In (function (formula, data, pl = TRUE, alpha = 0.05, control, plcontrol, :

logistf.fit: Maximum number of iterations for full model exceeded. Try to increase the number of iterations or alter step size by passing 'logistf.control(maxit=..., maxstep=...)' to parameter control

4: In (function (formula, data, pl = TRUE, alpha = 0.05, control, plcontrol, :

logistf.fit: Maximum number of iterations for null model exceeded. Try to increase the number of iterations or alter step size by passing 'logistf.control(maxit=..., maxstep=...)' to parameter control

5: In (function (formula, data, pl = TRUE, alpha = 0.05, control, plcontrol, :

Nonconverged PL confidence limits: maximum number of iterations for variables: (Intercept), Resent, Anger exceeded. Try to increase the number of iterations by passing 'logistpl.control(maxit=...)' to parameter plcontrol

Is there a difference on the result of factor loading when you use SPSS or JASP?

Hi,

I have conducted an RCT (psychological intervention) where participants were randomised to 1 of 3 conditions.

Initially I conducted a per-protocol analysis but now I would like to do an intention to treat analysis and compare the results. The issue is that there is a lot of missing data (about 19%) and the pattern is MNAR.

What is the best approach to doing an intention to treat analysis in this situation? Can I still conduct the analysis with the missing data (not replacing them)?

I am pursuing Ph.D. in Environmental Sciences from Sambalpur University, Odisha, India. I am specialised in water and soil analysis. I use SPSS and MS-excel for preparation of graphs and other statistical analysis. However, I find myself lacking in data representation in my research articles. I want to improve visibility of my research papers through R programming language. Please suggest a few study materials, including YouTube videos for quick learning.

What are the processes to extract ASI microdata with STATA and SPSS?

I have microdata in STATA and SPSS formats. I want to know about the process. Is there any tutorial on youtube for ASI microdata?

*Hello everyone,*

I have some questions regarding the use of these two effect sizes, and I am a bit uncertain about which one to choose for t-tests, independent t-tests, pairwise comparisons, or non-parametric tests.

The primary difference between Cohen’s d and Hedges’ g is that Cohen’s d divides by the pooled standard deviation, while Hedges’ g uses the sample size (N-1) in the denominator (https://doi.org/10.1037/h0087427). From this perspective, Hedges’ g may be more efficient to use, as the standard deviation values are typically reported in studies.

Furthermore, based on various opinions:

- For smaller sample sizes, Hedges’ g is considered more appropriate.

- For samples of unequal sizes, Hedges’ g may provide more accurate estimates.

However, many online calculation tools allow the use of Cohen’s d even for unequal sample sizes, and the standard deviation values seem sufficient for calculating Cohen’s d as well.

*I would appreciate your thoughts and insights on this matter. Thank you in advance for your contributions.*

*BERMAN.*

Im currently conducting a study on factors that may influence the chances of patients having delirium post-surgery. I have around 30 variables that have been collected including continous (HB, HBA1c, urea levels (pre-op, peri-operatively and urea difference), alcohol audit c score, CPB duration etc), categorical (blood transfusion - yes/no, smoking status, drinking status, surgery type etc) and demographic information (gender, age, ethnicity). The study also looks at whether our current measurements of risk of delirium are good predictors of actual delirium (the DEAR score, consists of 5 yes/no questions and a final total score).

As with many studies using data from previous patients (around 750), there are a lot of missing information in many categories. I have already been conducting assumption tests including testing the linearity of the logs and this has excluded some variables. I am using SPSS if anyone knows of anything on these systems i can use.

QUESTIONS:

1. (More of a clarification) I have not been using the pre-op, peri-op and difference between urea levels scores in the same models as i assume this violates the assumption of independency between variables - this is correct yes? If so, I assume that other variables that measure the same thing in different ways (e.g., age at time of surgery and the delirium risk quesiton that asks if patients are over 80) should also be excluded from the same model, and instead test the model with each difference and select the strongest model for prediction?

2. What should i do with my missing data? There is a big chunk (around 50% of the 750ish patients included) that dont have a delirium risk score - should i only conduct my model with patients that have a score if im investigating the validity of the DEAR score for predicting delirium or will SPSS select these case automatically for me? Other missing data includes HBA1c (because we do not test every patient), ethnicity (as the patient has not declared their ethnicity on the system), Drinking status (no audit c score made for the patient as they either dont drink or were not asked about their drinking status) etc... I've seen some chat about using a theory to generate predictions for the missing information but I feel like using this for example, for gender wouldnt be sensible as my population is heavily male centric.

3. Part of our hypothesis is identifying a model of prediction for males and females separately if they show different significant influences on chances of delirium. Can i simply split my data files by gender and conduct the regression that way to get different models for each gender? When i have done this, I have not used gender as a variable in the regression, but have tested it with all the data and found a significant influence of gender but only when tested with age and ethnicity or on its own (not in a model that includes all of my variables, or in a model that includes only the significant variables determined from testing various models). Should I just ignore gender all together?

Sorry for what may seem to be very silly or 'dealers choice' questions - I am not very experienced with studies with this many variables or cases and usually have full data sets (normally collect data in the here and now, not based on previous patients).

Any help or suggestions would be much appreciated!

Hello everyone,

I’m seeking advice on the best approach for analyzing data related to ESL learners’ self-regulation capacities in vocabulary learning, specifically in relation to socio-demographic variables like age, gender, major, and IELTS scores (Comparison). I just wanted to detect differences in self-regualtion and background variables without hypotheses.

Initially, I used MANOVA to identify overall differences across these variables, focusing on five dimensions of self-regulation. I conducted separate MANOVAs for each variable (e.g., age, gender) with the five domains of self-regualtion and then summarized the results in a comprehensive table.

Additionally, I performed one-way ANOVAs for each socio-demographic variable across the five domains, which yielded insightful and significant differences. After I wrote all the results and discussion, however, my supervisor suggested a different approach: using a single MANOVA model that includes all socio-demographic variables simultaneously and all self-regualtion domains. This method produced different results, highlighting only some differences in age and English levels. My supervisor is particularly interested in examining interaction effects.

The challenge is that my sample consists of approximately 550 participants, but the distribution is uneven across specific age groups and proficiency levels, leading to some cells being empty in the analysis of interaction effects. This imbalance complicates the analysis, and I’m torn between the two approaches. Even the discusstion becomes more challenging.

Personally, I favor the first approach because I didn't hypothesize any interaction effects, and the unequal sample sizes across groups make the results from the second approach less reliable. Additionally, the findings from the first approach are more interesting and aligned with my research objectives.

Given these considerations, I’m concerned about which method is more appropriate. I would greatly appreciate any insights or suggestions on how to proceed with this analysis. Note, I have both analysis and discussion in sperate files written but I am concerned what is the best for my thesis.

Thank you in advance for your help!

Q1: I analyzed my data using a nonparametric test, and I have 3 covariate variables. How can I test their effects on my study? Should I do first the nonparametric test and then do another analysis to test the covariate variables ( do all three together or separately every covariate)

Q2: When I analyzed my serial mediation effect by Hayes -SPSS, The X Variable is group 1, so I got the message that there was an error because the X variable consists of a constant variable( What should I put in X variable? ) ( I have to test the effect of group 1 on DV by serial mediation variables; also, I have to do it again with group 2 because it has different serial mediation variables )

Thank You

Hi there,

I was looking for a scoring guide or SPSS/Stata/R syntax for scoring SF 12 version-2. Can anyone help me in this regard? My email address is m.alimam@cqu.edu.au

Thanks in advance.

I am carrying out a research on patients with sarcopenia related to fracture rate, using SF-12 version 2 as the QoL tool.

I was wondering if anyone is using the same questionnaire and calculate the scores using SPSS syntax? Thank you very much!

Similar to this poster (https://www.researchgate.net/post/Is_there_a_non-parametric_alternative_to_repeated_measures_ANOVA), I'm trying to run an RM test with non-parametric continuous data, and am now interested in a between-subjects factor (sex). Since the big limitation of the Friedman test is an inability to include between-subjects factors, I was led to GEE, but the process appears complex. Does anyone know of any straightforward guides/steps to run this kind of analysis?

Dear Researchers:

when we do the regression analysis by using SPSS, when we want to measure a specific variable, some researchers take the average of items under each measurement while some others add the value of each items ? Which one is more reliable? which one produces more better results ?

Thanks in advance

Hello everyone!

I am writing my master thesis together with a fellow student and we are having problems analyzing our data.

We conducted a daily diary study. Our model is a mediation model with the following variables:

- Trait self-control (predictor, estimated at the beginning, level 2)

- Digital Media Self-Control Failure (mediator, estimated via daily diary - evening questionnaire, 5 days, level 1)

- Goal pursuit (outcome, estimated via daily diary - evening questionnaire, 5 days, level 1)

We are really unsure how to analyze the data (since we didn't have HLM or multilevel medation in our statistics lecture).

We think the lme4 package for R might be suitable for our analysis, or perhaps Rockwood's MLMED macro for SPSS.

What we have found out so far is that we need to center our mediator using CWC. We also think that we need an intercept only model to test the ICC. After that, we are not so sure what to do. We found some literature for HLM and for 2-1-1 Mediation, but nothing that explained what to in R/SPSS or how to it. We are really lost at the moment.

I'm really scared that I might fail my master thesis because we are not able to the analysis.

I really hope that someone has an idea or some other input that can help us.

Thank you so much!

Sophie Kittlaus

Then to compare it if there a significant difference between the two independant groups ?

what is the difference between (path Non parametric - > Independent samples then select Mann Whitney) and path Non parametric -> legacy -> 2 independent samples?

Because , while doing both , calculated value of Mann-Whitney is differ in both output.

Is there any different approaches?

I would like to learn more about SPSS and Its application especially in regards to data analysis. Please suggest me how I can learn more about it.

Thank you so much.

Are there any statistical methods to justify your sampling technique using SPSS or AMOS?

Using SPSS, I made my edits in one bar chart — e.g., font type and size, hiding grid lines, and colours, namely: blue, orange, green, purple, and grey for 5 bars, respectively, etc. — and I saved the chart as a template to apply to the remaining charts in my data/item analysis. When I analysed another item (from the same questionnaire section) and applied the saved template to it, the font features and grid lines applied successfully; however, the colors part did not take effect (and graph bars remained a default blue), although I thought I made no mistake in the template saving procedure.

Doesn't SPSS save bar colors in templates or have I made a mistake?

I want to use SPSS Amos to calculate SEM because I use SPSS for my statistical analysis. I have already found some workarounds, but they are not useful for me. For example, using a correlation matrix where the weights are already applied seems way too confusing to me and is really error prone since I have a large dataset. I already thought about using Lavaan with SPSS, because I read somewhere that you can apply weights in the syntax in Lavaan. But I don't know if this is true and if it will work with SPSS. Furthermore, to be honest, I'm not too keen on learning another syntax again.

So I hope I'm not the first person who has problems adding weights in Amos (or SEM in general) - if you have any ideas or workarounds I'll be forever grateful! :)

Afternoon!

Using SPSS, I made my edits in one bar chart—e.g., font type and size, hiding grid lines, and colours, namely: blue, orange, green, purple, and grey for 5 bars, respectively, etc.—and I saved the chart as a template to apply to the remaining charts in my data/item analysis. When I analysed another item (from the same questionnaire section) and applied the saved template to it, the font features and grid lines applied successfully; however, the colour part did not take effect (and graph bars remained a default blue), although I thought I made no mistake in the template saving procedure.

Doesn't SPSS save bar colours in templates, or have I made a mistake?

I am conducting my analysis using SPSS. I log transformed my data using In(X+1) as my data contain zero values. However, when I want to back transform the regression coefficients generated from my regression analyses, I encountered the following problem:

- I saw online that back transformation of In(X+1) can be done by (e^y )-1. However, since the regression coefficients generated from my log transformed data is between the value of 0 to 2 (like say b= 0.15), so after I back-transform it (exp(0.15) = 1.16) and then minus the answer by 1 (i.e. 1.16 - 1 = 0.16), the back-transformed value become less than 1. I read in a paper saying that a back-transformed value below 1.0 would correspond to a decease. Therefore, backtransformation using (e^y )-1 make my data seemed like each one-unit increase in X, the dependent variable Y is
**decreased**by ...%, but the fact is that with each one-unit increase in X, the dependent variable Y should be "**increased**" by...%. Therefore, is this the correct way to back-transform the data? If not, how should i do it? - Besides, some of the regression coefficients generated from log transformed data is less than 0 (e.g. -0.15). Should I ignore the negative sign, then back-transform it (i.e. exp(0.5) = 1.65), and add back a negative sign? Or should I do it in other way?
- To be honest, only 0.5% of the responses in my data is 0 and I have more than 1000 responses, so to simplify thing, do you think it is appropriate to use In(X) instead?

Thank you in advance for your help.

I am conducting a qualitative-driven approach to mixed-method research. The role of my quantitative data is to corroborate the findings of the qualitative data. Qualitative data has been collected through interviews at the end of the project, and quantitative with a pre-survey before the project and a post-survey at the end of the project. One of the questions that arises when analyzing the quantitative data from my first project is whether I should use a paired t-test or an unpaired t-test analysis. In the first project, I had a small group of participants: the same 18 participants responded to both the pre- and post-questionnaires, but in total, I have 28 participants; the remaining 10 only responded to either the pre- or the post-questionnaire. I am unsure if 28 participants are enough for an unpaired t-test; however, 18 participants might be enough for a paired t-test. I would appreciate your help regarding this question and any resources or videos I can use to analyse pre- and post-questionnaires with SPSS, as I am unsure if I need to upload all the data from the pre-and post-questionnaire in the same file in SPSS or separately. Thanks very much.

Hello network,

Is there anyone who could help me with my empirical research - especially with the statistical elaboration - on the topic of entrepreneurial intention and business succession in German SMEs, or who has experience with the preparation of Structural Equation Modeling?

Please feel free to send me a private message.

Thank you and best regards

Julia

Looking for international collaboration, someone with deep understanding of SPSS (Specifically on Difference in difference model both,DID and SDID).

I'm evaluating the reproductive performance of quails. I have the fertility, and hatchability percentages for six different sire groups and would like to determine the significant difference between the percentages of the sires using SPSS. I'd appreciate it if the steps are shared.

Video tutorial will be effective

Hi everyone,

I am working on case control genetic data. I have data for SNP with genotypes AA, AG and GG. I want to check the effect of these individual genotypes on the disease outcome, which in my case is diabetes. Now I want to calculate unadjusted and adjusted (for age and gender) Odds ratio in SPSS. I calculated unadjusted Odds ratio by using multinomial regression (is this suitable for my data?). But do not know how to calculate Odds ratio after adjustment for age and geneder.

Secondly, How can I apply dominant, recessive and co-dominant model to check which fits more.

I would be highly obliged if you provide me the flowchart of commands like

Spss--> analyze--> regression--> .........

Thanks,

Misbah

Dear all,

For my study, I ran a multiple regression with robust standard error, and a clustered multiple regression with robust standard error (using Huang's (2020) SPSS macro).

I noticed that the Rsquare of the simple multiple regression was the same as that of the robust standard error regression.

But when I run the regression with the clustered robust standard error, I don't get the Rsquare value. I was wondering if this is the same as for regression with robust standard error?

If the Rsquare doesn't change with different adjustments of the standard error ( robust standard error, or cluster robust standard error ) , could someone explain the idea behind it?

If not, could you tell me how to get the “Model Summary” output containing the Rsquare for the clustered robust standard error regression?

I am using IBM SPSS version 27

Thanks in advance,

Hello everyone,

I am a physician and interested in research. I wanted to know for beginners which one you recommend? spss vs stata- your response will be much appreciate- thank you

I did a moderation analysis using SPSS and Process macro model 3, whereby the interaction between X Z W was not significant, but the interaction between X and Z was. I am confused in how to interpret this, could someone please help me get back a grip on this?

I ran multiple linear regression in SPSS and it turned out that one of the variables was not statistically significant.

multiple regression equation formula, if X1 not significant at (P<0.05), Can this variable be entered into the equation?

Y = A+B1X1+B2X2,......

Hello,

I would really appreciate some help interpreting the output from my simple mediation analysis using PROCESS macro in SPSS please.

For context, the X predictor is severity of nausea and emesis in pregnancy (PUQE), the hypothesised mediator M is emetophobia (SPOVI), and the outcome Y is parental stress (PSS).

I believe the overall indirect effect of X on Y is significant, [Effect = .1094, 95% C.I (.0070, .2603)] however, when you look at the significance of the individual paths,

*a =*1.1232 (.3070) is significant at*p*= .0003, but*b*= .0974 (.0532) does not reach statistical significance at*p*= .0686.I am stuck as to how to best interpret this result - is it the overall indirect effect that matters, or the significance of the individual paths? Why would one be significant but not the other?

I have attached the full output, thank you in advance for any help/insight.

Hello,

I am writing a thesis where I want to test the moderation effect of 4 groups (that I transformed into 3 dummies), on the main impact of the independent variable on the dependent one. The 3 dummies are : Female Senior, Female Adult and Male Senior (the baseline being Male Adult). However, I do not know if I am allowed to just test the moderation of the 3 of them separately and just compare their coefficients or if I should put the 3 dummies together in PROCESS. If the latter is true, I do not know how to do this.

Could you please let me know if I should put the 3 of them together, and if so, how?

Thank you for your help!

Each row is a therapy session by a variable label ID. Each unique ID has between 1 and 50 rows in SPSS (sessions). How can I select only those unique IDs with 3 or more sessions?

Hi everyone,

Does anyone have a detailed SPSS (v. 29) guide on how to conduct Generalised Linear Mixed Models?

Thanks in advance!

Hello,

If I have the ROC curve from SPSS output, How can I determine the cut off?

In SPSS 25, I have a categorical variable which I would like to display in a frequency table. However, one of my categories was not selected by any of my respondents. I can generate a frequency distribution for this variable, but the unselected category is not included. How can I generate a frequency distribution table to show a zero count for this category?

I plan to apply multinomial logistic regression using the complex sample option of SPSS. The dependent variables have 04 categories (low, moderate, high, and very high), and 05 independent variables are classified/categorized as Yes/no. The 'low' category of dependent variable will be the reference. 'No' will be the reference category of each independent variable.

I plan to apply multinomial logistic regression using the complex sample option of SPSS. The dependent variables have 04 categories (low, moderate, high, and very high), and 05 independent variables are classified/categorized as Yes/no. The 'low' category of dependent variable will be the reference. 'No' will be the reference category of each independent variable.

In my study i have 2 groups of patient (diagnosed with stage 1 and 2 at the beginning of treatment), where dependent variable is "lesion volume", which was measured before and after treatment. Treatment time for each patient is not the same. I want to compare the response to treatment between 2 groups.

What is the best statistical test to use to compare compare the response of lesion volume to treatment? Is Mixed ANOVA test a possible test for my study?

Dear Colleagues

I carried out a multinomial logistic regression to predict the choice of three tenses based on the predictor variables as shown on the image. According to the SPSS output below, the predictor variable "ReportingVoice" appears to have the same result as the intercept. I wonder why this issue happens and how I should deal with this problem. Thank you for your help. Please note that I'm not good at statistics, so your detailed explanation is very much appreciated.

Dear All,

I need your help :)

For my thesis, I've done an experiment and I need to analyze it on SPSS.

Specifically, I have 112 participants who had to read 7 different scenarios, and after reading the scenario, they had to indicate their degree of agreement on an ascetic scale with five different statements that were intended to measure my dependent variables. The aim is to analyze how the different scenarios impact the dependent variables.

independent variables: scenario 1, scenario 2, scenario 3, scenario 4, scenario 5, scenario 6, and scenario 7

dependent variables: motivation, satisfaction, collaboration and help.

control varibale : gender( male, female ), age and status ( employed, unemployed, student , self-employed, retired, other )

My supervisor advised me to do a multiple linear regression that took into account the clustered standard error ( in order to control for individual heterogeneity). He explained that this would take into account my design within subject.

Unfortunately, I'm a beginner in statistics and this is the first time I've used Spss, so I can't figure out how I could permofer a test that would take clustered standard error into account.

Could you please help me and explain how to do it on spss?

Hello every one,

I run binary logistic regression in SPSS but i did not have results because of complete separation. How can i solve this proplem?

Thanks in advance.

In my research, I have two independent variables (one is within-subject, another is between subject) and a binary dependent variable (i.e. yes or no).

As I have two IVs and one of the them is a repeated measure, I cannot conduct analyses such as chi-square and logistic regression in SPSS. I came across an article saying that for analysis purpose, we can sometimes treat a binary variable as continuous, but in certain circumstances, treating a binary variable as continuous can affect the interpretation of result.

Therefore, I am whether in my case, can I treat my binary DV as continuous and conduct mixed model ANOVA? Can I interpret the result the same way as if the DV is continuous? Thank you in advance for your assistance.

I am running a PCA in JASP and SPSS with the same settings, however, the PCA in SPSS shows some factors with negative value, while in JASP all of them are positive.

In addition, when running a EFA in JASP, it allows me to provide results with Maximum Loading, whle SPSS does not. JASP goes so far with the EFA that I can choose to extract 3 factors and somehow get the results that one would have expected from previous researches. However, SPSS does not run under Maximum Loading setting, regardless of setting it to 3 factors or Eigenvalue.

Has anyone come across the same problem?

UPDATE: Screenshots were updated. EFA also shows results on SPSS, just without cumulative values, because value(s) are over 1. But why the difference in positive and negative factor loadings between JASP and SPSS.

Im testing the change in lesion volume between disease stages. I have measured lesion at T0 and T1, and calculated the change in volume with formula:

(VolumeT1 - VolumeT0) / VolumeT0 * 100

In my sample, there several subjects that has increase in volume (positive number) and several subjects has decrease in volume (negative number).

Can I just input all these mixed value into SPSS? and does these mixed values affect the distribution of my data set and affect the choice between parametric and nonparametric tests? Thank you so much

I have fit a Multinomial Logistic Regression (NOMREG) model in SPSS, and have a table of parameter estimates. How are these parameter estimates used to compute the predicted probabilities for each category (including the reference value) of the dependent variable?

I have a question. I made a comparison between women and men on a series of attitudes concerning romantic relationships. Significant differences were found in a line of T-tests, but I also wanted to say something about the great similarity between the two groups in relation to many issues such as recovery time from separation, responsibility for keeping in touch, and more.

Correct me if I'm wrong, but the places where similarity is found, or no difference is found between women and men, is an important finding no less than significant results in a t-test for differences

Now here is the question

What measure of similarity between groups do we have? Is there is a procedure in SPSS that can give an idea of the extent of similarity

Or should I assume that everything that has no difference has similarity? And how do you claim it?

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11כל הרגשות:

Hello fellow researchers,

In my research, I investigate two members of the same household. The members of the same household share the same ID number (Nomem_encr). I want to retain household pairs where one member is the household head (position=1) and the other member is the residing child (position=5). Currently, alongside the pairs I desire (position=1 and position=5), I also have household pairs where both members are household heads (position=1 and position=1) or both are residing children (position=5 and position=5). How can I remove these unwanted pairs without losing my desired pairs using SPSS?

Kind Regards,

Raquel

how to do calibration curves for the prediction models in SPSS, i think it is important in addition to the external validation

I'm the student of M.Phil English, my interest area is to explore teachers perception on implementing inquiry based teaching. I need your guidance regarding theory or theoratical model. In my base paper there is Constructivist theory along with mixed method qualitative and quantitative along with interviews and questionnaire survey. The researcher has not used smart Pls or SPSS to run the data. What'll be suitable for me? Which theory or theoretical model I should use

Hello,

I have data in SPSS from a scale I did, I have 1 variable which is age (separated into 3 categories using values of 1-3), a 2nd variable which is gender (again separated into 3 categories using value), and finally, I have a 3rd IV of education level (this is separated into 5 categories), I need to compare each category against their scores on a 10 item scale.

I am measuring attitudes, each person completed the same scale and I have input them into SPSS but I am unsure which test I need to do to see if there is a difference between each variable's category and their scale scores.

In my research, I have 11 multiple-choice questions about environmental knowledge, each question with one correct option, three incorrect options, and one "I don't know" option (5 options in total). When I coded my data into SPSS (1 for correct and 0 for incorrect responses) and ran a reliability analysis (Cronbach's Alpha), it was around 0,330. I also ran a KR20 analysis since the data is dichotomous but still not over 0,70.

These eleven questions have been used in previous research, and when I checked them, they all stated a reliability over 0,80 with a similar sampling to the sampling of my research. This got me thinking whether I was doing something wrong.

Low reliability might be caused by each question measuring knowledge from different environmental topics? If this is the case, do I still have to state its reliability when using the results in my study? For example, I can give correct and incorrect response percentages, calculate the sum points, etc.

Thank you!

I am using IBM SPSS version 21 as a statistical analysis software. My research is about comparing 2 diferent populations. Let's say it's group A and group B.

Each group has variables changing between 2 different timelines : T0 and T1, and these variables are qualitative. Let's say one of these variables is called X.

X is coded either 0 for no, or 1 for yes.

As a qualitative variable, the frequency of X is calculated in percentage by SPSS.

The difference between the frequencies in T0 and T1 is calculated manually by this formula : ( Freq of X(T0) in group A - Freq of X (T1) in group A )/ Freq of X(T0) in group A * 100

So we obtain the variation between these 2 timelines in percentage.

**My question is : how do i compare the variation of group A versus the variation of group B between these two timelines (T0 and T1) using SPSS ?**

Hi! My hypothesis have 2 Likert scaled variables to check the effect on one dichotomous dependent variable. Which test to put in SPSS? Can the dichotomous variable later be checked as a DV in mediation analyses?

1. Is Smartpls the only way to deal with formative constructs?

If we have formative constructs, cant we use SPSS and AMOS? If not, that means that all the studies done on SPSS and AMOS are having only reflective constructs (having similar meaning items)?

2. What to do if i have formative constructs, but want to do EFA & CFA?. Can I do ANOVA, MANOVA and other multivariate tests on formative constructs?