Questions related to SPSS
I have a questionnaire with questions that measure one variable. The questions have the Likert scale format. I want to check convergent or discriminant validity for the questionnaire. How can I carry out it?
Result of inclusion of an independent variable (continuous) when included, among others, in R, says
algorithm did not converge
fitted probabilities numerically 0 or 1 occurred
When done similarly in SPSS gives p values of 0.99 or 1 for all variables and also for the constant.
But when the variable is removed, this gives reasonable-seeming p values for all other variables included (categorical and continuous). The results of p values and estimates are same in R and SPSS in this case.
The relationship of the very independent variable with the dependent variable is:
I am currently doing an analysis in SPSS using Hayes Process Model Nr. 21. I have one multicategorical moderator (W) and one continuous moderator (Z). Even though my output gives me significant results regarding the moderation interactions of X*W and M*Z, the output does not show me an index for the moderated moderated mediation.
Hayes itself states: “If your model has more than one moderator, an indirect effect may be a function of two moderators simultaneously, in which case no index is provided. “
But how can I then be sure that the moderated moderated mediation worked out if the index is not provided?
I am looking forward to your advice,
This is the final model.
Log (odds of discontinuing exclusive breastfeeding) = -4.259+0.850 superior support+0.802 sufficient duration to express breastmilk.
This is my first time of attempting to run survival analysis. I have a data on patients with End-stage Kidney Diseases (ESKD). I would want to run survival probability by treatment method (eg conservative therapy vs dialysis), duration of diagnosis etc
I've got 2 independent sample groups (firm ownership, dummy), and a DV (Pay level, continuous), significance for the t-test is 0,055. I would like to improve the results by introducing controls and moderators in the model.
I wanted to ask how can I check for possible moderation effect in SPSS ( moderators are size, performance, etc) for those groups, and would it still make sense to try, if they already didn't show any significance as control variables with ANCOVA? Thank you
I am operating data for establishing the norms for Likert 7-point scale. I was suggested to use z-norms. But while computing the standard scores in SPSS, I got the value +2.15 and -1.96 (instead of ± 3). And while checking the data, it showed that the data do not follow the normality. In this case, how do I set the norms for interpretation?
My sample sizes are 41 and 12 respectively and are normally distributed, continuous data, and randomly selected. However the means for both sample sizes (I even did a combined sample of 41 and 12) are above the mean score that is being compared to. Both have a standard deviation of around 20. I am using SPSS. My data: administered a survey to two groups (two languages) and language one, 41 people replied and language two, 12 people replied. Thus, my first sample size is statistically significant and my second sample size is not.
I am comparing to a mean of 60 and the sample size of 41 yields a mean of 80 and the sample size of 12 yields a mean of 88. When running a one sample t test respectively on both sample sizes, my significance is < .05 which means H0 is rejected (means are the same in comparison to the compared value). Yet doing a two sample t test yields a significance that is > .05 which means H0 is accepted but this would not make sense since a two sample t test gives me a mean that is much higher than 60. Any advice on how to proceed with statistical analysis?
the two tailed/independent samples t test on SPSS tells me in the equal variances assumed row that the significance is > .05. The row beneath it is equal variances not assumed and there is no value for f or significance. To my understanding, if Levine's says significance > .05 I use equal variance assumed and that same significance is telling me that it is not significantly different to the mean value of 60 since it is > .05. This still does not make sense. In this case what am I concluding in respect to the mean value I am comparing my data to?
I am running a ANOVA analysis including Maine effects and interaction effects. The output shows, that SPSS excludes all main effects from the analysis. How come?
I've noticed that there is a lot of material on how to run GLMMs on R, yet not a lot on SPSS.
Can anyone shed any light or material that could be helpful?
I need help on running it, analysing and reporting on SPSS - this is for my MSc dissertation
kindly assist me in writing and interpreting results of MODERATION (gender, age etc.) .
I used SPSS Split file option and there after used FREE STATISTICS CALCULATORS ( https://www.danielsoper.com/statcalc/default.aspx) to get T values.
Although I have the results but I do not know how to write and interpret for research paper/ thesis.
Kindly give your valuable suggestions on the same.
I want to conduct a linear regression in SPSS with 4 IVs and 1DV but I also have 2 other variables besides the IVs that I want to use as adjustment variables. The adjustment variables are age and gender. Since I collected categorical data for age and gender, I created dummy variables for each category of age and did the same for gender. What are the steps in SPSS for conducting a linear regression and adding in these dummy variables?
I have extended the UTAUT2 model and I have already developed my model hypotheses. I read about various methods/techniques used such as (Factor Analysis, Partial Least Square (PLS), Structure Equation Model (SEM), Regression Analysis) and tools such as (SPSS, Smart-PLS, Mplus, R, PLS-graph, AMOS). But I am not sure which one to use.
Please I would like some advice about recommended techniques and tools. I am really looking to find out what you consider to be the most efficient method and your rationale as cost and time are limited factors.
I am working on Abusive supervision as X value, Job stress as Y value and Work Family Conflict as a mediator and Procedural Justice as a moderator. While working on my data set in SPSS regression analysis shows a significant negative effect of moderator and my hypothesis is acceptable. But while cross validating with PROCESS it shows a different results and appears that moderation is insignificant. I am adding the results output of both SPSS and PROCESS for reference.
Could you please help how to interpret this?
I am trying to test the model in the figures below. That is, a linear relationship between X and Y, with two moderators variables (X and W) in which there is an influence of the joint effects of the two moderators together.
I could use the macro for SPSS called PROCESS, but I am more interested in developing a hierarchical regression with STATA by multiplying the different variables (previously centered).
Has anyone worked with similar models before or recall a similar study I can base to develop the methodological part of this kind of moderation effect correctly?
Thank you un advance.
I need to convert three independent variables (V1; V2; V3) into categories of a new variable (V (n): Cat1 (v1) .; Cat2 (v2); Cat3 (v3)). I have studied the procedures of Counting and recoding variables but they do not work. Please could you tell me if it is possible to create the new three category variable in SPSS.
Hi, in SPSS (vs 17) I am trying to create a before-after plot with lines for all cases, colored based on 3 categories. The data are organised as: 2 lines per case (before and after, labeled as 1 and 2), and labeled based on 3 categories of cases (labeled as 1, 2 and 3, per 2 lines per case). Using line plots I have managed to produce a before-after plot for all cases, but only with a seperate color for each case. I am also managing to create a before-after plot with 3 lines, which are all cases per category combined. Using scatter plots, I am managing to get something resembling a before-after plot, only without the connecting lines between the points, but managing to color the seperate cases based on category 1, 2 or 3. However, as described above, I want to have a before-after plot with lines colored based on three categories. I feel this should not be hard to do, but I'm just not managing. Many thanks in advance.
I have a data set of particulate concentration (A) and corresponding emission from car (B), factory (C) and soil (D). I have 100 observations of A and corresponding B , C and D. Lets say, there are no other factor is contributing in particulate concentration (A) other than B, C and D. Correlation analysis shows A have linear relationship with B , exponential relationship with C and Logarithmic relationship with D. I want to know which factor is contributing more in concentration of A (Predominant factor). I also want to know if any model can be build like following equations
A = m*A+n*exp(B)+p*Log (C), where m, n and p are constant, from the data-set I have
Hello, I'm looking to calculate the Interquartile Range (IQR) for responses using a 7pt likert scale (sample size = 26). For some questions, Im noticing a difference in the Median and IQR when I calculate using Excel, compared to when calculated in SPSS. Any explanations??? I think SPSS may be using a 95% CI and removing the outliers. Is this possible?
Note: I checked a few sources and can confirm I calculated it correctly in Excel
Thank you for your help in advance.
I am working on a journal revision. The reviewers ask me to do a mixed procedures analysis because my experiment was a multiple-period task in which a participant repeated a task over several periods, and all periods observations were used in the analysis.
The reviewers also provided a reference for rerunning the analysis. When I was reading the reference paper, the results table is reported as in the picture attached.
My question is how do I conduct an ANOVA or mixed procedure and report results similar to the table attached. Specifically, do I need to conduct two ANOVA analyses to report one for between subjects and one for within-subjects? Or one analysis is enough. If so, how do I find the two Errors (one for between subjects and one for within-subjects)?
BTW, I use SPSS.
Thank you very much for your help!
I am doing my dissertation on Eating disorders and body dissatisfaction. I want to associate eating disorders, body dissatisfaction and self esteem. Also to compare this with BMI. Which method should I use?
I need to use regression models for my research. I used SPSS for linear regression but I want to use univariate and multivariate power regression such as:
a,b,c: model parameters
Y: dependent variable
X,Z: independent variables
Is there any user friendly statistical software to do it?
(I know about SAS or R software, but I think they perform regression by programming)
I wish to perform an analysis of an experiment with a dichotomous independent variable and a continous dependent variable and two moderators that are measured with a 5 point likert scale in SPSS. I have little experience with experiments in statistics. I have performend an independent t-test to analyse direct effect between the dependent and independant variable, but am now unsure how to proceed with my analysis of the the two moderator effects.
My understanding is that to analyse a moderator in SPSS I would need to create a new variable where i multiply the independent variable with the moderator variable to get the combined effect. However, I'm not sure how this would work when the independent variable is a dichotomous variable.
Another way to do this analysis that I thought of would be to change the moderators variables into dichotomous variables (e.g score 1-3 would be category 0 en 4-5 would be category 1) and do another independent t-test on the dependent variables with each of the moderators.
Can anyone point me in the right direction?
I'm looking for some advice on how to run a mediation analysis with multiple predictor variables in SPSS. I have downloaded Hayes PROCESS macro to assist me with the mediation analysis but it appears that it can only accommodate one predictor variable at a time. I know there is a MEDIATE macro that was available before PROCESS but I don't know where I would find resources/guides to help me with the MEDIATE macro for SPSS as PROCESS has taken it's place now. Should I run three separate mediation analyses (one for each predictor variable) or is there a way for me to run a single mediation analysis with the three predictor variables included?
This is a call for PhD student volunteers to participate in a Qualitative Interview. Please write to [email protected] or just make a comment if you can participate. I will send further details in a direct email.
Brief: Doctoral students require a high amount of cognitive, emotional, and personal competencies to complete and achieve the required degree qualifications. This research is to find out research is about Student perspectives about the research process and the impact of software application tools such as Mendeley, AtlastTI, NVivio, SPSS or any other tolls and their impact on individual productivity.
Please share your wisdom with me.
As you know, for using nonlinear regressions in statistical software like SPSS or Minitab or codes, you need to determine a start point(start value or initial guess) for regression's parameters. Actually you need to choose an optimum start value for parameters to achieve the best nonlinear equation.
How can we determine the optimum start value?
Is there an other way (the other software) to use nonlinear regression regardless to parameter's start value?
I'm currently working on my dissertation - it's on the gender differences in moral reasoning behind moral dilemmas associated with cybercrime. Each moral dilemma has 3-4 open questions where each participant had to write an answer and their reasoning behind the answer. I have coded these answers via themes/insights and have calculated the percentages behind these themes.
It is worth noting, that each question allowed multiple answers, therefore leading to participants having two or more codes associated with their answer.
My leading professor asked me to analyze these percentages to seek any statistical differences. My only lead is a Chi-square test, however, it probably doesn't seem the right choice because:
1. Each question is not limited to a yes/no answer, there are answers like: I don't know, or answers providing alternative solutions, thus omitting the aforementioned moral dilemma.
2. Participants often answered with multiple answers, that is they could answer yes and provide a reason but later on in their response answer no and also provide a reason.
What I need to know if there is a way to analyze statistically this data, what is more, if it's even possible to somehow analyze the percentages I've calculated of this qualitative data?
I want to compare the performance of two diagnostic tests (binary yes or no) on the same population, first, I want to compare if there is a significant difference in their ability to predict my specific outcome/disease and second, I want to identify if there is a significant difference in the distribution of different characteristics between the positive diagnostic test results (both numeric and binary). Can this be done on SPSS? and if so under which test?
I am using the PROCESS Macro to conduct a moderation analysis. To visualise the significant interaction, I copied and pasted the necessary text and created a new syntax. The scatterplot was produced but I am unable to add fit lines at the subgroups (please see screenshot of error message attached).
Does anyone know why this might be? I have had a browse online and have seen a lot of mention about re-coding categorical variables when faced with this problem. However, in my case, the variables included in the creation of the scatterplot are all continuous.
I recently updated to SPSS 27 on Mac OS X 11.4 and I am having difficulty installing the custom dialogue box for Andrew Hayes' PROCESS Macro. I also recently installed the fix pack from IBM to 220.127.116.11. When I attempt to install, I get the attached error message.
I saw that others identified an error in the code for the custom dialogue box (https://www.ibm.com/support/pages/process-macro-v35-custom-dialog-returns-error-about-invalid-character-ibm-spss-statistics-27-or-subscription-release), but I'm not even able to open the code to examine it without getting an error.
I'm assuming this is an SPSS issue rather than a PROCESS issue. Has anyone else encountered this?
I feel like maybe this question is so easy that it's hard and I keep doubting every choice I make, so I thought I would just finally ask online. I have one dependent variable with three levels: percentage of caches a bird make in one of three different types of substrates (sand, gravel, or other). My independent variable has two levels (the three conditions the birds are in whilst caching).
I pretty much know my pattern of results and I have figures drawn up and everything (I can post a small subset of the data if that would be helpful to people in answering the question), but I cannot, for the life of me, figure out if I have chosen the right test to analyse this data and I keep having doubts every time I make any progress.
I'd ran a similar study not too long ago that was almost identical, except the independent variable only had two levels (gravel and sand). Therefore, I could just run a Friedman ANOVA (since the data violated normality assumptions) for percentage of caches in one or the other substrate.
Right now the only solution I have been able to come up with for this is running multiple Friedman ANOVAs, but, as mentioned, I am really starting to doubt this. This is primarily because--unlike when I only had 2 levels in my DV--, I cannot just run a test for one of the levels (e.g. in my first experiment, if 25% of the caches were in sand, you automatically knew the other 75% were in gravel; however, for this experiment, if 25% are in sand, that only means that 75% are in gravel or "other" and you do not know how it is divided up between the these two).
I know there are more advanced forms of analysis as well, like mixed models, but I've been specifically advised against doing those in this particular instance.
It's just that I keep writing and writing, then I get freaked out about my stats, so I do a bunch of research, then I try to re-calculate and re-write….and it's getting to the point where I can't do that anymore. So, I kind of need to know now if I need to change my method of analysis.
Could anyone give me advice?
In my research, I've made a following equation (with a demographic dummy variable Czech = 1, Dutch = 0): DV * Czech = b0 + b1*Czech + b2*Czech ... b10*Czech.
I can simply interpret the b1...b2 for the Czech demographics, however, how can I interpret the variables for the Dutch population (Dutch=0)? I would need to use only the intercept? Or do I have a mistake in the model equation (the DV should not be an interaction term)?
Thank you for your response!
1) no =0, yes =1
2) no =0, yes =1
3) no =0, yes =1
4) no =0, yes =1
1) no =0, yes =1
2) no =0, yes =1
1) no =0, yes =1
Using SPSS, I want to test the mediation effect with the help of multivariate logistic regression.
Is there any method except the way proposed by Baron & Kenny?
For my thesis research in SPSS, I asked respondents to evaluate a company based on five items - affordability, quality, sustainability, trendiness, ethical behavior (5-point Likert scale, where 1-low and 5-high). I would like to ask, if it is better to do factor analysis out of the items or do a mean value out them. Any help is welcome! Thank you!
I used binary logistic regression to model the behavior of drivers' stop and go decision in dilemma zone. Now I have to estimate the elasticity of variables (corresponding change in outcome probability on changing one unit of X). My variables are of continuous and categorical nature. Any help would be highly appreciated.
P.S: I have uploaded the screenshot of one of the papers where author has calculated the elasticities.
We are investigating the impact of online learning environment on sustained attention and student engagement among undergraduate students (Year 1-3).
We had the participants watch a lecture video and answer multiple choice questions related to the content of the video. We also had them fill an engagement questionnaire which had questions about their involvement with peers and various university related activities.
Hypothesis I - 1st year students are able to focus more over a long period of time than 2nd and 3rd year students.
Hypothesis II - 1st year students actively participate and are more engaged with peers than 2nd and 3rd year students.
My questions are:
1. What statistical test would be most appropriate?
2. How do I set a value to prove significance...for example they had 13 MCQ questions, how do I know what number of questions they need to have answered correctly in order to say they paid more attention?
I'm very lost, i'd appreciate your help and any remarks that could provide me some clarity.
Good day everyone.
i need guidance regarding how to calculate correlation coefficient of two continuous variables; one being predictor and other being outcome variable..
looking forward for a quick response.
I am trying to do an analysis of the Job Demands and Resources Model (JDR) with a one mediating variable using the Hayes model.
However, I am not sure how I can do an analysis of one big model of both demands AND resources (Multiple independent variables) and Outcomes (dependent variables). Motivation is the mediating variable.
Hi, I'm currently writing my masters disso and I thought this community may be able to give me a few different perspectives!
Im using secondary data from the UKHLS and doing a cross-sectional panel analysis, to measure how age/sex/ethnicity and personality affect an individuals subjective well being over different points in time. The same respondents are included in each wave. Due to my IVs being a mixture of time variant/invariant how would i run a regression analysis on SPSS? (we have to use SPSS).
So far Ive changed my panel data to wide format and created dummy variables for each of my IVs.
I would appreciate any advice on what the best way of doing this would be, my supervisor also mentioned clustered standard errors?!
For my thesis project, I want to fit a quadratic model on each individual separately. For example, I have variable X1 and X2 (e.g., price1 and price 2) and variable Y1 and Y2 (e.g., Reaction Time1 and Reaction time 2), and 75 participants. Each participant conducted X1 and X2, and it is expected that the RT distribution is a quadratic distribution. Moreover, the expectation is that the peak of the distribution in X2 is on a different spot than on X1.
I want to estimate for each participant their RT distribution on X1 and X2 separately, and compare the outcome parameters with a statistical test.
Does anyone know how to analyze this problem in SPSS or in R?
Thank you very much!
I have 7 variables of different food samples, and I have 2 CFU values of each, for Day One and Day Two for each sample.
What SPSS tests should I apply to test them?
I am currently doing a research on service quality on customer satisfaction and loyalty . I am currently on the planning and analysis results using SPSS but I am a little confused with regards to regression analysis . What would be the best steps to follow to present data ( see file attached) to show Regression analysis of relationship between service quality and customer
When I am on SPSS I am not too sure where to put control variables as linear regression option only has option for dependent/independent variables . I am not too sure what model 1/2 means
Thanks in advance
I am studying the impacts of receiving a certain treatment (kind or mean) on the motivation. I expect that the treatment influences the motivation through three mediation variables. So my conceptual framework looks somewhat like this: X -> Z1, Z2, Z3 -> Y.
To explain it with an easier example: I want to know the effects your gender (male/female) has on your life expectancy, but I study it through the mediating variables height, weight and smoking (all on a likert scale). I expect that the gender affects the mediating variables, but they in their turn have an effect on the life expectancy.
(A little more detailed: I have an experimental design where I use vignettes to reboot the type of treatment someone will receive (the respondents either get the soft treatment or the hard treatment, not both and it is randomly assigned). Then I test my mediation and Y variables through survey questions with a 5 point Likert-scale.)
Which test do I use for this in SPSS or does anybody have an idea how to format my conceptual framework better?
What if the Cronbach's Alpha of a scale (4-items) measuring a control variable is between the .40 -.50 in your research. However, the scale is the same scale used in previous research in which the scale received a Cronbach's Alpha of .73.
Do you have to make some adjustments to the scale or can you use this scale because previous research showed it is reliable?
What do you think?
I am currently working on a project that addresses educational communication and technology (ECT) barriers in online education and distance learning in the Philippines. However, according to my internet research, SPSS can only handle 1500 cases or respondents. My project requires over 3000 student respondents with more than 150 variables, which includes sub-variables. Is there any statistical software that can meet my requirements?
I am testing to see whether a person's food choice is influenced by their personality traits (Big 5 - Conscientious, extraversion, agreeableness, openess and neurotism) I have 26 question completed food choice questionnaires where they need to say what is important to them when choosing food to eat on a daily basis. The final answers are split into categories e.g health, weight control, natural content, familiarity, mood, ethics, price. The scale is 4 likert 1) not important at all, 2) a little important 3) moderately, 4 very important. Each category of the food choice questionaire is the dependent variable.
The independent variable is also ordinal I am testing personality traits. Each personality type is split into 5 e.g for conscientiousness, 1) very low, 2) low, 3) Mid 4) high conscientious and 5) very high.
My theory is that for example people placing a high importance on health and weight control are also high in conscientiousness, similarly those who place a higher importance on imporving mood might be higher on the neurotism scale and lower conscientious
What I do not understand is if I have liert scale on both variables, how do I interpret my results?
I have run a test using the importance of weight control as the dependent which is ordered 1. not important at all, 2. a little important, 3 moderately important and 4 very important. The independent variable that has shown significance is the personality trait conscientious. It is showing that people in the high level of conscientiousness so level 4 (5th is the reference which is very high).
My p test is <0.002 with expB of 0.071 CI :0.013-0.385 (P coefficient is -2.642)
I just don't understand if a number <1 on expB means that the likelihood of being in the higher level on the dependent is higher or lower or the other way around. It seems like a very low expB. Could it mean that the likelihood of being in the lower level for weight control is lower?
How can I insert statistical significance (i.e. F test P value < 0.05) annotations on top of my column bars on SPSS ?
I am conducting a study into the effects of evaluation anxiety on working memory task performance. I have an Independent variable, the task which the individual will complete, observed v unobserved, and the same people complete both tasks, a dependent variable, the scores on the memory task in each condition, and also a covariable, score on the Liebowitz social anxiety scale. I did also include memory but this is not something I want in the main analysis but just to compare the means with anxiety score afterwards. I want to compare the difference between the two memory scores with the anxiety score. Which test should I use? I have been told by my supervisor a one way within ss ANCOVA is the correct way, but I am not sure. Clarification on this would be greatly appreciated. Below I include the analysis I have run. Thank you in advance.
Recently, I did a path analysis in SPSS. Before the path analysis, Pearson Correlation were done. Factor A and B are correlated, r=.428, p<.001. However, when I use AMOS to run the path analysis, it showed insignificant effect.
Could it be possible?
Did I do anything wrong?
Why the statistical analysis showed correlation significant but insignificant in path analysis?
I do not know how to figure this analysis out. For my thesis I have the following data:
Independent (categorical) : diet choice
- pesco--pollo vegetarian
- non vegetarian
Moderator (continuous) : environmental concern
Dependent (continuous) : happiness
Does anyone know how to analyse this in SPSS? I am very lost.
I already made dummy variables of
1 = vegetarian (vegetarian + pesco-pollo)
0 = non- vegetarian.
However I still do not understand how to do the analysis. Can anyone please help me?
I am working on a follow-up data set in SPSS in wide format for which ~ 400 cases were lost to follow-up since baseline. I am looking to insert the lost cases and their variable information as system missing based on the patient ID.
I am hoping there is a simple solution using syntax so I don't have to do it manually.
Is it possible to run two-way anova in SPSS with secondary data? I only have the mean, standard deviations, median, and mode. I also have the sample size. However, I don't have the original data that was used to determine the mean and the rest.
I am using SPSS to run moderation analysis with categorical moderators. How to interpret the output with reference to the levels of the moderator categories?
I want to run a moderation analysis using SPSS, where I have 2-IVs, 1-MV, and 1-DV.
I am new here, do not have much knowledge of moderation. I will appreciate it if you guide both on writing the model, steps involved in SPSS, and the interpretation.
Thank you in advance to all those who will reply.
I made the question to check people knowledge and how much aware they are actually of the terms, so I put only two correct questions and the rest is wrong but now I got 200 responses and many responses selected all, some selected wrong and write both(eg: one Right and one wrong). so I have no idea how to analyze. please help
Kruskal Wallis gives a 2 tailed significance value only. according to this value SPSS decides whether to continue and calculate the pairwise comparisons automatically or not.
Since I have an hypothesis with a direction, I can divide the significance value by two, but in this case, SPSS won't run the post-hoc analysis, and so I'm interested to know how to run only Dunn's-Bonferroni test separatly.
I have sample a of 138 observations (cross sectional data) and running OLS regression with 6 independent variables.
My adjusted R2 is always coming negative even if I include only 1 independent variable in the model. All the beta coefficients as well as regression models are insignificant. The value of R2 is close to zero.
My queries are:
(a) Is negative adjusted R2 possible? If yes how should I justify it in my study and any references that can be quoted to support my results.
(b) Please suggest what should I do to improve my results? It is not possible to increase the sample size and have already checked my data for any inconsistencies.
I am currently running a binary logistic regression on my data in SPSS. However, I want SPSS to provide me with the AIC (Akaike's Information Criterion) as well. Can anyone help me out with this and provide me with the steps that I need to take in SPSS, as I am not very experienced with SPSS.
I am currently analyzing data for my master's thesis.
One question was a ranking question where participants could rank 4 different options based on their perceived effectiveness.
In SPSS I now have 4 different variables for each participant.
Variable 1 shows the first ranked options, variable 2 the second-ranked options, etc..
How can I analyze this Data, to see which option was overall perceived as most effective, second effective, third effective, and least effective?
Hi, I am running path analysis with latent variables. My model fit indices are good, however some of the factor loadings are negative. Also some of the standardized estimate are more than 1 like chemical on N2O is more than 1 (1.60) and topographical on N2O is -1.03.
Is it alright to have negative loadings in the attached path diagram. How can i correct this diagram?
I am currently investigating whether males nurses are more likely than female nurses to be brought before a fitness to practice hearing. I have tried to do a one sample binomial test in SPSS, however the expected results show a 50/50 split in terms of gender. The issue being that the actual proportion of male nurses is 11%. That being the case is it possible to discover whether the proportion in the sample is significant using a binomial approach or is another test needed?
I'm researching the effect of a self-compassion intervention on well-being. There are 2 groups (intervention and control) and 3 time points (pre, post and follow-up).
Since I have multiple dependent variables (life satisfaction, positive affect, psychological well-being, optimism, negative affect, depression, stress) I wanted to run a Mixed MANOVA instead of a Mixed ANOVA but can't seem to find how to include multiple dependent variables in Mixed Models in SPSS.
Is this the correct study design and is it possible to run the Mixed MANOVA in SPSS or do I have to run multiple Mixed ANOVAS?
I have a file of lab data in which every patient has multiple values from measurements taken on different days. It looks like this:
BP1 BP2 BP3 BP4 BP5 date1 date 2 date 3 date 4 date 5 (BP1 was measured on date1 etc)
I'm interested in the date on which the lowest BP value was measured. I have been able to use a command to find what the lowest BP value was and also what date variable it can be found in, so for example: I know that for a specific patient the lowest BP was 75 and that this value is in the BP3 variable for that patient. But I want to tell SPSS that if BP3 contains the lowest value, that it should copy the date in "date3" to a new variable so I have a list with the dates on which the values were the lowest.
How do I know if the data I collected follows a normal distribution or not through SPSS? And if it does not follow a normal distribution, how do I convert it so that parametric tests can be performed on it?
I have 4 IV (categorical data) - age, gender, ethnicity, job position
and 1 DV (continuous data) - muscle strength
What is the most suitable statistical analysis because I want to find which factor (age, gender, ethnicity, job position) contributed the most toward the muscle strength?
I'm doing research about The changes in dietary habits among undergraduate students before and during COVID-19. My research objective is to determine dietary habits before and during COVID-19. I want to know if there are possible changes between the two periods. Does anyone know how to determine this using logistic regression in SPSS?
Hello RG researchers,
I am a bit confused due to different questions and comments.
Well, I have a single factor containing 11 items (Likert rating). For the EFA, I am using SPSS (maximum likelihood) and I use lavaan and Amos for the CFA. I've got three questions:
1. KMO and Bartlett's tests' criteria are met while the normality tests (Kolmogorov-Smirnov and Shapiro-Wilk test) are not met (they are both significant). So, am I good to keep up the EFA or shall I need to use Satorra-Bentler or Yuan-Bentler adjustments (if yes, what software do I need to use)?
2. Should I be checking the normality for each item or checking the variable's normality is enough?
3. For the divergent validity, I use two other variables aside from my main questionnaire. Do they also need to be distributed normally as well?
Thanks for your time,
I am encountering a bit of an issue in SPSS right now. I was recoding some variables in my dataset, after which I made a mistake in the calculations. I tried to reverse it by pressing CTRL+Z. That did not work, but eventually, I calculated the variable again. However, now I see that in my whole dataset, all the string variables, are showing symbols such as '@' in the cell instead of normal text (example in the picture below). Did I do something wrong and how can I fix it?
I have data set for prevalence of mental illness for two years, from 2017-19 (36 months) and 2020-21 (14 months). I am comparing the prevalence of mental illness rate between 36 months and 14 months. For this I am comparing the mean mental illness rate between 36 months and 14 months through t-test using SPSS software and well as using the formula.
However, I have observed that the calculated mean value by using the formula given in statistics text book and the out put value given by SPSS software is varying considerably (0.47 vs 17). Now, I am confused which mean value should I give in the table, whether obtained through SPSS software or derived manually. Which one will be more reliable to give in the table? Please suggests.
Thanking you in advance.
Dr Bisu Singh