SPSS - Science topic

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?

📷

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.

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?

Hey! I want to know the hazard ratio for male vs female using keplan-meier survival command, however so far in SPSS they can only compute survival plot and statistic significance without telling me the Hazard Ratio, is there any way to compute it? (Using log rank, it only provides significance)

I have eight index items but respondents are randomly assigned to answer four of the eight items. How do I create an index based on the items. I can't simply ask the eight items together because everyone has missing data so no one is included in the completed index. Thanks for your help!

1. If I plan to do all data analysis using IBM SPSS and not using AMOS, do i need to do CFA?

2. If not, then how to judge convergent and discriminant validity of the instrument (survey questionnaire)?.

3. This doubt has come because, if I am not doing SEM, then why should i develop a measurement model in AMOS.

Hi all!

I've been collecting data on a group of 8 chimpanzees at Chester Zoo for my dissertation. The group consists of 4x males and 4x females, all of which have different hierarchical status' and ages.

I have been doing random focal observations with a checksheet consisting of 4 state behaviours (timed) and 6 behaviours (frequencies). I would start a random focal observation when a stressful context arose (such as high visitor numbers, anticipation to feeding, or feeding time)and denote the durations or frequencies of behaviours exhibited by that individual for 15 minutes. Then at the following visit, I would observe the same individual at the same time but under a non-stressful context (therefore utilising the Matched Control Method).

This process repeated for 4 months and I now have a complete data set.

I am <really> struggling on 1. How to use SPSS, and 2. What tests would be ideal to use? As you can imagine there is quite alot of data which hold different values so you can hopefully see my confusion around this.

Ideally, the statistical analysis of my data will reveal which contexts in the zoo precipitate an increase in stress the most (e.g. high visitor numbers, anticipation to feeding, feeding). I also want to be able to compare this data to the hierarchial status' and ages of the individuals.

Any help would be so appreciated. Thanks in advance!

I need to find if the relationship between two variables is linear or not. I'm using IBM SPSS. I found that some people apply Test for Linearity (Analyze->Compare Means->Options->Test for linearity) while others apply Regression Test (Analyze->Regression->Linear). What is the difference between these two tests and how to interpret the results? I applied the Test of Linearity but I don't know how to interpret the results. Please guide me。 (Screenshot attached)

I really want to learn about I really want to learn about Linear Mixed-Effects Modeling

in SPSS or Mixed Models for Logistic Regression in SPSS. Can you show me:

1. Theory of those two models

2. How to run in SPSS

3. Is there a way to select variables into mixed models and random effects models?

Thank you

in SPSS or Mixed Models for Logistic Regression in SPSS. Can you show me:

1. Theory of those two models

2. How to run in SPSS

3. Is there a way to select variables into mixed models and random effects models?

Thank you

Hello, I am trying to evaluate the group differences of the change from baseline in an EEG index (fMMN_amplitude), using the Linear Mixed Models process. I entered Cluster (three groups), time (two visits), Cluster × time interaction as fixed effects, baseline MMN_amplitude value as a covariate, and participant as a random effect for the intercept. I used EMMEANS to obtain the change from baseline of MMN_amplitude in each Cluster. Picture 1 is the command lines I used.

However, I could not find a way to compare the change values between groups. Can someone please let me know if there is a way to acquire such group differences and the effect sizes, just like the mean difference versus placebo in the Picture 2 (from DOI: 10.1016/S2215-0366(20)30513-7). It's like computing [(g2t2 - g2t1) - (g1t2 - g1t1)]. SPSS command lines will be best, GUI operations are also fine. Any help will be appreciated!!

Sincerely,

Greatson Wu

I want to see if there is a difference in age structure between male and female fish so I have ages from 0.5 to 8.5 and the number of each sex at that age but I dont know which variables I use in the chi squared

Hi all!

I've been collecting data on a group of 8 chimpanzees at Chester Zoo for my dissertation. The group consists of 4x males and 4x females, all of which have different hierarchical status' and ages.

I have been doing random focal observations with a checksheet consisting of 4 state behaviours (timed) and 6 behaviours (frequencies). I would start a random focal observation when a stressful context arose (such as high visitor numbers, anticipation to feeding, or feeding time)and denote the durations or frequencies of behaviours exhibited by that individual for 15 minutes. Then at the following visit, I would observe the same individual at the same time but under a non-stressful context (therefore utilising the Matched Control Method).

This process repeated for 4 months and I now have a complete data set.

I am <really> struggling on 1. How to use SPSS, and 2. What tests would be ideal to use? As you can imagine there is quite alot of data which hold different values so you can hopefully see my confusion around this.

Ideally, the statistical analysis of my data will reveal which contexts in the zoo precipitate an increase in stress the most (e.g. high visitor numbers, anticipation to feeding, feeding). I also want to be able to compare this data to the hierarchial status' and ages of the individuals.

Any help would be so appreciated. Thanks in advance!

If a research has 3 independent variables and each variable has 10 components; To perform exploratory factor analysis, should I do the kmo test for independent variables separately or should I do this test for all variables at the same time?

is there any mistake in the data or there is a probability of no connection between the variables? Kindly help me with the interpretation. Also, explain me the R square as well. I have attached the framework too.

Hi,

Can anyone explain if there is a better system available to analyze data than the IBM SPSS?

Thank you,

Ameenah

I am using a Questionnaire that generates a total score using Likert scale responses 1-5 however there is an option for "Not applicable" how should this values be treated

Originally, I intended to conduct independent samples t-test and one-way ANOVA in SPSS for analysing my data. However, when I examined the normality of the dependent variable (DV) for checking their assumptions, it showed that my DV is highly skewed as the attached photo.

My DV is measured through an open-ended question and it is a continuous variable about participants’ predictions of the duration of an emotion, ranging from 0 to almost 300 unit. Sample size is around 1000.

Since the normality assumption is violated, I am wondering:

Thank you in advance for your advice.

In my study, I have a within-subject independent variable (the DV was measured twice for each participant), and I am also interested in examining the effect of participants’ demographic characteristics (between-subject) on the dependent variable. Therefore, I am running a mixed model ANOVA using SPSS.

Here are my questions:

Thank you in advance for your advice.

In detail, when utilizing the data from 1998 to 2014 as the training dataset, the Ljung-Box (Q) statistic, particularly Ljung-Box (18), is not generated in SPSS. However, if the analysis incorporates the entire dataset spanning from 1998 to 2021, the statistics are produced without issue.

For our 2x2 experiment (4 groups), we found that those in condition A did recognize their manipulation accurately, but that there is a spillover effect and the two groups in condition B also report different levels of A.

- 2x2 scenario experiment on e-mails

- condition 1 content positive vs negative

- condition 2 tone polite vs impolite

What we see is that the two groups in positive content rate their email as more positive in content than those in negative content - so manipulation successful. But, those in polite tone also rate their e-mail als more positive in content than those in the impolite tone condition. But the F-values are quite far apart.

How do I show that despite this spillover, the F-value of the manipulation of content is so much greater (signif) than the F-value for the tone groups?

I want to show that these two F-values differ significantly - right? Using SPSS, could anyone recommend the steps to take here. I've been recommended a Wald test but am struggling.

I am conducting a study that involves analyzing categorical outcome variables with multiple levels and want to employ multinomial logistic regression analysis in SPSS for my data. However, I'm uncertain about the appropriate steps and procedures to follow in SPSS to conduct this analysis accurately. Specifically, I need guidance on how to input my data, set up the model, interpret the output, and assess the model's goodness-of-fit. Any detailed explanations, step-by-step instructions, or recommended resources regarding multinomial logistic regression analysis in SPSS would be greatly appreciated.

I think SPSS made my computer slow. Be careful when run in your computer.

Hello everyone, for my dissertation I have two predictor variables and one criterion variable. In one of the predictor variable- I further have 5 domains and it doesn't have a global score so in that case can i used multiple regression or i have to perform step wise linear regression seperately for 6 predictors(5 domains and another predictor) ?- keeping in mind the assumption of multicollinearity.

Theory suggests that these latent variables (SEFM, SEFI, SIF, and SER) variables are correlated.

hi, i'm currently writing my psychology dissertation where i am investigating "how child-oriented perfectionism relates to behavioural intentions and attitudes towards children in a chaotic versus calm virtual reality environment".

therefore i have 3 predictor variables/independent variables: calm environment, chaotic environment and child-oriented perfectionism

my outcome/dependent variables are: behavioural intentions and attitudes towards children.

my hypotheses are:

i used a questionnaire measuring child-oriented perfectionism which will calculate a score. then participants watched the calm environment video and then answered the behavioural intentions and attitudes towards children questionnaires in relation to the children shown in the calm environment video. participants then watched the chaotic environment video and then answered the behavioural intentions and attitudes towards children questionnaire in relation to the children in the chaotic environment video.

i am unsure whether to use a multiple linear regression or repeated measures anova with a continuous moderator (child-oriented perfectionism) to answer my research question and hypotheses. please please can someone help!

My current study has almost 1000 responses. However, for one of the item I am interested in examining, it is not normally distributed (see attached image 1 for your reference). Since participants’ responses are really diverse on that item, so even removing some of the extreme outliers still cannot solve the problem.

I would like to run a moderation analysis using this item as the dependent variable. From different source of information on the Internet, I learned that normality should not be an issue for PROCESS macro as it provides the function to bootstrap.

From one tutorial, I saw that in order to not to care about the normality issue, we should select the “Bootstrap inference for model coefficients” option (see attached image 2 for your reference). However, from the other tutorials I read, they only mention about the number of bootstrap samples, without mentioning that we have to select the “Bootstrap inference for model coefficients” option. I tried to run the analyses with and without this option, and it changed from significant interaction effect of IV and moderator (when this option is not selected) to insignificant (when this option is selected).

I cannot really find the purpose of the “Bootstrap inference for model coefficients” option in Hayes's book or on the Internet, and I am also not really good at statistics. Therefore, I would like your help in the following:

Thank you in advance for your kind assistance.

The AVE of the scale is below 0.5 and rest of the parameters viz. CR and discriminant validity are above the threshold level

Hello!

I have done psychometric analysis using both SPSS and R, and the values are similar, but some are not the same, is this usual? Has it happened to anyone else even with same estimators?

Thank you for your feedback on this matter.

All the best,

Ana

SPSS doesn't offer Aligned Rank sum test in version 17-21? If one were to report a multiple time data where continuous variable werent normal, would normalizing and using anova not amount to simple split plot anova? what of if the data is ranked originally, how can one handle it possible with these older SPSS versions?

I performed the mixed model ANOVA analyses and repeated measures analyses.

I need answer for this question:

What is considered small, medium or large effect size and cite reference as needed? Also explain, how should I calculate or examine effect size. I used SPSS.

I am a bit confused about the interaction effect of a two-way ANOVA and moderation.

For example, I want to examine how the intensity of exercise (i.e. 1 = walking; 2 = running) affects the amount of calories burned. I suspect that higher intensity exercise (i.e. running than walking) lead to more calories burned. However, I suspect that the gender of the participants may also have an effect. For example, among male, higher intensity exercise should lead to more calories burned than female.

In this case, should I use (1) two-way anova, and see whether the two independent variables (i.e. "intensity of exercise" and "gender of the participants") have a significant interaction effect; OR (2) moderation analysis using PROCESS Macro of SPSS and see whether the interaction term of the independent variable (i.e. "intensity of exercise") and the moderator (i.e. "gender") is significant?

To me, it seems that no matter which analyses, as long as it gives me a significant result, I can still interpret the result the same way, and answer my hypothesis. Therefore, I would like to know the rationale in choosing which analyses to use in similar cases. Is it related to the nature of my variables (i.e. categorical/ continuous) or are there other reasons?

Can anybody help? Thank you.

I am analysing a set of data which have both within-subject and between-subject variables. However, since my assigned task is just simply focusing on part of this data set, I am not interested in examining the within-subject variable.

The original study is to examine how the branding of the product (independent variable) affects participants' happiness (dependent variable), and all participants are required to try both brand A and brand B product. However, extending from this original study, I was asked to examine how participants' demographic may actually affect their happiness instead.

How can I solve these problems using SPSS? Can anyone help? Thank you

Right now I am wondering what I might be possibly doing wrong with my SPSS syntax. I have got 2 already dichotomized variables and I would need to combine them to make the 3rd one.

If var1 and var2 = 0, than it should be 0, and if var1 or var2 = 1 (or both of them), then it should be 1.

I tried this syntax (and many more :D), it is just not working.

I have data from a questionnaire study structured like so:

I am using SPSS to run an ordinal logistic regression with 'response' as my dependent variable and the other 5 as my independent variables.

When putting the data into SPSS I have coded it as follows:

When I run the regression, this is my output with a significant result highlighted in yellow (attached).

From what I've read and understood about interpreting the results of an ordinal logistic regression, this is saying that:

"The odds ratio of being in a higher category of the dependent variable for males versus females is 2.244" which is saying that males are more likely to agree more strongly than females.

However, when I create a graph looking at the split of responses between males and females it shows that females are actually more likely to agree more strongly than males (see attached).

I would be grateful if anyone could help me to understand what I'm doing wrong - either in my modelling or my interpretation.

Hello,

I greatly appreciate your help in my following statistic task. I need to reduce the number of financial ratios (FRs) - currently 20 - that best represent the firms' profitability in the technology sector. These 20 FRs were selected from 32 sources - practitioners and peer-reviewed articles.

I plan to reduce the 20 financial ratios through diverse steps, starting with the "Intercorrelation Matrix Analysis" applied to the 20 FRs of 10 companies (random sample) over the past five years, using SPSS (29). It will eliminate FRs with weak inter-correlation – i.e., ≤ ± .5.

FRs data will be derived from the firms' annual reports.

PROBLEM: How do I build the intercorrelation matrix with the 20 financial ratios data (tabulated in Excel) from 10 firms over the past five years using SPSS (29)?

Should I perform a Matrix Correlation Analysis over the period by the single firm? If so, how do I interrelate the results of the five Correlation Matrixes - one for each firm - to eliminate those financial ratios with weak correlation?

Or,

Is it correct to create a single Excel table with 20 variables (FRs) columns and 25 Rows (5 firms x 5 years) and then export it to SPSS for the correlation analysis?

Is there any better approach for this first reduction step?

Thank you very much for your time and support!

I am trying to examine the effects of studentification on private residents in a studentified area, either it is positive or negative (which is coded as 1 or 0 respectively) as the dependent variable.

The independent variables are effects of studentification (across literatures) on 5-likert scale.

The question is am I to also dichotomies the likert scale responses from (strongly disagree, disagree, neutral, agree and disagree) to (1: positive, 0: negative)?

R. Shanthi's book ''MULTIVARIATE DATA ANALYSIS: Using SPSS and AMOS'' has been required for academic work. If there is someone who has this book, can I provide it? It is a big help for me.. Thanks

I am conducting a cross-sectional study from NHANES database. I used the "Full.sample.2.year.interview.weight" to weight the data. However, I do not know where should I put the decimal to finish my data analysis.

The total sample size before weighting is 5639 participants.

If I put the decimal after 5 numbers, the sample size will be decreased by 50% and will be around 2700 participants.

If I put the decimal after 4 numbers, the sample size will be increased by 200% and will be more than 10,000 participants.

I am attaching a screen shot after weighting the data.

I used SPSS for analysis, tab (data), weight by "Full.sample.2.year.interview.weight".

Would anyone happen to know how the percentages are calculated in SPSS for the predicted and observed categories? Is it something you can do by hand or does SPSS use some kind of internal calculation? The type of output I'm referring to is the screenshot below. Huge thanks in advance for any help!

I am trying to report the results of a study that employs a 2 (between subjects: tx vs ctrl) X 4 (within subject: time :T0,T1, T2 and T3) mixed factorial ANOVA. I have analyzed the data using both SPSS and JAMOVI.

Personally, I find the JAMOVI results cleaner and the graphs of estimated marginal means much better because SPSS is not graphing with error bars at all. and JAMOVI tables are much simpler.

Question is: why does SPSS provide a multivariate table here for an analysis that is essentially univariate (One continuous DV), then follows it up with separate tables for within and between subjects factors?

Second, from the JAMOVI output, I have been using the interaction term posthoc testing to breakdown the significant interaction- I wanted to verify this is essentially doing the same thing as a simple main effects analysis. conceptually this is testing each level of each factor within each level of the other factor- so this should be okay, right?

I will also need the interpretation concerning the reference variable.

I found the macro and the dataset in David Kenny's website

But this macro was developed for versions 16 and 18, and I tried with the version 20 and it didnt work. Did anyone try to use this macro with the version 20 and it worked? Or does anyone have an updated version of the macro which is compatible with version 20?

Thanks in advance!

I wish to use SPSS to achieve minimum-maximum normalization (from 0 to 1)) for 5 variables. How can I do that?

In an earlier version of SPSS I was able to get visual means for each group (see picture below) when creating scatterplots with one categorical and one scaled variable.

Now I have tried for hours to find out in my updated SPSS 26.0 how to do it, but I can't figure it out.

I have tried do use the same syntax as earlier, but that does not create mean-dots, which lead me to think I used some modification. Still can't figure it out?

Any suggestions?

Hi all,

I want to do a repeated measure Ancova with constant covariates. The IBM SPSS website mentions you could do it using a two-step approach in this software (https://www.ibm.com/support/pages/repeated-measures-constant-covariates-glm). Can someone please verify this is a proper way to do it?

Many thanks in advance!

I want to study the effect of covariates (age, sex, cause of injury, lesions, income, subjective social status), and these are multi categorical. Can anyone suggest how to add them in my model of analysis?

Should I transform them into dummy variables or what?

I firstly put them as they are in SPSS, and it shows output without error.

Response will be highly appreciated

I've a lot of qualitative data, so I asked.

Hello, I am trying to run a t-test on differences in mental well-being in the last month (average of 14 sentences) between two groups, women I have 200 and men I have only 34 who answered on the questionnaire. when processing SPSS shows me that there are only 12 men, why is that? Frustrated from a whole day of trying to understand where the problem is and why 22 more people ( that I really need) are disappearing in the data analysis

I have performed a Box-Cox transformation of a response variable in multiple linear regression in SPSS. As I understand, in order to correctly interpret and present the data (B, t, CI for B and p), I need to back-transform the data by applying the formula for Box-Cox back-transformation to B, t, CI for B. But the formula specifies lambda and I can't find it anywhere in SPSS. Could you please tell me where I can find the lambda to apply this formula for back transformation?

Hi everyone

Can someone help with the following observation while applying simple linear regression in SPSS and STATA

STATA results: Correlation coefficient = 1.7 (see the image), How can this be possible?

SPSS results: Correlation coefficient = 0.17 (different from STATA) and a p value of 0.01 for such a weak coefficient.

DATA set is SAME in both STATA and SPSS

Can someone explain the coefficient of more than 1 in STATA and a different result in SPSS

I am hoping someone can advise on the use of the Bonferroni correction. I am struggling to understand how and when to use it.

I have been part of a pilot study evaluating balance in older adults. We used several balance measures (sway area, sway velocity) with different parameters (eyes open/closed, solid/compliant surface), plus a timed functional test, measured on three occasions with the same subjects. I have performed separate repeated measures ANOVAs for each measure and parameter combination (7 in total) using SPSS, alpha set at .05. I understand the need to use the Bonferroni correction with the pairwise comparisons and that SPSS does this. However, I am unsure whether the correction needs to be applied across all the ANOVAs, namely whether I should set the corrected alpha level to 0.05/7 = 0.0071 when determining statistical significance for each individual ANOVA.

I understand there are debates around minimising Type I errors at the expense of Type II errors, but is the approach of using alpha of 0.0071 for each individual ANOVA fundamentally correct?

Scenario - There is an IV and DV. IV is measured in 5 point likert scale questions and DV is measured in 7 point likert scale questions.

Doubts -

01. Can we run a test like regression analysis directly irrespective of differences in measures?

02. if NOT, what are the transformation techniques available to transform data into same scale?

با سلام و احترام

اینجانب سپیده مرادخانی کارشناس علوم آزمایشگاهی و دانشجوی دکترای ایمونولوژی در دانشگاه تهران بوده که در حال کار کردن روی پایان نامه خود در زمینه نانودارو می باشم. با توجه به سوابق کاری در آزمایشگاه تشخیص طبی و مرکز تحقیقات و همچنین علایق در زمینه فعالیت های گروهی آزمایشگاهی و پژوهشی تمایل به استخدام و همکاری با مراکز محترم را در سال جدید دارم.

گزیده ای از رزومه بنده:

تسلط بر تکنیک های الیزا و الیسپات

تسلط بر الکتروفورز، روش های استخراج DNA and RNA و روش های مولکولی PCR and real time PCR

آشنایی با فلوسایتومتری، نرم افزار فلوجو، وسترن بلات، HPLC

آشنایی با ماکرواری و مبانی بیوانفورماتیک

آشنایی با روش های سبز تولید نانودارو

آشنایی با نرم افزارهای آنالیز آماری SPSS and

Prism

تسلط بر نرم افزار های رفرنس نویسی End note and Mendely

دارای مدارک زبان MSRT, MHLE

دارای چندین مقاله پژوهشی و مروری

خواهشمند است در صورت تمایل در پی وی به بنده اطلاع دهید تا رزومه کامل خدمتتان ارسال گردد.

پیشاپیش از حسن همکاری شما کمال سپاسگزاری را دارم

Anyone with AMOS for SPSS Version25,please?

I calculated SPSS that is better.

I calculated my ms thesis data by SPSS, I think excel is difficult.

I calculated a thesis of my sister. That calculation found the error result.

I am a new leaner.

I've completed my Ms thesis data calculation by SPSS.

When conducting a logistic regression analysis in SPSS, a default threshold of 0.5 is used for the classification table. Consequently, individuals with a predicted probability < 0.5 are assigned to Group "0", while those with a predicted probability > 0.5 are assigned to Group "1". However, this threshold may not be the one that maximizes sensitivity and specificity. In other words, adjusting the threshold could potentially increase the overall accuracy of the model.

To explore this, I generated a ROC curve, which provides both the curve itself and the coordinates. I can choose a specific point on this curve.

My question now is, how do I translate from this ROC curve or its coordinates to the probability that I need to specify as the classification cutoff in SPSS (default: 0.50)? The value must naturally fall between 0 and 1.

Sometime ago for ease of calculations, I have given the following exercise to my students to find out if there is any significant difference between the pre-test and post-test scores (with a fixed difference of 3 between each pair):

Pre-test: 5 -8- 10- 11-14-17

Post-test: 8-11-13-14-17-20

Surprisingly, students came back to me with no possible answer! SPSS provides no answer either for this type of problems as the denominator in t-test formula turns to be zero and calculations could not continue. Simply because a division by zero is undefined.

I am wondering if there is any solutions for such problems?

Mahmood

Using SPSS to group respondents

I have a large dataset containing many dates per participant. I want to creat a new variable containing the number of unique dates per participant. I have been trying to figure this out but it does not work. An example of how the dataset looks like you see below:

participant 1 - date.1 - date.2 - date.3 - date.4 ... date.412

participant 2 - date.1 - date.2 - date.3 - date.4 ... date.412

Dates are in the format: 31-oct-20

Some participants have only 10 dates, others have 412 dates

Any idea how I can create this new variable containing the number of unique dates?

Can anyone please explain how to analyse data collected using brief cope 28 item scale?

Like i won't get the total score rather than score for each subscale and then it is mentioned in a research to use normative data of a heart failure study for calculating percentile ranks. Can anyone please help as I dont get the data analysis part after collection using this scale?

Thank you this question has been answered.

Hello,

Question regarding my Master Thesis research design and approach. I'm doing research on a dataset If earnings management has a negative relationship with the use (AND degree) of alternative performance measures. It's an european data set so I also want to examine if the ESMA Guidelines had any effect on this relationship (moderator).

Whereas

- Earnings Management is measured by Modified Jones Model (numeric)

- Alternative performance measures (or Non-Gaap Earnings) is measured by a proxy (1 if mentioned; 0 otherwise) and measured by I/B/E/S earnings (numeric)

- ESMA is measured by a proxy (1 if > 2017; 0 otherwise)

Also got some controlevariables/covariaties but not necessary to mention right now.

My thesis supervisor can't help me with the fact what analysis I should run in SPSS to get to the right answers. I'm doing my best to find solutions on the internet or reading the book Discovering statistics using spss statistics (Andy Field). I just don't understand what I 'm supposed to do if my moderator is a proxy because I also assume a relationship with this proxy to the use and degree of Alternative performance measures (or Non-Gaap Earnings).

Can someone help me out please?

Hi,

I have just installed and used spss 29. I was using spss 27.

I am analyzing data with a crossed random effects mixed model.

I am using syntax for this type of analysis. With the exact same syntax and data base I obtain different results with spss 29 and spss 27!

Specifically, the same model (that I call model 3) run with spss 27 was not giving me a warning whereas with spss 29 I get a warning (The final Hessian matrix is not positive definite although all convergence criteria are satisfied. The MIXED procedure continues despite this warning. Validity of subsequent results cannot be ascertained.).

Another case: with a slightly simpler model that I call model 2, I have no warnings but the results with spss 27 and spss 29 are not identical (e.g. BIC is different).

Is anyone experiencing the same or similar ?

I am using SPSS to analyze measurements of systolic and diastolic blood pressure in late adolescence. I want to make a binary hypertension variable, so I need to calculate percentiles for SBP and DBP. However, I need to adjust these for height, age, and gender. Is there a way I can easily do this in SPSS?