Transparency in data visualization is an essential ingredient for scientific communication. The traditional approach of visualizing continuous quantitative data solely in the form of summary statistics (i.e., measures of central tendency and dispersion) has repeatedly been criticized for not revealing the underlying raw data distribution. Remarkably, however, systematic and easy-to-use solutions for raw data visualization using the most commonly reported statistical software package for data analysis, IBM SPSS Statistics, are missing. Here, a comprehensive collection of more than 100 SPSS syntax files and an SPSS dataset template is presented and made freely available that allow the creation of transparent graphs for one-sample designs, for one- and two-factorial between-subject designs, for selected one- and two-factorial within-subject designs as well as for selected two-factorial mixed designs and, with some creativity, even beyond (e.g., three-factorial mixed-designs). Depending on graph type (e.g., pure dot plot, box plot, and line plot), raw data can be displayed along with standard measures of central tendency (arithmetic mean and median) and dispersion (95% CI and SD). The free-to-use syntax can also be modified to match with individual needs. A variety of example applications of syntax are illustrated in a tutorial-like fashion along with fictitious datasets accompanying this contribution. The syntax collection is hoped to provide researchers, students, teachers, and others working with SPSS a valuable tool to move towards more transparency in data visualization.

From the massive set of V-Dem v 12 variables, we have inserted 27 in the main CPEDB dataset. Here is the entire set, organized in an excel file to match their country/year rows in the main SPSS file. This precise correspondence makes it easy to insert other variables from the V-Dem dataset into the main file where they can be statistically combined with a wide variety of variables from other sources.

analyze the current population survey (cps) annual social and economic supplement (asec) with r the annual march cps-asec has been supplying the statistics for the census bureau's report on income, poverty, and health insurance coverage since 1948. wow. the us census bureau and the bureau of labor statistics ( bls) tag-team on this one. until the american community survey (acs) hit the scene in the early aughts (2000s), the current population survey had the largest sample size of all the annual general demographic data sets outside of the decennial census - about two hundred thousand respondents. this provides enough sample to conduct state- and a few large metro area-level analyses. your sample size will vanish if you start investigating subgroups b y state - consider pooling multiple years. county-level is a no-no. despite the american community survey's larger size, the cps-asec contains many more variables related to employment, sources of income, and insurance - and can be trended back to harry truman's presidency. aside from questions specifically asked about an annual experience (like income), many of the questions in this march data set should be t reated as point-in-time statistics. cps-asec generalizes to the united states non-institutional, non-active duty military population. the national bureau of economic research (nber) provides sas, spss, and stata importation scripts to create a rectangular file (rectangular data means only person-level records; household- and family-level information gets attached to each person). to import these files into r, the parse.SAScii function uses nber's sas code to determine how to import the fixed-width file, then RSQLite to put everything into a schnazzy database. you can try reading through the nber march 2012 sas importation code yourself, but it's a bit of a proc freak show. this new github repository contains three scripts: 2005-2012 asec - download all microdata.R down load the fixed-width file containing household, family, and person records import by separating this file into three tables, then merge 'em together at the person-level download the fixed-width file containing the person-level replicate weights merge the rectangular person-level file with the replicate weights, then store it in a sql database create a new variable - one - in the data table 2012 asec - analysis examples.R connect to the sql database created by the 'download all microdata' progr am create the complex sample survey object, using the replicate weights perform a boatload of analysis examples replicate census estimates - 2011.R connect to the sql database created by the 'download all microdata' program create the complex sample survey object, using the replicate weights match the sas output shown in the png file below 2011 asec replicate weight sas output.png statistic and standard error generated from the replicate-weighted example sas script contained in this census-provided person replicate weights usage instructions document. click here to view these three scripts for more detail about the current population survey - annual social and economic supplement (cps-asec), visit: the census bureau's current population survey page the bureau of labor statistics' current population survey page the current population survey's wikipedia article notes: interviews are conducted in march about experiences during the previous year. the file labeled 2012 includes information (income, work experience, health insurance) pertaining to 2011. when you use the current populat ion survey to talk about america, subract a year from the data file name. as of the 2010 file (the interview focusing on america during 2009), the cps-asec contains exciting new medical out-of-pocket spending variables most useful for supplemental (medical spending-adjusted) poverty research. confidential to sas, spss, stata, sudaan users: why are you still rubbing two sticks together after we've invented the butane lighter? time to transition to r. :D

This dataset contains raw data and their corresponding results files associated with a recent study. Each MS Excel spreadsheet entails the data for one aspect of study which is specified by name of the file.The information about participants i.e. personal and demographic, responses for first SD scale, second SD scale and personal evaluation are presented in each spreadsheet. The supplemental material (participant information sheet, informed consent form, online questionnaire, risk assessment form) are also enclosed with this dataset. Lastly, for the analysis of raw data, statistical test such as; independent sample t-test was performed. The original SPSS data files are also included.

Please find attached the raw coding data, the datasets and alayses codes for each analysis (Excel for sequential analysis and SPSS Syntax for correlations).

The Comparative Political Economy Database (CPEDB) began at the Centre for Learning, Social Economy and Work (CLSEW) at the Ontario Institute for Studies in Education at the University of Toronto (OISE/UT) as part of the Changing Workplaces in a Knowledge Economy (CWKE) project. This data base was initially conceived and developed by Dr. Wally Seccombe (independent scholar) and Dr. D.W. Livingstone (Professor Emeritus at the University of Toronto). Seccombe has conducted internationally recognized historical research on evolving family structures of the labouring classes (A Millennium of Family Change: Feudalism to Capitalism in Northwestern Europe and Weathering the Storm: Working Class Families from the Industrial Revolution to the Fertility Decline). Livingstone has conducted decades of empirical research on class and labour relations. A major part of this research has used the Canadian Class Structure survey done at the Institute of Political Economy (IPE) at Carleton University in 1982 as a template for Canadian national surveys in 1998, 2004, 2010 and 2016, culminating in Tipping Point for Advanced Capitalism: Class, Class Consciousness and Activism in the ‘Knowledge Economy’ (https://fernwoodpublishing.ca/book/tipping-point-for-advanced-capitalism) and a publicly accessible data base including all five of these Canadian surveys (https://borealisdata.ca/dataverse/CanadaWorkLearningSurveys1998-2016). Seccombe and Livingstone have collaborated on a number of research studies that recognize the need to take account of expanded modes of production and reproduction. Both Seccombe and Livingstone are Research Associates of CLSEW at OISE/UT. The CPEDB Main File (an SPSS data file) covers the following areas (in order): demography, family/household, class/labour, government, electoral democracy, inequality (economic, political & gender), health, environment, internet, macro-economic and financial variables. In its present form, it contains annual data on 725 variables from 12 countries (alphabetically listed): Canada, Denmark, France, Germany, Greece, Italy, Japan, Norway, Spain, Sweden, United Kingdom and United States. A few of the variables date back to 1928, and the majority date from 1960 to 1990. Where these years are not covered in the source, a minority of variables begin with more recent years. All the variables end at the most recent available year (1999 to 2022). In the next version developed in 2025, the most recent years (2023 and 2024) will be added whenever they are present in the sources’ datasets. For researchers who are not using SPSS, refer to the Chart files for overviews, summaries and information on the dataset. For a current list of the variable names and their labels in the CPEDB data base, see the excel file: Outline of SPSS file Main CPEDB, Nov 6, 2023. At the end of each variable label in this file and the SPSS datafile, you will find the source of that variable in a bracket. If I have combined two variables from a given source, the bracket will begin with WS and then register the variables combined. In the 14 variables David created at the beginning of the Class Labour section, you will find DWL in these brackets with his description as to how it was derived. The CPEDB’s variables have been derived from many databases; the main ones are OECD (their Statistics and Family Databases), World Bank, ILO, IMF, WHO, WIID (World Income Inequality Database), OWID (Our World in Data), Parlgov (Parliaments and Governments Database), and V-Dem (Varieties of Democracy). The Institute for Political Economy at Carleton University is currently the main site for continuing refinement of the CPEDB. IPE Director Justin Paulson and other members are involved along with Seccombe and Livingstone in further development and safe storage of this updated database both at the IPE at Carleton and the UT dataverse. All those who explore the CPEDB are invited to share their perceptions of the entire database, or any of its sections, with Seccombe generally (wseccombe@sympatico.ca) and Livingstone for class/labour issues (davidlivingstone@utoronto.ca). They welcome any suggestions for additional variables together with their data sources. A new version CPEDB will be created in the spring of 2025 and installed as soon as the revision is completed. This revised version is intended to be a valuable resource for researchers in all of the included countries as well as Canada.

In 1910, Franz Boas published the first results from his classic study, Changes in Bodily Form of Descendants of Immigrants. This landmark work became controversial almost immediately, as it challenged many prevailing ideas about human biology and race. The most striking finding at the time was that head shape—long thought to be a fixed, purely hereditary marker of race—was in fact sensitive to changes in environment within a single generation.Boas’s most impressive response to the controversy was his decision in 1928 to publish 504 pages of raw, handwritten data from the immigrant study as Materials for the Study of Inheritance in Man (New York: Columbia University Press). He explained: "It seemed necessary to make the data accessible, because a great many questions relating to heredity and environmental influences may be treated by means of this material." In the same spirit, here we provide the machine-readable data set that is the basis of our published reanalysis of Boas’s data set.The data are provided in two structures:Files labeled "master" are formatted to match Boas’s original. Each individual is assigned to a unique case.Files labeled "family" facilitate parent-offspring comparisons. Second-generation immigrants are assigned to cases, with data for each descendant’s mother and father assigned as variables.Both data structures are available as SPSS files (.sav) and as ASCII text.

This dataset contains daily retail gasoline prices, for the fuel types diesel and euro95, in The Netherlands for the period October 1, 2005 to April 25, 2011 for almost every gasoline station selling fuel to consumers.

Explanation tab 'Data files':
- The folder 'orginal' contains project documentation, XML file and STATA files as deposited by the depositor.
Part of these files, the STATA files, have been converted into SPSS sav and por files by DANS for sustainability purposes and the benefit of SPSS users. You can find these files in the folder 'SPSS conversions by DANS'.

This article presents a detailed notational analysis of women’s pickleball, focusing on the technical-tactical aspects that define performance in the sport. The study examines the court zones most utilized during points and the types of shots that determine point outcomes, such as forehands, backhands, and volleys. Through the analysis of 15 matches from five tournaments of the 2023 PPA Pro Tour, common gameplay patterns and significant differences in shot execution between women and men are identified. The results suggest that, while both genders display a combination of baseline and net strategies, women tend to focus more on baseline shots. Additionally, a high incidence of unforced errors is observed, emphasizing the importance of consistency and accuracy in play. Finally, the article provides practical applications to improve pickleball performance based on the observed findings.In the directory, three files are available. The SPSS subdirectory contains the database file for use with IBM's Statistical Package for the Social Sciences (SPSS). Additionally, the THEME6 subdirectory includes two files compatible with the Theme 6 Edu software for T-Pattern analysis. If using Theme 5, the :and & categories must be added to the "Start-End" criterion in the VVT file. Without this adjustment, the file will not function properly.--------------Este artículo presenta un análisis notacional detallado de la categoría femenina de pickleball, centrándose en los aspectos técnico-tácticos que definen el rendimiento en este deporte. Se examinan las zonas de la pista más utilizadas durante los puntos y los tipos de golpes que determinan el desenlace de las jugadas, como los golpes de derecha, revés y voleas. A través del análisis de 15 partidos de cinco torneos del PPA Pro Tour 2023, se identifican patrones de juego comunes y diferencias significativas en la ejecución de los golpes entre mujeres y hombres. Los resultados sugieren que, aunque ambos géneros muestran una combinación de tácticas de fondo y de red, las jugadoras tienden a centrarse más en los golpes de fondo. Además, se destaca la alta incidencia de errores no forzados, lo que subraya la importancia de la consistencia y precisión en el juego. Finalmente, el artículo ofrece aplicaciones prácticas para mejorar el rendimiento en pickleball, basadas en los hallazgos observados.En el directorio se encuentran tres archivos. En el subdirectorio SPSS se incluye el archivo de la base de datos diseñado para su uso con el software IBM SPSS (Statistical Package for the Social Sciences). Por otro lado, en el subdirectorio THEME6 se encuentran dos archivos compatibles con el programa Theme 6 Edu para la búsqueda de T-Patterns. Si se utiliza Theme 5, será necesario añadir al archivo VVT el criterio "Inicio-Fin" con las categorías : y &. De no realizar esta modificación, el archivo no funcionará correctamente.

Data of three studies reported in the above manuscript. All datafiles are provided in MLWIN/WSZ-format and in SPSS-file (Dataverse does not support MLWIN/WSZ-files but the data were analyzed in this program rather than in SPSS). For each study (1, 2, 3) two datafiles are uploaded, one for the accuracy scores and one for the reaction times. An Excel-file with information about the stimulus materials (e.g., word frequency of the manipulated words) has also been uploaded.

please move to the note to see the specific description ICD-9 file show the relationship with ICD-9 code and the disease name in Chinese .csv and excel shows the specific infomation for each record .png shows the Chinese Explanation from v10 to v99 .sav files are SPSS files corresponded with each disease

you can compare these data and costs with DRGs system

The project aimed to understand whether young adults who take care of a loved-one (young adult caregivers; YACs) differ in their perceived life balance and psychosocial functioning as compared to young adults without care responsibilities (non-YACs). In addition, this project aimed to understand how YACs evaluated a tool to support informal careg

ivers. This tool (“Caregiver Balance”; https://balans.mantelzorg.nl) is specifically designed to support informal caregivers taking care of a loved-one in the palliative phase and could potentially be adapted to meet the needs of YACs.

In this project, we collected data of 74 YACs and 246 non-YACs. Both groups completed questionnaires, and the YACs engaged in a usability test. The questionnaire data was used to compare the perceived life balance and psychological functioning between YACs and non-YACs, aged 18-25 years, and studying in the Netherlands (study 1). Furthermore, we examined the relationship between positive aspects of caregiving and relational factors, in particular, relationship quality and collaborative coping among YACs (study 2). Finally, we conducted a usability study where we interviewed YACs to understand the needs and preferences towards a supportive web-based solution (study 3).

Table: Study details and associated files

    Number
    Study Name
    Study Aim
    Study Type
    Type of data
    Associated Files


    1
    Perceived life balance among young adult students: a comparison between caregivers and non-caregivers
    Compare the perceived life balance and psychological functions among student young adult caregivers aged 18-25 years (YACs) with young adult without care responsibilities
    Survey study
    Quantitative

ENTWINE_YACs_nonYACsSurvey_RawData

ENTWINE_PerceivedLifeBalanceSurvey_YACs_nonYACs_CleanedData

ENTWINE_ PerceivedLifeBalanceSurvey _Syntax

ENTWINE_YACs_nonYACsSurvey_codebook

    2
    Examining the relationship of positive aspects of caregiving with relational factors among young adult caregivers
    Examine the relationship of positive aspects of caregiving with relational factors, in particular, relationship quality and collaborative coping among a particular group of ICGs, young adult caregivers (YACs), aged 18-25 years.
    Survey study
    Quantitative

ENTWINE_YACs_nonYACsSurvey_RawData

ENTWINE_PositiveAspectsCaregiving_Survey_YACs_cleanedData

ENTWINE_PositiveAspectsCaregiving_Survey_Syntax

ENTWINE_YACs_nonYACsSurvey_codebook

    3
    Exploring the support needs of young adult caregivers, their issues, and preferences towards a web-based tool
    Explore (i) challenges and support needs of YACs in caregiving, (ii) their needs towards the content of the ‘MantelzorgBalans’ tool, and (iii) issues they encountered in using the tool and their preferences for adaptation of the tool.
    Usability study

Qualitative and Quantitative

ENTWINE_Needs_Web-basedTools_YACs_Interview_Usability_RawData [to be determined whether data can be shared]

ENTWINE_Needs_Web-basedTools_YACs_Questionnaires_RawData

Description of the files to be uploaded

Study 1: Perceived life balance among young adult students: a comparison between caregivers and non-caregivers

ENTWINE_YACs_nonYACsSurvey_RawData: SPSS file with the complete, raw, pseudonomyzed survey data. The following cleaned dataset ‘ENTWINE_PerceivedLifeBalanceSurvey_YACs_nonYACs_CleanedData’ was generated from this raw data.

ENTWINE_PerceivedLifeBalanceSurvey_YACs_nonYACs_CleanedData: SPSS file with the cleaned dataset having the following metadata -

Population: young adult caregivers and young adult non-caregivers aged 18-25 years studying in the Netherlands;

Number of participants: 320 participants in total (74 young adult caregivers and 246 young adult non-caregivers)

Time point of measurement: Data was collected from December 2020 till March 2022

Type of data: quantitative

Measurements included, topics covered: perceived life balance (based on the Occupational balance questionnaire [1]), burnout (Burnout Assessment Tool [2]), negative and positive affect (Positive and Negative Affect Schedule [3]), and life satisfaction (Satisfaction with Life Scale [4])

Short procedure conducted to receive data: online survey on Qualtrics platform

SPSS syntax file ‘ENTWINE_ PerceivedLifeBalanceSurvey _Syntax’ was used to clean and analyse ENTWINE_PerceivedLifeBalanceSurvey_YACs_nonYACs_CleanedData dataset

ENTWINE_YACs_nonYACsSurvey_codebook: Codebook having the variable names, variable labels, and the associated code values and code labels for ENTWINE_PerceivedLifeBalanceSurvey_YACs_nonYACs_CleanedData dataset

Study 2: Examining the relationship of positive aspects of caregiving with relational factors among young adult caregivers

ENTWINE_YACs_nonYACsSurvey_RawData: SPSS file with the complete, raw survey data. The following cleaned dataset ‘ENTWINE_PositiveAspectsCaregiving_Survey_YACs_cleanedData’ was generated from this raw data.

ENTWINE_PositiveAspectsCaregiving_Survey_YACs_cleanedData: SPSS file with the cleaned dataset having the following metadata -

Population: young adult caregivers aged 18-25 years studying in the Netherlands;

Number of participants: 74 young adult caregivers

Time point of measurement: Data was collected from December 2020 till March 2022

Type of data: quantitative

Measurements included, topics covered: positive aspects of caregiving (positive aspects of caregiving scale [5]), relationship quality (Relationship Assessment Scale [6]), collaborative coping (Perception of Collaboration Questionnaire [7] )

Short procedure conducted to receive data: online survey on Qualtrics platform.

SPSS syntax file ‘ENTWINE_PositiveAspectsCaregiving_Survey_Syntax’ was used to clean and analyse ‘ENTWINE_PositiveAspectsCaregiving_Survey_YACs_cleanedData’ dataset.

ENTWINE_YACs_nonYACsSurvey_codebook: Codebook having the variable names, variable labels, and the associated code values and code labels for ENTWINE_PositiveAspectsCaregiving_Survey_YACs_cleanedData dataset.

Study 3: Exploring the support needs of young adult caregivers, their issues, and preferences towards a web-based tool

ENTWINE_Needs_Web-basedTools_YACs_Interview_Usability_RawData: Pseudonymized word file including 13 transcripts having the qualitative data from interview and usability testing with the following metadata –

Population: young adult caregivers aged 18-25 years studying in the Netherlands; 13 participants in total

Time point of measurement: data was collected from October 2021 till February 2022

Type of data: qualitative and quantitative

Measurements included, topics covered: Caregiving challenges, support needs and barriers, usability needs, preferences and issues towards eHealth tool

Short procedure conducted to receive data: Online interviews

ENTWINE_Needs_Web-basedTools_YACs_Questionnaires_RawData: Excel sheet having the quantitative questionnaire raw data with the following metadata

Population: young adult caregivers aged 18-25 years studying in the Netherlands; 13 participants in total

Time point of measurement: data was collected from October 2021 till February 2022

Type of data: qualitative and quantitative

Measurements included, topics covered: User experience (user experience questionnaire [8]), satisfaction of using the web-based tool (After scenario questionnaire [9]), Intention of use and persuasive potential of the eHealth tool (persuasive potential questionnaire [10])

Short procedure conducted to receive data: Online questionnaire

Data collection details

All data was collected, processed, and archived in accordance with the General Data Protection Regulation (GDPR) and the FAIR (Findable, Accessible, Interoperable, Reusable) principles under the supervision of the Principal Investigator.

The principal researcher and a team of experts (supervisors) in the field of health psychology and eHealth (University of Twente, The Netherlands) reviewed the scientific quality of the research. The studies were piloted and tested before starting the collection of the data. For the survey study, the researchers monitored the data collection weekly to ensure it was running smoothly.

The ethical review board, Centrale Ethische Toetsingscommissie of the University Medical Center Groningen, The Netherlands (CTc), granted approval for this research (Registration number: 202000623).

Participants digitally signed informed consent for participating in the study.

Terms of use

Interested persons can send a data request by contacting the principal investigator (Prof. dr. Mariët Hagedoorn, University Medical Center Groningen, the Netherlands mariet.hageboorn@umcg.nl).

Interested persons must provide the research plan (including the research question, methodology, and analysis plan) when requesting for the data.

The principal investigator reviews the research plan on its quality and fit with the data and informs the interested person(s).

(Pseudo)anonymous data of those participants who agreed on the reuse of their data is available on request for 15 years from the time of completion of the PhD project.

Data will be available in Excel or SPSS format alongside the variable codebook after the completion of this PhD project and publication of the study results.

References

1. Wagman P, Håkansson C. Introducing the Occupational Balance Questionnaire (OBQ). Scand J Occup Ther 2014;21(3):227–231. PMID:24649971

2. Schaufeli WB, Desart S, De Witte H. Burnout assessment tool (Bat)—development, validity, and reliability. Int J Environ Res Public Health 2020;17(24):1–21. PMID:33352940

3. Watson D, Clark LA, Tellegen A. Development and Validation of Brief Measures of Positive and Negative Affect: The

On this project you will find the data and syntax (All data and syntax files are in SPSS).

The Cohort Hip & Cohort Knee (CHECK) is a population-based observational multicenter cohort study of 1002 individuals with early symptomatic osteoarthritis (OA) of knee and/or hip in the Netherlands. The participants were followed for 10 years. The study evaluated clinical, radiographic and biochemical variables in order to establish the course, prognosis and underlying mechanisms of early symptomatic osteoarthritis. The Dutch Artritis Foundation initiated and funded this inception cohort.This dataset covers the data collection from baseline to 5 years follow-up: T0, T1, T2, T3, T4 and T5. All data files include the variable 'Subject identification number’. Included is a Kellgren-Lawrence radiographic classification covering T0,T2,T5, T8 and T10. Also X-rays of hips and knees of baseline, 2 and 5 year follow-up visits are available. More information on the variables can be found in the documentation.In the description file you can find an overview of the data belonging to this dataset and more information about the format and kind of view of the X rays.See relations for other CHECK datasets and for the overview 'Thematic collection: CHECK (Cohort Hip & Cohort Knee)'. Date Submitted: 2015-12-24 2019-12-20: a new data file on X-Ray data 'Rontgen_opT10_20191118' was added to the dataset.2017-10-04: A data file on X-Ray ratings has been added and the variable guide is replaced by a new version (6) with information on this data file. Please note the variable names start with 'RontgT10_' in the data file.2017-07-12: Due to an error a data file has been replaced. CHECK_T0_DANS_ENG_20151211.sav is now replaced by CHECK_T0_DANS_ENG_20161128.sav.---The informed consent statements of the participants are stored at the participating hospitals.The .dta (STATA) and .por (SPSS) files are conversions of the original .sav (SPSS) files.

1. Raw Data in Spss-all variables

In the Spss file, you can find the scores of 68 students (12 years old; Female = 34, Male = 34 ) originally from an intervention study. Student's emotional awareness (EA) and Internalizing problems (IP) score measured at Time1 and one month later at Time 2. For measuring EA, the level of emotional awareness scale (LEAS-C) and for measuring IP, the Strength and Difficulty questionnaire is used. The internalizing problems score obtained by summing up the emotional and peer problem scales.

1. R-script for the Cross-Lagged Panel Model (CLPM) and two-factor latent model using Lavaan package (Rosseel, 2012)

The present study aimed to create a quantitative screening tool for amusia and to compare variables which are related to the occurrence of this disorder. A small percentage of the population suffers from amusia, namely, difficulty perceiving music and acquiring musical skills, despite their normal hearing, language, and intelligence. Based on the pre-existing data of its diagnosis and studying the deficits of this disorder, the present detection tool was created with laboratory evaluation and contains seven acoustic tests: "dissonant intervals", "out of tone", "contour", "memory", "rhythm", "integration" and "emotion". The participants were 200 people (80 students and 120 adults). According to the findings, only 4% suffer from amusia and it was found that those who have music education more than two years present higher performance in the present test than the others. In conclusion, factors such as gender and age do not affect the existence of this musical disorder.

BackgroundMaternal mortality ratio (MMR) has decreased worldwide but Pakistan is still striving towards achieving the SDG targets for maternal health. This study highlights the trends in maternal mortality levels and risk factors in Pakistan between 2007 and 2019.MethodsThis study compares the results of secondary data analysis of the Pakistan Maternal Mortality Survey 2019 with the Pakistan Demographic and Health Survey 2007. A nested case-control study was carved to compare maternal deaths with the women who survived a pregnancy, in the same sampling clusters during the same period. Logistic regression was used to estimate odds ratios (OR) for major risk factors of maternal mortality after adjusting for the women’s age, parity, education, and wealth quintile.ResultsIn 2019, Pakistan’s MMR was 186 per 100,000 live births, registering a 33% decline from 2007 (rural 42% vs. urban 11%). The leading causes of maternal mortality were postpartum hemorrhage, hypertensive disease of pregnancy, postpartum infection, and post-abortion complications. Women > 35 years and those expecting their first child were more likely to die from childbirth, while those who had ever used family planning had a lower risk according to the data for both years. In 2007, a distance of > 40 kilometers to a hospital significantly increased the risk of mortality but this association was not significant in 2019. In 2019, women who died were more likely to receive antenatal care than those who survived (adjusted OR 9.3); this association was not significant in 2007.ConclusionThe modest reduction in MMR can be attributed to improved access to maternal health services in rural areas with increased antenatal care and institutional deliveries. However, most maternal deaths were caused by poor accessibility to quality emergency obstetric care. Lack of family planning remains a major risk factor for high maternal mortality in Pakistan.