Part 1 of the course will offer an introduction to SPSS and teach how to work with data saved in SPSS format. Part 2 will demonstrate how to work with SPSS syntax, how to create your own SPSS data files, and how to convert data in other formats to SPSS. Part 3 will teach how to append and merge SPSS files, demonstrate basic analytical procedures, and show how to work with SPSS graphics.

ABSTRACT

The Albero study analyzes the personal transitions of a cohort of high school students at the end of their studies. The data consist of (a) the longitudinal social network of the students, before (n = 69) and after (n = 57) finishing their studies; and (b) the longitudinal study of the personal networks of each of the participants in the research. The two observations of the complete social network are presented in two matrices in Excel format. For each respondent, two square matrices of 45 alters of their personal networks are provided, also in Excel format. For each respondent, both psychological sense of community and frequency of commuting is provided in a SAV file (SPSS). The database allows the combined analysis of social networks and personal networks of the same set of individuals.

INTRODUCTION

Ecological transitions are key moments in the life of an individual that occur as a result of a change of role or context. This is the case, for example, of the completion of high school studies, when young people start their university studies or try to enter the labor market. These transitions are turning points that carry a risk or an opportunity (Seidman & French, 2004). That is why they have received special attention in research and psychological practice, both from a developmental point of view and in the situational analysis of stress or in the implementation of preventive strategies.

The data we present in this article describe the ecological transition of a group of young people from Alcala de Guadaira, a town located about 16 kilometers from Seville. Specifically, in the “Albero” study we monitored the transition of a cohort of secondary school students at the end of the last pre-university academic year. It is a turning point in which most of them began a metropolitan lifestyle, with more displacements to the capital and a slight decrease in identification with the place of residence (Maya-Jariego, Holgado & Lubbers, 2018).

Normative transitions, such as the completion of studies, affect a group of individuals simultaneously, so they can be analyzed both individually and collectively. From an individual point of view, each student stops attending the institute, which is replaced by new interaction contexts. Consequently, the structure and composition of their personal networks are transformed. From a collective point of view, the network of friendships of the cohort of high school students enters into a gradual process of disintegration and fragmentation into subgroups (Maya-Jariego, Lubbers & Molina, 2019).

These two levels, individual and collective, were evaluated in the “Albero” study. One of the peculiarities of this database is that we combine the analysis of a complete social network with a survey of personal networks in the same set of individuals, with a longitudinal design before and after finishing high school. This allows combining the study of the multiple contexts in which each individual participates, assessed through the analysis of a sample of personal networks (Maya-Jariego, 2018), with the in-depth analysis of a specific context (the relationships between a promotion of students in the institute), through the analysis of the complete network of interactions. This potentially allows us to examine the covariation of the social network with the individual differences in the structure of personal networks.

PARTICIPANTS

The social network and personal networks of the students of the last two years of high school of an institute of Alcala de Guadaira (Seville) were analyzed. The longitudinal follow-up covered approximately a year and a half. The first wave was composed of 31 men (44.9%) and 38 women (55.1%) who live in Alcala de Guadaira, and who mostly expect to live in Alcala (36.2%) or in Seville (37.7%) in the future. In the second wave, information was obtained from 27 men (47.4%) and 30 women (52.6%).

DATE STRUCTURE AND ARCHIVES FORMAT

The data is organized in two longitudinal observations, with information on the complete social network of the cohort of students of the last year, the personal networks of each individual and complementary information on the sense of community and frequency of metropolitan movements, among other variables.

Social network

The file “Red_Social_t1.xlsx” is a valued matrix of 69 actors that gathers the relations of knowledge and friendship between the cohort of students of the last year of high school in the first observation. The file “Red_Social_t2.xlsx” is a valued matrix of 57 actors obtained 17 months after the first observation.

The data is organized in two longitudinal observations, with information on the complete social network of the cohort of students of the last year, the personal networks of each individual and complementary information on the sense of community and frequency of metropolitan movements, among other variables.

In order to generate each complete social network, the list of 77 students enrolled in the last year of high school was passed to the respondents, asking that in each case they indicate the type of relationship, according to the following values: 1, “his/her name sounds familiar"; 2, "I know him/her"; 3, "we talk from time to time"; 4, "we have good relationship"; and 5, "we are friends." The two resulting complete networks are represented in Figure 2. In the second observation, it is a comparatively less dense network, reflecting the gradual disintegration process that the student group has initiated.

Personal networks

Also in this case the information is organized in two observations. The compressed file “Redes_Personales_t1.csv” includes 69 folders, corresponding to personal networks. Each folder includes a valued matrix of 45 alters in CSV format. Likewise, in each case a graphic representation of the network obtained with Visone (Brandes and Wagner, 2004) is included. Relationship values range from 0 (do not know each other) to 2 (know each other very well).

Second, the compressed file “Redes_Personales_t2.csv” includes 57 folders, with the information equivalent to each respondent referred to the second observation, that is, 17 months after the first interview. The structure of the data is the same as in the first observation.

Sense of community and metropolitan displacements

The SPSS file “Albero.sav” collects the survey data, together with some information-summary of the network data related to each respondent. The 69 rows correspond to the 69 individuals interviewed, and the 118 columns to the variables related to each of them in T1 and T2, according to the following list:

 • Socio-economic data.


 • Data on habitual residence.


 • Information on intercity journeys.


 • Identity and sense of community.


 • Personal network indicators.


 • Social network indicators.

DATA ACCESS

Social networks and personal networks are available in CSV format. This allows its use directly with UCINET, Visone, Pajek or Gephi, among others, and they can be exported as Excel or text format files, to be used with other programs.

The visual representation of the personal networks of the respondents in both waves is available in the following album of the Graphic Gallery of Personal Networks on Flickr: .

In previous work we analyzed the effects of personal networks on the longitudinal evolution of the socio-centric network. It also includes additional details about the instruments applied. In case of using the data, please quote the following reference:

Maya-Jariego, I., Holgado, D. & Lubbers, M. J. (2018). Efectos de la estructura de las redes personales en la red sociocéntrica de una cohorte de estudiantes en transición de la enseñanza secundaria a la universidad. Universitas Psychologica, 17(1), 86-98. https://doi.org/10.11144/Javeriana.upsy17-1.eerp

The English version of this article can be downloaded from: https://tinyurl.com/yy9s2byl

CONCLUSION

The database of the “Albero” study allows us to explore the co-evolution of social networks and personal networks. In this way, we can examine the mutual dependence of individual trajectories and the structure of the relationships of the cohort of students as a whole. The complete social network corresponds to the same context of interaction: the secondary school. However, personal networks collect information from the different contexts in which the individual participates. The structural properties of personal networks may partly explain individual differences in the position of each student in the entire social network. In turn, the properties of the entire social network partly determine the structure of opportunities in which individual trajectories are displayed.

The longitudinal character and the combination of the personal networks of individuals with a common complete social network, make this database have unique characteristics. It may be of interest both for multi-level analysis and for the study of individual differences.

ACKNOWLEDGEMENTS

The fieldwork for this study was supported by the Complementary Actions of the Ministry of Education and Science (SEJ2005-25683), and was part of the project “Dynamics of actors and networks across levels: individuals, groups, organizations and social settings” (2006 -2009) of the European Science Foundation (ESF). The data was presented for the first time on June 30, 2009, at the European Research Collaborative Project Meeting on Dynamic Analysis of Networks and Behaviors, held at the Nuffield College of the University of Oxford.

REFERENCES

Brandes, U., & Wagner, D. (2004). Visone - Analysis and Visualization of Social Networks. In M. Jünger, & P. Mutzel (Eds.), Graph Drawing Software (pp. 321-340). New York: Springer-Verlag.

Maya-Jariego, I. (2018). Why name generators with a fixed number of alters may be a pragmatic option for personal network analysis. American Journal of

The Current Population Survey Civic Engagement and Volunteering (CEV) Supplement is the most robust longitudinal survey about volunteerism and other forms of civic engagement in the United States. Produced by AmeriCorps in partnership with the U.S. Census Bureau, the CEV takes the pulse of our nation’s civic health every two years. The data on this page was collected in September 2023. The next wave of the CEV will be administered in September 2025.

The CEV can generate reliable estimates at the national level, within states and the District of Columbia, and in the largest twelve Metropolitan Statistical Areas to support evidence-based decision making and efforts to understand how people make a difference in communities across the country.

Click on "Export" to download and review an excerpt from the 2023 CEV Analytic Codebook that shows the variables available in the analytic CEV datasets produced by AmeriCorps.

Click on "Show More" to download and review the following 2023 CEV data and resources provided as attachments:

1) 2023 CEV Dataset Fact Sheet – brief summary of technical aspects of the 2023 CEV dataset. 2) CEV FAQs – answers to frequently asked technical questions about the CEV 3) Constructs and measures in the CEV 4) 2023 CEV Analytic Data and Setup Files – analytic dataset in Stata (.dta), R (.rdata), SPSS (.sav), and Excel (.csv) formats, codebook for analytic dataset, and Stata code (.do) to convert raw dataset to analytic formatting produced by AmeriCorps. These files were updated on January 16, 2025 to correct erroneous missing values for the ssupwgt variable. 5) 2023 CEV Technical Documentation – codebook for raw dataset and full supplement documentation produced by U.S. Census Bureau 6) 2023 CEV Raw Data and Read In Files – raw dataset in Stata (.dta) format, Stata code (.do) and dictionary file (.dct) to read ASCII dataset (.dat) into Stata using layout files (.lis)

Compassion has been a subject of extensive scientific research for over two decades. There is clear evidence that our capacity for compassion evolved out of care motivation. Like all motivations it is operated via stimulus response algorithms. For compassion motivation stimulus sensitivity focuses on the processing of indicators of suffering, distress and need, called engagement. The response functions switch attention and processing to what is likely to be helpful in alleviating suffering, distress and need, called action. The Compassion Engagement and Action Scales (CEAS) were developed to measure the S-R algorithm of compassion. Because compassion, like other psychological phenomena can operate interpersonally and intrapersonally, there are three scales that give separate assessments for directing compassion to 1. the self, 2. others and 3. responsiveness to compassion from others. They have been used in many international studies and there is now substantial evidence. The research aimed to validate the CEAS within a Polish population. The three cross-sectional studies involved a total of 1,219 participants from Poland. Confirmatory factor analysis conducted on two separate samples indicates that bifactor models provide the best fit for both the Compassion for Others scale and the Compassion from Others scale. In the first, the model includes a general compassion for others factor alongside specific factors for engagement and actions. Similarly, the second features a general compassion from others factor with the same specific factors. This means that being sensitive to suffering and taking action represent specific components of compassion. However, the bifactor model for Compassion for Self requires further refinement due to lower fit indices and the need for item adjustments. The study results generally support the reliability and validity of the CEAS-PL across diverse samples, aligning with findings from previous studies on the original tool and its language adaptations. Notably, tests of validity—including correlations with emotion regulation, well-being, and attachment styles—highlighted distinct patterns for the three flows of compassion, underscoring their conceptual independence. The CEAS-PL shows promise as a valuable tool for psychological research and practice, especially in the areas of pro-social behaviour and helping people with mental health problems, facilitating the assessment of compassion across different orientations. It may support practitioners in identifying individual competencies and tailoring interventions to enhance compassion-related competencies to address particular difficulties.

Abstract

The Kenya National Micronutrients Survey (NMS) 2011 was the first NMS to be carried by the Kenya National Bureau of Statistics. The purpose of this survey is to ensure the quality of HIV testing and the interpretation of results, both in the laboratory and in the community. Fort HIV testing, it is extremely important that "the correct results go to the right client". The identity of clients and the labelling of test devices should therefore be preserved properly.

Geographic coverage

National

Analysis unit

• Households
• Individuals

Universe

The survey covered household members (usual residents), womens questinnaire( aged 15-49 years) resident in the household, children( aged 0-6-49months), School age children (aged 5-14 years) resident in the household and Men questionnire (aged 15-54 year).

Kind of data

Sample survey data [ssd]

Sampling procedure

Sample size estimation The sample size required for each stratum was based on the estimated prevalence for each nutritional indicator, the desired precision for each indicator, an assumed design effect of 2.0, and a non-response of 10% (including refusals) at the household level and 10% at the individual levels for children 6-59 months of age and non-pregnant women. An additional non-response rate of 10% (for a total 30% non-response rate) was assumed for the men and SAC 5-14 years old.

Sampling design In 2010, Kenya ratified a new constitution which established 47 county governments. This change has highlighted the need for national surveys to collect information beyond the provincial level, and move towards collection of county-level estimates. However, obtaining county-level estimates with adequate precision were not considered feasible in KNMS due to limitations in sample size and resources. Therefore KNMS consisted of the three domains as defined earlier. The sampling frame for the 2010 KMNS was based on the National Sample Survey and Evaluation Programme (NASSEP IV) master sampling frame maintained by the Kenya National Bureau of Statistics (KNBS). Administratively, Kenya is divided into 8 provinces. In turn, each province is The Kenya National Micronutrient Survey 2011 subdivided into districts, each district into divisions, each division into locations and each location into sub-locations. In addition to these administrative units, during the last 1999 population census, each sub-location was subdivided into census Enumeration Areas (EAs) i.e. small geographic units with clearly defined boundaries. As defined in the 1999 census, Kenya has eight provinces, 69 districts, and approximately 62,000 EAs. The list of EAs is grouped by administrative units and includes information on the number of households and population. This information was used in 2002 to design a master sample with about 1,800 selected EAs. The cartographic material for each EA in the master sample was updated in the field. The resulting master sampling frame was NASSEP IV which is still currently used by KNBS. The NASSEP IV master frame is a two-stage stratified cluster sample format. The first stage is a selection of Primary Sampling Units (PSUs), which are the EAs using probability proportional to measure of size (PPMOS) method. The second stage involves the selection of households for various surveys. EAs are selected with a basis of one Measure of Size (MOS) defined as the ultimate cluster with an average of 100 households and constitute one (or more) EAs. Although consideration was given to development of a new master frame for KNMS, time and other resource constraints dictated that the sample frame of this survey was NASSEP IV. The KNMS sample was selected using a stratified two-stage cluster design consisting of 296 clusters, 123 in the urban and 173 in the rural areas. From each cluster a total of 10 households were selected using systematic simple random sampling. For the KNMS survey, an urban area was defined as "an area with an increased density of human-created structures in comparison to the areas surrounding it and has a population of 2,000 people and above". Using this definition, urban areas included Cities, Municipalities, Town Councils, Urban Councils and all District Headquarters. A rural area was defined as an isolated large area of an open country in reference to open fields with peoples whose main economic activity was farming. Every attempt was made to conduct interviews in the 10 selected households, and one additional visit was made to ascertain this compliance in cases of absence of household members to minimize potential bias. Non responding households were not replaced.

Mode of data collection

Face-to-face [f2f]

Research instrument

The survey covers household members questionnaire (usual residents), women questinnaire ( aged 15-49 years), preschool children questionnarie( aged 6-59 months), school age children questionnaire (aged 5-14 years) and men questionnire (aged 15-54 year). The hosehold member questionnaire includes: Identification, Interviewer Visits, Socio demographic characteristics, Socio-economic characteristics, Food fortification, Wheat flour fortification, Salt fortification, Sugar fortification, Oils/fats fortification, Interviewer's observations. The women questionnarie includes: Identification, Interviewer Visits, Micronutrient Supplementation and Pica Questions, WRA Health questions. The school age children questionnaire includes: Identification, Interviewer Visits, Micronutrient Supplementation and Pica Questions, Child Health questions, Dietary Diversity Score Questions, Infant Feeding Practice Questions children 6-35 months, Interviewer Observations, The preschool children questionnarie includes: Identification, Interviewer Visits, Micronutrient Supplementation and Pica Questions, Child Health questions, Interviewer Observations. The men questionnarie includes: Identification, Interviewer Visits, Health questions, Interviewer Observations.

Cleaning operations

The field questionnaires baring household characteristics, individual population characteristics, and anthropometrics measurements were double entered into a computer database designed using MS-Access application. Regular file back-up was done using flash disks and external hard disk to avoid any loss or tampering. Data comparison was done using Epi-info version 7.0. Data cleaning and validation was performed to achieve clean datasets. The datasets were exported into a Statistical Package format (IBM® SPSS® Statistics version 20.0). The laboratory results were entered in excel format and later exported into a Statistical Package format (IBM® SPSS®Statistics version 20.0). Data merging exercise was systematically conducted using the four datasets i.e. household characteristics, individual population characteristics, anthropometrics measurements, and laboratory results. Each of the five populations namely; Pre-school children (PSC), School aged children (SAC), Pregnant women (PW), Non-pregnant women (NPW), and Men were separately merged. Data merging was conducted as follows: STEP1: The 'laboratory results' file was first merged to the 'anthropometrics' file using 'LABLE NUMBER' as the unique identifier. STEP2: The merged 'laboratory + anthropometrics' file was merged to individual population characteristics file using a merging variable constructed by concatenating 'CLUSTER NUMBER + HOUSEHOLD NUMBER + LINE NUMBER' as the unique identifier. STEP3: The merged 'laboratory + anthropometrics + individual population characteristics' file was merged to the 'household characteristics' file using a merging variable constructed by concatenating 'CLUSTER NUMBER + HOUSEHOLD NUMBER + LINE NUMBER' as the unique identifier. Five master-files were backed-up for safe keeping and a copy was shared with the statisticians for analysis. All the questionnaires and laboratory forms were filed and stored in lockable drawers for confidentiality.

The validated data was exported to SPSS Version 20 for analysis.