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Introduction: Meta-analysis can be conceptualized as a multilevel analysis: effect sizes are nested within studies. Effect sizes vary due to sampling variance at Level 1, and possibly also due to systematic differences across studies at Level 2. Therefore, multilevel models and software can be used to perform meta-analysis. An advantage of using the multilevel framework for doing meta-analyses is the flexibility of multilevel models. For instance, additional levels can be added to deal with dependent effect sizes within and between studies. In primary studies, it is common to report multiple effect sizes extracted from the same sample. Also, studies might belong to different higher-level clusters, as countries or research groups. These two scenarios generate dependency among effect sizes, and for appropriately accounting for this dependency (and therefore avoid inflated Type I errors), additional levels can be added that explicitly account for the variation among effect sizes within and/or between studies. Besides hierarchical models, other non-purely hierarchical models have been also proposed for meta-analysis, such as Cross-Classified Random Effects models (CCREMs, Fernández-Castilla et al., 2018). Although multilevel models are very flexible, we suspect that applied researchers do not take advantage of all possibilities that these models offer. In fact, most published meta-analyses are restricted to three-level models despite some meta-analytic data require other model specifications, such as four- or five- level models or CCREMs. Therefore, the goal of this study is to describe how multilevel models are typically applied in meta-analysis and to illustrate how, in some meta-analyses, more sophisticated models could have been applied that accounts better for the (non) hierarchical data structure. Method: Meta-analyses that applied multilevel models with more than one random component were searched in June, 2018. We looked at the meta-analyses that cited the studies of Cheung (2014), Hox and De Leeuw (2003), Konstantopoulos (2011), Raudenbush and Bryk (1985), and Van den Noortgate, López-López, Marín-Martínez, & Sánchez-Meca (2013, 2014). We also searched in six electronic databases, using the strings "three-level meta-analysis" OR "multilevel meta-analysis" OR "multilevel meta-analytic review". No date restriction was imposed. Meta-analysis were included if: a) effect sizes were combined using a multilevel model with more than one random component; b) The meta-analysis was included in a journal article, conference presentation or a dissertation; c) The meta-analysis was written in English, Spanish or Dutch. Results: The initial search resulted in 1,286 studies. After applying the inclusion criteria, we finally retrieved 178 meta-analyses. From these, 162 meta-analysis fitted a three-level model, 9 fitted a four-level model, 5 applied CCREMs, and 2 reported a five-level model. We could distinguish five situations in which other models different from the three-level model would have been more appropriate given the (non) hierarchical data structure: 1. A fourth level could have been added to model dependency within studies. For instance, Fischer and Boer (2011) specified a three-level model, were effect sizes (Level 1) were nested within studies (Level 2), nested within countries (Level 3). There were several effect sizes within studies, but this within-study variance was ignored. Therefore, it would have been appropriate to add an additional level to model between-outcomes (within-study) variance. 2. A fourth level could have been specified to deal with more sources of within-study dependencies. For instance, in O'Mara (2006), there were several interventions within studies, and that is why a three-level model was specified: Sampling variance (Level 1), between-interventions variance (Level 2), and between-studies variance (Level 3). However, there were 200 interventions and 460 effect sizes in total, meaning that each intervention led to multiple effect sizes, and that the dependency between these outcomes (within interventions) was not taken into account. A more appropriate model would have been a four-level model: Sampling variance (Level 1), between-outcomes variance (Level 2), between-comparisons variance (Level 3) and between-studies variance (Level 4). 3. A fourth level could have been added to take into account dependency across studies. In the study of Klomp and Valckx (2014), a three-level model was fitted because there were multiple outcomes within studies. In this case, some studies made use of the same big dataset, so a fourth level could have been added to model between-datasets variance. 4. A five-level model could have been applied to model additional within-study and between-study dependencies. In Rabl, Jayasinghe, Gerhart, and Kühlmann (2014), a three-level model was fitted, where effect sizes were nested within studies, nested within countries. There were several effect sizes within studies, so an additional level could have been added to model within-study variance. Furthermore, some studies used the same dataset, so another level could have been specified to estimate the between-datasets variance. The inclusion of these two additional levels would have led to a five-level model. 5. CCREM's could have been applied instead of three-level models. In the study of Fisher, Hanke and Sibley (2012), effect sizes were nested within studies, nested within countries. However, studies were not completely nested within countries, but rather studies and countries were two cross-classified factors: in one study, effect sizes could come from different countries, and effect sizes from the same country could belong to different studies. Therefore, a CCREM model would have accounted better for this cross-classified data structure. Discussion: This systematic review shows how researchers using multilevel model typically apply three-level models to account for dependent effect sizes, although alternative model specifications, such as four- or five- level models or CCREMs, might be more correct given the nature of the data. We have given some examples of how alternative models could have been used for meta-analysis, and we encourage researchers to carefully consider the underlying data structure before selecting a specific multilevel model. Omitting levels in a multilevel analysis might increase the possibility of committing a Type I error. Therefore, the proper specification of the model is the only way to guarantee appropriate estimates of the combined effect size, standard errors, and variance components. References: Cheung, M. W. L. (2014). Modeling dependent effect sizes with three-level meta-analyses: A structural equation modeling approach. Psychological Methods, 19, 211-229. Fernández-Castilla, B., Maes, M., Declercq, L., Jamshidi, L., Beretvas, S. N., Onghena, P., & Van den Noortgate, W. (2018). A demonstration and evaluation of the use of cross-classified random-effects models for meta-analysis. Behavior Research Methods, 1-19. Fischer, R., & Boer, D. (2011). What is more important for national well-being: money or autonomy? A meta-analysis of well-being, burnout, and anxiety across 63 societies. Journal of Personality and Social Psychology, 101, 164-184. Fischer, R., Hanke, K., & Sibley, C. G. (2012). Cultural and institutional determinants of social dominance orientation: A cross‐cultural meta‐analysis of 27 societies. Political Psychology, 33, 437-467. Hox, J. J., & de Leeuw, E. D. (2003). Multilevel models for meta-analysis. In S. P. Reise & N. Duan (Eds.), Multilevel modeling: Methodological advances, issues, and applications (pp. 90–111). Mahwah, NJ: Erlbaum. Klomp, J., & Valckx, K. (2014). Natural disasters and economic growth: A meta-analysis. Global Environmental Change, 26, 183-195. Konstantopoulos, S. (2011). Fixed effects and variance components estimation in three-level meta-analysis. Research Synthesis Methods, 2, 61-76. O'Mara, A. J., Marsh, H. W., & Craven, R. G. (July, 2006). A Comprehensive Multilevel Model Meta-Analysis of Self-Concept Interventions. In Fourth International Biennial SELF Research Conference, Ann Arbor. Rabl, T., Jayasinghe, M., Gerhart, B., & Kühlmann, T. M. (2014). A meta-analysis of country differences in the high-performance work system–business performance relationship: The roles of national culture and managerial discretion. Journal of Applied Psychology, 99, 1011-1041. Raudenbush, S. W., & Bryk, A. S. (1985). Empirical Bayes meta-analysis. Journal of Educational Statistics, 10, 75-98. Van den Noortgate, W., López-López, J. A., Marín-Martínez, F., & Sánchez-Meca, J. (2013). Three-level meta-analysis of dependent effect sizes. Behavior Research Methods, 45, 576-594. Van den Noortgate, W., López-López, J. A., Marín-Martínez, F., & Sánchez-Meca, J. (2014). Meta-analysis of multiple outcomes: A multilevel approach. Behavior Research Methods, 47, 1274-1294.

Purpose: The increasing need for school-based mental health services has altered teachers' involvement in mental health services. Methods: This study presents a meta-analysis from a previous systematic review to identify which study characteristics result in effective treatment outcomes. Specific treatment characteristics analyzed in this study include type of intervention, treatment modality, length of treatment, and type of measurement. Effect sizes were coded by internalizing and externalizing disorders, depending on the symptoms the corresponding treatments were intended to address. A final sample size included 9 independent effect sizes of internalizing behaviors and 21 effect sizes of externalizing behaviors. Results: Internalizing disorders, social skill interventions, classroom modalities, and medium treatment length were moderating treatment characteristics. No significant effects were found for externalizing disorders. Conclusions: These results further add to the research on teacher's role in school-based mental health services and provide important information for social workers who work in schools.

Department of Energy (United States of America) ; National Science Foundation (United States of America) ; Australian Research Council (Australia) ; National Council for the Development of Science and Technology ; Fundação de Amparo à Pesquisa do Estado do Rio de Janeiro (FAPERJ) ; Natural Sciences and Engineering Research Council (Canada) ; Chinese Academy of Sciences ; National Natural Science Foundation of China (China) ; Administrative Department of Science, Technology and Innovation (Colombia) ; Ministry of Education, Youth and Sports (Czech Republic) ; Academy of Finland (Finland) ; Alternative Energies and Atomic Energy Commission ; National Center for Scientific Research/National Institute of Nuclear and Particle Physics (France) ; Bundesministerium fur Bildung und Forschung (Federal Ministry of Education and Research) ; Deutsche Forschungsgemeinschaft (German Research Foundation) (Germany) ; Department of Atomic Energy (India) ; Department of Science and Technology (India) ; Science Foundation Ireland (Ireland) ; Istituto Nazionale di Fisica Nucleare (National Institute for Nuclear Physics) (Italy) ; Ministry of Education, Culture, Sports, Science and Technology (Japan) ; Korean World Class University Program ; National Research Foundation (Korea) ; National Council of Science and Technology (Mexico) ; Foundation for Fundamental Research on Matter (The Netherlands) ; National Science Council (Republic of China) ; Ministry of Education and Science of the Russian Federation ; National Research Center Kurchatov Institute of the Russian Federation ; Russian Foundation for Basic Research (Russia) ; Slovak R&D Agency (Slovakia) ; Ministry of Science and Innovation ; Consolider-Ingenio Program (Spain) ; Swedish Research Council (Sweden) ; Swiss National Science Foundation (Switzerland) ; Ministry of Education and Science of Ukraine (Ukraine) ; Science and Technology Facilities Council ; Royal Society (United Kingdom) ; A. P. Sloan Foundation (United States of America) ; European Union community Marie Curie Fellowship ; European Union community Marie Curie Fellowship: 302103 ; Drell-Yan lepton pairs produced in the process p (p) over bar -> l(+)l(-) + X through an intermediate gamma*/Z boson have an asymmetry in their angular distribution related to the spontaneous symmetry breaking of the electroweak force and the associated mixing of its neutral gauge bosons. The CDF and D0 experiments have measured the effective-leptonic electroweak mixing parameter sin(2) theta(lept)(eff) using electron and muon pairs selected from the full Tevatron proton-antiproton data sets collected in 2001-2011, corresponding to 9-10 fb(-1) of integrated luminosity. The combination of these measurements yields the most precise result from hadron colliders, sin(2)theta(lept)(eff) = 0.23148 +/- 0.00033. This result is consistent with, and approaches in precision, the best measurements from electron-positron colliders. The standard model inference of the on-shell electroweak mixing parameter sin(2) theta(W), or equivalently the W-boson mass M-W, using the ZFITTER software package yields sin(2) theta(W) = 0.22324 +/- 0.00033 or equivalently, M-W = 80.367 +/- 0.017 GeV/c(2).

Department of Energy ; National Science Foundation (U.S.A.) ; Australian Research Council (Australia) ; National Council for the Development of Science and Technology ; Fundação de Amparo à Pesquisa do Estado do Rio de Janeiro (FAPERJ) ; European Union community Marie Curie Fellowship Contract ; European Union community Marie Curie Fellowship Contract: 302103 ; : DE-AC02-07CH11359 ; The CDF and D0 experiments at the Fermilab Tevatron have measured the asymmetry between yields of forward- and backward-produced top and antitop quarks based on their rapidity difference and the asymmetry between their decay leptons. These measurements use the full data sets collected in proton-antiproton collisions at a center-of-mass energy of root s = 1.96 TeV. We report the results of combinations of the inclusive asymmetries and their differential dependencies on relevant kinematic quantities. The combined inclusive asymmetry is A(FB)(t (t) over bar) = 0.128 +/- 0.025. The combined inclusive and differential asymmetries are consistent with recent standard model predictions.

BMWFW (Austria) ; FWF (Austria) ; FNRS (Belgium) ; FWO (Belgium) ; Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) ; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) ; Fundação de Amparo à Pesquisa do Estado do Rio de Janeiro (FAPERJ) ; Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) ; MES (Bulgaria) ; CERN ; CAS (China) ; MoST (China) ; NSFC (China) ; COLCIENCIAS (Colombia) ; MSES (Croatia) ; CSF (Croatia) ; RPF (Cyprus) ; SENESCYT (Ecuador) ; MoER (Estonia) ; ERC IUT (Estonia) ; ERDF (Estonia) ; Academy of Finland (Finland) ; MEC (Finland) ; HIP (Finland) ; CEA (France) ; CNRS/IN2P3 (France) ; BMBF (Germany) ; DFG (Germany) ; HGF (Germany) ; GSRT (Greece) ; OTKA (Hungary) ; NIH (Hungary) ; DAE (India) ; DST (India) ; IPM (Iran) ; SFI (Ireland) ; INFN (Italy) ; MSIP (Republic of Korea) ; NRF (Republic of Korea) ; LAS (Lithuania) ; MOE (Malaysia) ; UM (Malaysia) ; BUAP (Mexico) ; CINVESTAV (Hungary) ; CONACYT (Hungary) ; LNS (Hungary) ; SEP (Hungary) ; UASLP-FAI (Mexico) ; MBIE (New Zealand) ; PAEC (Pakistan) ; MSHE (Poland) ; NSC (Poland) ; FCT (Portugal) ; JINR (Dubna) ; MON (Russia) ; RosAtom (Russia) ; RAS (Russia) ; RFBR (Russia) ; MESTD (Serbia) ; SEIDI (Spain) ; CPAN (Spain) ; Swiss Funding Agencies (Switzerland) ; MST (Taipei) ; ThEPCenter (Thailand) ; IPST (Thailand) ; STAR (Thailand) ; NSTDA (Thailand) ; TUBITAK (Turkey) ; TAEK (Turkey) ; NASU (Ukraine) ; SFFR (Ukraine) ; STFC (United Kingdom) ; DOE (U.S.A.) ; NSF (U.S.A.) ; Marie-Curie program ; European Research Council ; EPLANET (European Union) ; Leventis Foundation ; A.P. Sloan Foundation ; Alexander von Humboldt Foundation ; Belgian Federal Science Policy Office ; Fonds pour la Formation a la Recherche dans l'Industrie et dans l'Agriculture (FRIA-Belgium) ; Agentschap voor Innovatie door Wetenschap en Technologie (IWT-Belgium) ; Ministry of Education, Youth and Sports (MEYS) of the Czech Republic ; Council of Science and Industrial Research, India ; HOMING PLUS program of the Foundation for Polish Science ; European Union ; Regional Development Fund ; Mobility Plus program of the Ministry of Science and Higher Education ; National Science Center (Poland) ; Thalis program - EU-ESF ; Aristeia program - EU-ESF ; National Priorities Research Program by Qatar National Research Fund ; Programa Clarin-COFUND del Principado de Asturias ; Rachadapisek Sompot Fund for Postdoctoral Fellowship (Thailand) ; Chulalongkorn University (Thailand) ; Chulalongkorn Academic into Its 2nd Century Project Advancement Project (Thailand) ; Welch Foundation ; Thalis program - Greek NSRF ; Aristeia program - Greek NSRF ; Science and Technology Facilities Council ; National Science Center (Poland): 2014/14/M/ST2/00428 ; National Science Center (Poland): Opus 2013/11/B/ST2/04202 ; National Science Center (Poland): 2014/13/B/ST2/02543 ; National Science Center (Poland): 2014/15/B/ST2/03998 ; National Science Center (Poland): Sonatabis 2012/07/E/ST2/01406 ; Welch Foundation: C-1845 ; Science and Technology Facilities Council: ST/K001256/1 ; Science and Technology Facilities Council: ST/N000250/1 ; Science and Technology Facilities Council: GRIDPP ; Science and Technology Facilities Council: CMS ; A search for heavy resonances that decay to tau lepton pairs is performed using proton-proton collisions at root s = 13 TeV. The data were collected with the CMS detector at the CERN LHC and correspond to an integrated luminosity of 2.2 fb(-1). The observations are in agreement with standard model predictions. An upper limit at 95% confidence level on the product of the production cross section and branching fraction into tau lepton pairs is calculated as a function of the resonance mass. For the sequential standard model, the presence of Z' bosons decaying into tau lepton pairs is excluded for Z/ masses below 2.1 TeV, extending previous limits for this final state. For the topcolor-assisted technicolor model, which predicts Z' bosons that preferentially couple to third-generation fermions, Z' masses below 1.7 TeV are excluded, representing the most stringent limit to date.

BMWFW (Austria) ; FWF (Austria) ; FNRS (Belgium) ; FWO (Belgium) ; Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) ; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) ; Fundação de Amparo à Pesquisa do Estado do Rio de Janeiro (FAPERJ) ; Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) ; MES (Bulgaria) ; CERN ; CAS (China) ; MoST (China) ; NSFC (China) ; COLCIENCIAS (Colombia) ; MSES (Croatia) ; CSF (Croatia) ; RPF (Cyprus) ; SENESCYT (Ecuador) ; MoER (Estonia) ; ERC IUT (Estonia) ; ERDF (Estonia) ; Academy of Finland (Finland) ; MEC (Finland) ; HIP (Finland) ; CEA (France) ; CNRS/IN2P3 (France) ; BMBF (Germany) ; DFG (Germany) ; HGF (Germany) ; GSRT (Greece) ; OTKA (Hungary) ; NIH (Hungary) ; DAE (India) ; DST (India) ; IPM (Iran) ; SFI (Ireland) ; INFN (Italy) ; MSIP (Republic of Korea) ; NRF (Republic of Korea) ; LAS (Lithuania) ; MOE (Malaysia) ; UM (Malaysia) ; BUAP (Mexico) ; CINVESTAV (Mexico) ; CONACYT (Mexico) ; LNS (Mexico) ; SEP (Mexico) ; UASLP-FAI (Mexico) ; MBIE (New Zealand) ; PAEC (Pakistan) ; MSHE (Poland) ; NSC (Poland) ; FCT (Portugal) ; JINR (Dubna) ; MON (Russia) ; RosAtom (Russia) ; RAS (Russia) ; RFBR (Russia) ; MESTD (Serbia) ; SEIDI (Spain) ; CPAN (Spain) ; Swiss Funding Agencies (Switzerland) ; MST (Taipei) ; ThEPCenter (Thailand) ; IPST (Thailand) ; STAR (Thailand) ; NSTDA (Thailand) ; TUBITAK (Turkey) ; TAEK (Turkey) ; NASU (Ukraine) ; SFFR (Ukraine) ; STFC (United Kingdom) ; DOE (U.S.A.) ; NSF (U.S.A.) ; Marie-Curie programme and the European Research Council and EPLANET (European Union) ; Leventis Foundation ; A. P. Sloan Foundation ; Alexander von Humboldt Foundation ; Belgian Federal Science Policy Office ; Fonds pour la Formation a a la Recherche dans l'Industrie et dans l'Agriculture (FRIA-Belgium) ; Agentschap voor Innovatie door Wetenschap en Technologie (IWT-Belgium) ; Ministry of Education, Youth and Sports (MEYS) of the Czech Republic ; Council of Science and Industrial Research, India ; HOMING PLUS programme of the Foundation for Polish Science ; European Union ; Regional Development Fund ; Mobility Plus programme of the Ministry of Science and Higher Education ; National Science Center (Poland) ; Thalis and Aristeia programmes - EU-ESF ; Greek NSRF ; National Priorities Research Program by Qatar National Research Fund ; Programa Clarin-COFUND del Principado de Asturias ; Rachadapisek Sompot Fund for Postdoctoral Fellowship, Chulalongkorn University ; Chulalongkorn Academic into Its 2nd Century Project Advancement Project (Thailand) ; Welch Foundation ; Science and Technology Facilities Council ; National Science Center (Poland): 2014/14/M/ST2/00428 ; National Science Center (Poland): 2013/11/B/ST2/04202 ; National Science Center (Poland): 2014/13/B/ST2/02543 ; National Science Center (Poland): 2014/15/B/ST2/03998 ; National Science Center (Poland): 2012/07/E/ST2/01406 ; Welch Foundation: C-1845 ; Science and Technology Facilities Council: ST/N001273/1 ; Science and Technology Facilities Council: ST/K003542/1 GRID PP ; Science and Technology Facilities Council: ST/K001639/1 CMS Upgrades ; Science and Technology Facilities Council: ST/N000250/1 ; Science and Technology Facilities Council: ST/J005479/1 ; Science and Technology Facilities Council: PP/E002803/1 ; Science and Technology Facilities Council: PP/E000479/1 ; Science and Technology Facilities Council: ST/K003224/1 CMS Upgrades ; Science and Technology Facilities Council: ST/H000925/2 ; Science and Technology Facilities Council: ST/K003542/1 ; Science and Technology Facilities Council: ST/K003542/1 GRIDPP ; Science and Technology Facilities Council: ST/J004871/1 ; Science and Technology Facilities Council: ST/K001256/1 ; Science and Technology Facilities Council: ST/L005603/1 ; Science and Technology Facilities Council: ST/K001639/1 ; Science and Technology Facilities Council: ST/H000925/1 ; Science and Technology Facilities Council: ST/M004775/1 ; Science and Technology Facilities Council: ST/M004775/1 GRIDPP ; Science and Technology Facilities Council: ST/M002020/1 ; Science and Technology Facilities Council: ST/K001531/1 ; Science and Technology Facilities Council: ST/N000242/1 ; Science and Technology Facilities Council: GRIDPP ; Science and Technology Facilities Council: CMS ; Science and Technology Facilities Council: ST/I003622/1 GRIDPP ; Science and Technology Facilities Council: ST/I003622/1 ; Science and Technology Facilities Council: ST/I000305/1 ; Interactions between jets and the quark-gluon plasma produced in heavy ion collisions are studied via the angular distributions of summed charged-particle transverse momenta (p(T)) with respect to both the leading and subleading jet axes in high-p(T) dijet events. The contributions of charged particles in different momentum ranges to the overall event p(T) balance are decomposed into short-range jet peaks and a long-range azimuthal asymmetry in charged-particle p(T). The results for PbPb collisions are compared to those in pp collisions using data collected in 2011 and 2013, at collision energy root s(NN) = 2.76 TeV with integrated luminosities of 166 mu b(-1) and 5.3 pb(-1), respectively, by the CMS experiment at the LHC. Measurements are presented as functions of PbPb collision centrality, charged-particle p(T), relative azimuth, and radial distance from the jet axis for balanced and unbalanced dijets.

FMSR (Austria) ; FNRS (Belgium) ; FWO (Belgium) ; Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) ; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) ; Fundação de Amparo à Pesquisa do Estado do Rio de Janeiro (FAPERJ) ; Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) ; MES (Bulgaria) ; CERN (China) ; CAS (China) ; MoST (China) ; NSFC (China) ; COLCIENCIAS (Colombia) ; MSES (Croatia) ; RPF (Cyprus) ; Academy of Sciences and NICPB (Estonia) ; Academy of Finland, ME, and HIP (Finland) ; CEA (France) ; CNRS/IN2P3 (France) ; BMBF (Germany) ; DFG (Germany) ; HGF (Germany) ; GSRT (Greece) ; OTKA (Hungary) ; NKTH (Hungary) ; DAE (India) ; DST (India) ; IPM (Iran) ; SFI (Ireland) ; INFN (Italy) ; NRF (Korea) ; LAS (Lithuania) ; CINVESTAV (Mexico) ; CONACYT (Mexico) ; SEP (Mexico) ; UASLP-FAI (Mexico) ; PAEC (Pakistan) ; SCSR (Poland) ; FCT (Portugal) ; JINR (Armenia, Belarus, Georgia, Ukraine, Uzbekistan) ; MST (Russia) ; MAE (Russia) ; MSTDS (Serbia) ; MICINN ; CPAN (Spain) ; Swiss Funding Agencies (Switzerland) ; NSC (Taipei) ; TUBITAK ; TAEK (Turkey) ; STFC (United Kingdom) ; DOE (USA) ; NSF (USA) ; European Union ; Leventis Foundation ; A. P. Sloan Foundation ; Alexander von Humboldt Foundation ; Measurements of inclusive charged-hadron transverse-momentum and pseudorapidity distributions are presented for proton-proton collisions at root s = 0.9 and 2.36 TeV. The data were collected with the CMS detector during the LHC commissioning in December 2009. For non-single-diffractive interactions, the average charged-hadron transverse momentum is measured to be 0.46 +/- 0.01 (stat.) +/- 0.01 (syst.) GeV/c at 0.9 TeV and 0.50 +/- 0.01 (stat.) +/- 0.01 (syst.) GeV/c at 2.36 TeV, for pseudorapidities between -2.4 and +2.4. At these energies, the measured pseudorapidity densities in the central region, dN(ch)/d eta vertical bar(vertical bar eta vertical bar and pp collisions. The results at 2.36 TeV represent the highest-energy measurements at a particle collider to date.