quantitative research methods by cheryl vierheilig

Quantitative Research Methods Matrix

Quantitative Research Methods (Experimental, Quasi-Experimental, Ex Post Facto, DescriptiveBy: Cheryl Vierheilig

Primary Characteristics Current Peer-Reviewed StudyExperimental

Identifies cause and effect relationships; Researcher attempts to control all influential factors except those whose possible effects are the focus of investigation; Clearly identifiable independent and dependent variable; Internal validity is essential (Leedy, 2013). Researcher manipulates the independent variable and examines its effect on another, dependent variable. People or other units of study are randomly assigned to groups. (Leedy, 2013). Tests the impact of a treatment or an intervention on an outcome, controlling for all other factors that might influence the outcome (Creswell, 2014). Researchers randomly assign individuals to groups to control. When one group receives a treatment and the other does not, the experimenter can isolate whether it is the treatment and not the other factors that influence the outcome (Creswell, 2014).

The problem investigated:

The problem of the current study was to investigate the relationship between a pre-workout warm-up and psychological processes.

How the sample was selected:

The study surveyed seventy-six (n=76) participants from a small, Midwestern college aged 18-25. Study primarily focused on the participant's reported levels of enjoyment and motivation for those who used warm-up prior to exercise versus those who did not. Additionally, the researcher was interested in reported short and long-term adherence rates of warm-up users, versus non-users. (Thirty-three (n=33) male, forty-three (n=43) female). Participants were members of introductory psychology and fitness management classes on the researcher's campus and were between the ages of 18 and 25 (M= 19.21, SD=.93). The researcher's Institutional Review Board approved the study.

How variables were defined and measured:

The researcher obtained permission from the class instructors and scheduled class visits. Following the instructor dismissing themselves from the class, the researcher informed the class of the nature of the study and solicited participation. Additionally, the researcher informed the participants that participation was voluntary and would have no bearing on their grade in the class. The results would also be confidential; the questionnaires recorded no identifiable information. The researcher asked participants to place completed surveys in a manila envelope located at the front of the classroom. Finally, the researcher thanked the participants


for their participation prior to leaving the classroom.

How data was collected and analyzed:

The researcher collected data using a ten item pen-or-pencil questionnaire. The first two questions asked for information pertaining to how often the participant exercised (one day per week) to (more than five days per week) and how long the participant had been exercising at this rate (less than three months) to (over one year). Following these items, the questionnaire asked participants to report whether or not they used a pre-workout routine (Yes) or (No). If answering yes to this question, the next item asked participants to specify the type of pre-workout routine (Warm-up) or (Stretch). The researcher formulated the option "stretch" to mask the fact that researcher was interested in solely warm-up use. The researcher omitted results from participants who reported using only stretch or a combination of warm-up and stretch. A debate exists in the literature over the use of five or seven point ordinal Likert-type scales in the measurement of exercise-related motivation and other affective ratings. As this debate is currently not settled, the researcher opted to use five point Likert-type scales to measure exercise-related motivation and enjoyment. The questionnaire measured participant's average level of motivation for exercise with options ranging from 1 (Not motivated at all) to 5 (Very motivated); enjoyableness of an average workout with a scale from 1 (Very Unenjoyable) to 5 (Very Enjoyable); how often participants completely finish planned workouts with a (0%) to (100%) scale in 10% increments. Participants rated their long-term adherence rates to their exercise programs from 1 (Less than a week) to 5 (Six months or more). Additionally, participants reported two final items regarding age and gender. The researcher did not ask participants to report ethnic differences in the study.

The key findings:

The study hypothesized that exercisers who reported using a warm-up prior to exercise would report significantly higher levels


of exercise-related motivation, enjoyment, and greater short and long-term adherence rates to exercise programs.

The results demonstrated significantly higher ratings for motivation and enjoyment for those participants who reported using a warm prior to exercise. Results for short and long-term exercise adherence were higher for those using a pre-workout warm-up, though not statistically significant.

Of the 76 participants, 51 responded "yes" to using a warm-up and 25 responded "no" to this item. Independent samples t-tests were used to compare the mean scores of those who reported engaging in a warm-up relative to those who did not. There was a significantly greater difference for participants who indicated use of a warm-up (M=4.3, SD=.74) and those who did not utilize a warm-up (M=3.60, SD=.957); t(74) =2.315, p < .05, in rating their average level of enjoyment for exercise. Additionally, there was a significantly greater difference for those participants who indicated use of a warm-up (M=4.6, SD=1.24) and those who did not utilize a warm-up (M=3.92, SD=1.48); t(74) =3.593, p < .05, in rating their level of motivation for completing their workout.

Results were not significant for those who indicated warm-up use (M=8.52, SD=1.40) and those who did not (M = 7.67, SD=2.51); t(30.07) =1.551, p>.05, for what percentage of the time participants completed their workout. The results were not significant for those who indicated use of a warm-up (M=3.73, SD=1.23) and those who did not (M=3.16, SD= 1.31); t(74) = 1.838, p>.05, for how long participants adhere to workout routines. The researcher excluded twelve (n=12) participants for reporting only stretch use and additionally, five (n=5) participants who selected both warm-up and stretch options.

The generalizability of the findings:


A larger scale replication is necessary to more powerfully generalize these findings to the population, and to study the possible effects of other types of pre-workout routines, such as stretching. If follow-up research demonstrates a significant effect for motivation, enjoyment, and adherence reports, this may warrant experimental research examining the underlying mechanisms affecting these results. Follow-up studies should utilize a larger, more diverse sample of young adults to more readily generalize these findings. Because a large percentage of respondents were fitness management students, this group may already have been more active than the other respondents, which could have affected overall scores. Additionally, one of the limitations of this study was that the research did not target whether one type of pre-workout routine-warm-up, stretch, or a combination of the two-produced significantly higher scores relating to motivation, enjoyment, and adherence. Future studies should examine these possible differences. Another limitation of the current study was that the design only yielded correlations between the variables and not a cause-and-effect relationship. If larger scale follow-up research demonstrates a significant effect for reported motivation, enjoyment, and adherence scores, this may warrant experimental research examining the underlying mechanisms affecting these results. After analyzing the data, there is clear support for one of the major hypotheses. Participants responding "yes" to warm-up use reported significantly higher overall mean scores for both exercise-related motivation and enjoyment than those who did not report using a warm-up. However, participants reporting warm-up use did not have significantly greater scores for whether or not participants finished their workouts more often or for how long participants adhered to a workout program.

References:

Ladwig, M. A. (2013). The psychological effects of a pre-workout warm-up: An exploratory study. Journal of Multidisciplinary Research, 5(3), 79-88. Retrieved from


http://search.proquest.com/docview/1492260371?accountid=35812

Quasi-experimentalResearcher does not control confounding variables so cannot rule out alternative explanations for the results obtained (Leedy 2013). Researchers must take whatever variables and explanations they have not controlled into consideration when they interpret their data (Leedy, 2013). These designs provide an alternate means for examining causality in situations which are not conducive to experimental control. The designs should control as many threats to validity as possible in situations where at least one of the three elements of true experimental research is lacking (manipulation, randomization, control group) (Simon, 2013).

Quasi-Experimental Design


The problem investigated consists of students’ anxiety over the courses that are being taught in quantitative research methods which play a central role in many undergraduate programs in sociology. Many students perceive the subject as inherently uninteresting and difficult. This study describes an experiment designed to introduce aspects of quantitative reasoning into a large substantively focused class in the social sciences. The experiment assessed whether students can learn quantitative reasoning skills in the context of a large "nonmethods" class in sociology. The experiment measured students' mastery of these skills by comparing their competence at quantitative reasoning at the beginning and end of the class term. The results revealed that students' abilities to interpret and manipulate empirical data increased significantly. Further, the increase occurred independent of students ' basic reasoning skills as measured by baseline SAT verbal and math scores. The implications of these findings for teaching quantitative methods in sociology undergraduate curricula are discussed. The central purpose of the experiment was to assess whether it is possible for students, at the earliest stages of their college careers, to learn basic quantitative and analytic skills in the context of a "nonmethods" class in sociology.


Quantitative reasoning materials were developed in four steps. First, baseline levels of competence for "average" undergraduate students in entry-level sociology courses were established. Students for basic math and analytic skills were tested. Second,


quantitative reasoning materials, in the form of exercises and assignments in interpreting and analyzing empirical information, based upon knowledge of these baseline competence levels and upon the teaching skills of TAs who were scheduled to introduce them were selected. Third, pretested and revised assignments in an ongoing course prior to their introduction in the large enrollment sociology class were completed.

Establishing Baseline Competence Levels

The first step in developing the experimental materials was measuring average baseline levels of competence in quantitative reasoning skills of entry-level undergraduates. This required development and administration of a quantitative skills test to a "typical" entry level sociology class. This test was adopted from materials developed for mathematics classes, asking students to answer questions about two-by-two and more complex tables showing bivariate and multivariate statistical relationships.

Development of Materials

Once baseline competence levels were established, learning modules were developed with three objectives in mind. First, they should serve to assist in introducing the substantive material for the class. Thus, each module was framed in terms of a single question to be addressed in the class and included introductory material describing the question and relevant theoretical material about the question. Second, each exercise was developed with a specific quantitative problem or set of problems related directly to the central substantive question. Third, each module included classroom illustrations and a specific homework assignment for the students. The homework required the students to manipulate data (e.g., computing percentages) and to interpret the data in a written assignment of two to three pages in length.



As noted earlier, the structure of the class (Sociology of Deviance) included lectures and quiz sections. Lectures met three times a week and were delivered by the instructor. Quiz sections, where students met twice a week in groups of 25, were taught by graduate student TAs. The content of the course was divided into five segments corresponding to major sociological perspectives on deviant behavior and social control. Each part of the class was comprised of a series of lectures delivered by the instructor, a series of lessons delivered by the graduate student TAs in discussion sections, and a series of readings from scholarly articles and books. The course emphasized, as learning objectives, the development of critical reasoning skills, the mastery of knowledge about social phenomena and their explanation from the perspective of sociological theories, and the effective application of knowledge to solving problems of public policy.

Material on quantitative reasoning was introduced in a series of stages in lectures and quiz sections. During the first week of the term, the lecturer delivered presentations on quantitative reasoning skills (e.g., table reading, computation of percentages, interpretation of findings) and the logic of causal analysis in the context of a sequence of examples of juvenile delinquency and its correlates. Following this presentation, three subsequent presentations or modules were delivered by TAs in quiz sections over the course of the academic term. These modules were deliberately spaced two to three weeks apart and were of increasing difficulty. Each presentation was divided into three components: short lectures by the TAs, classroom discussions of the ideas and the materials, and written homework assignments. Class discussions followed these lectures. The discussions centered around in-class assignments in which students individually and collectively worked to answer questions about a tabular presentation of data on the classroom subject. Finally, students were assigned short essays of two to three


pages in length, in which they were asked to interpret additional data and apply their interpretations to the theoretical issues addressed in quiz sections. These essays were graded and included as part of the course grade.

An illustration of one of the modules, as integrated into the class materials, may prove useful here. One of the major objectives in the class was for students to learn how to apply sociological theories of deviance and social control to explain contemporary social problems. One segment of the class examined macrolevel theories of conflict and social control. In addition to several lectures, students examined writing and research on Marxist or conflict theories of deviance to explain race and gender differences in rates of imprisonment across regions of the country. As part of the preparation for the assignment, students read the published research of one of the present study's authors and contrasted it with other work on the same general subject. Then, in their quiz sections, students participated in an in-class activity, applying the ideas they learned from lectures and class readings to tabular data on patterns of imprisonment presented by the TAs. Students discussed the analysis and interpreted the data in terms of the substantive questions raised by the theories. At the end of the discussion section, the students received a writing assignment in which they analyzed and interpreted another set of tabular data, similar to the data analyzed in class. The assignment was due at the beginning of the next quiz section, usually two days later. After turning the assignment in, students participated in a second activity and discussion involving the completed writing assignment.

How data were collected and analyzed:

Data on student learning of the material and skills on quantitative reasoning were collected using a one-group pretest/posttest experimental design. Only students who completed both the pre- and posttests were included in the analyses. The tests were administered as follows. On the first day of the academic


term, students' quantitative reasoning skills were measured with the pretest. At the very end of the term, students’ quantitative reasoning skills were measured with a posttest. The posttest was identical to the pretest. Four students were given approximately 15 minutes to complete each test.

The test consisted of 10 questions designed to measure students' quantitative reasoning and table-reading skills. The questions were developed in a manner that would reveal changes in students' performance that would be attributable to changes in quantitative reasoning ability rather than to substantive knowledge contained in the course or in sociology in general. Questions were divided into three sections addressing three progressively difficult concepts. The first section measured students' ability to identify the relationship between two variables. The second section introduced the concept of theory. This section included a short vignette describing a theory and four tables reporting findings. Students were asked to determine which of the four tables offered evidence supporting the theory and which of the four tables offered evidence disproving the theory. The final section addressed the issue of linearity in statistical relationships. In this section, students were asked to determine the relative fit of several relationships-that is, whether the relationships were linear or nonlinear.

Students were given a graded test score ranging from 0 to 10, depending upon the number of questions they answered correctly. Partial credit was not given. Neither the pre- nor posttests counted as a grade or as extra credit for the students. However, material from the learning modules introduced in the lectures and the discussion sections did factor into students' grades, as noted above, because the students were graded on the quantitative writing assignments and on quantitative exam questions administered throughout the quarter.

Finally, TAs proctored the pretest and posttest measurements, collecting observational data on the students as they completed the


tests. Although not systematic, two types of observational information were collected. First, TAs monitored the time required to complete the tests. Second, they observed whether students were actively engaged in completing the tests-that is, whether the classroom was quiet and students concentrated heavily on the tests.

The key findings:

The difference of means for the latter group is significant, revealing a substantial increase in correct scores over the experimental period. For reasons exhibited and discussed below, we are inclined to attribute this increase to learning and the acquisition of quantitative reasoning skills. Quite clearly, students performed better, on average, on the posttest. Whereas the mean number of correct answers for the pretest was 5.71, the posttest mean was 6.73. This represents a 20 percent increase in correct responses between test administrations.

It is possible that students' basic reasoning and analytical skills may contribute to these differences. Students with strong basic reasoning skills-as reflected in mathematical or even verbal reasoning performance-may be more likely to grasp the concepts in lectures and in quiz sections such that posttest scores would be higher than for those students with weak basic skills. The concern here is that some students may enter the class with either much stronger reasoning skills than others or an accumulated academic advantage such that their learning will be significantly shaped by their skills or their past academic success. According to this reasoning, the pretest/posttest differences may be influenced as much by an individual's quantitative reasoning ability and/or test-taking skills prior to the experiment as they are to the experimental introduction of quantitative reasoning materials in the class. In order to examine whether the experimental results were related to quantitative reasoning skills prior to the course and the experiment, we collected additional data on students' SAT verbal and math scores.

Of the 455 students who completed the pretest, 414 had also


submitted SAT scores at the time they were admitted to the university. Of the 261 students who completed both the pretest and the posttest, 260 had submitted SAT scores at admission. the correlations between the verbal portion of the SAT and the pretest and posttest scores were equivalent in strength to the correlations between the math portion and the pretest and posttest scores. Thus, our quantitative reasoning results measure analytical abilities that are different from math or verbal skills that are reflected in SAT performance.

Equally important is that the correlations between the SAT scores and the pretest/ posttest difference scores are near zero (r = .04, .02, .03). Students' improvements in quantitative reasoning skills, as reflected in the difference scores, are not associated with their analytical skills as measured by the SAT upon entering the university. Thus, students with low verbal or math scores were just as likely to achieve improved quantitative reasoning skills as students who entered with high math or verbal scores. We also performed a repeated measures analysis of covariance with SAT total score as the covariate and pre- and posttest as the repeated measure. This test examined the hypothesis of no difference between pre- and posttest scores on the quantitative reasoning test, once differences in ability as reflected in SAT scores were removed. However, the differences between the pre- and posttest scores remained sizable and statistically significant in this analysis (F = 63.59; df = 1, 259; p< .001), indicating as above that the change from pretest to posttest was substantial and statistically independent from SAT scores.

One possible interpretation of the results is that the difference between the pretest and posttest scores reflects improvements in testing skills rather than improvements in actual reasoning skills-that is, improvements in guessing the answers correctly rather than deriving answers from correct manipulations of data. While it is impossible to test this hypothesis definitively, TAs' observations about the test-taking performance of students in the sessions shed some light on this issue. TAs


generally reported that the testing situations for the pre- and posttests were quite similar. Students completed the tests in about the same amount of time for both sessions-there were no more "late" or "early" finishers in the pretest than the posttest. Further, students exhibited the same level of seriousness and commitment to the task in both sessions. Very few students in either session failed to answer all of the problems. Further, there were no more inquiries from students about the test questions (e.g., "I don't understand what this question means. Could you explain it to me?") during the pretest session than during the posttest session. Thus, there is no observational evidence from students' behavior during testing sessions that suggests the students were more adept at taking the posttest than the pretest, having worked with similar material a few times over the course of the term.

A related issue is whether students' uncertainty or anxiety over the course changed with the introduction of the quantitative reasoning modules. We did not incorporate any anxiety measures into the study design. However, we compared students' evaluations of the experimental course before and after the experiment was conducted in order to examine how students' perceptions may have changed. Three aspects of the evaluations were examined. The first was a measure of students' satisfaction with the assignments and grading practices. The second measured students' perception of the reasonableness of assigned work. The third measure assessed students' beliefs about the clarity of the instructor's expectations of them. No qualitative data or written comments by students regarding the assignments were available for the analyses.

Our analyses of these measures found that students' evaluations of the course were higher on each of the measures for the experimental period than in previous courses. Prior to the inclusion of the quantitative reasoning modules, students consistently rated the clarity, level of organization, and structure of assigned work in the class as relatively low. Following inclusion of the modules, a higher proportion of students felt that the assignments were clear, that expectations were certain, and that the


organizational level of the class was high. One interpretation of this pattern is that the structured nature of the quantitative work, unlike previous writing assignments used in the course, actually increased the clarity of tasks and students' perceptions of what was expected from them. By increasing the organization and predictability in assignments, the inclusion of quantitative reasoning modules may have actually improved students' assessments of the course overall and material included in the course. While this reveals little about whether teaching quantitative methods in this manner reduces student uncertainty and anxiety relative to traditional teaching approaches, it does suggest that adding carefully structured quantitative material to a substantively oriented class does not increase students' uncertainty, confusion, or frustration with the class.


The experimental results suggest that students' ability to interpret and manipulate empirical data increased over the course of a single term in which instructors introduced quantitative reasoning modules as part of the course material. Further, the increase occurred above and beyond the effects of students' basic reasoning skills as measured by baseline SAT verbal and math scores. Thus, the improvements in learning are not necessarily attributable to certain types of students or student experiences prior to participating in the experiment or the class.

Although these analyses suggest that instructors may achieve significant improvements in students' learning of statistics and methods skills in "nonmethods" classes, the results do not inform debate over many important concerns in teaching sociological methods. First, our experimental results do not address whether instruction in the manipulation and interpretation of data in substantive classes is more effective pedagogically than instruction in "stand-alone" statistics classes. The analysis performed in the present study involved no comparisons between learning in a substantive sociology class with learning in a class


devoted entirely to social statistics or research methods. Indeed, it is quite possible that greater improvements in quantitative reasoning, as measured in the present study, might be achieved in a statistics or methods class. An important consideration in comparing the two types of instruction would be to separate differences between substantive and methods courses in teaching and delivering material to students. Any such comparison would need to separate "instructor effects" on learning from the effects of actual exposure to material about quantitative methods and analyses. An obvious approach to the comparison would be to conduct the experiment by having the same instructor teach the same material in two different types of classes one a substantive class and the other a statistics or methods class-and then compare student learning between the two types of classes, adjusting for other important factors like class composition and baseline reasoning skills.

References from: Bridges, G. S., Gillmore, G. M., Pershing, J. L., & Bates, K. A. (2012).


Ex-post Facto Researcher can investigate the extent to which specific independent variables may possibly effect the dependent variable (Leedy, 2013). Researcher identifies events that have already occurred or conditions that are already present and then collects data to investigate a possible relationship between these factors and subsequent characteristics and behaviors (Leedy, 2013). After observing that differing circumstances have prevailed for two or more different groups—such circumstances comprise the independent variable—the researcher tries to determine whether the groups differ on some other dependent variable (Leedy, 2013). There is no direct manipulation of the independent variable. The presumed “cause” has already occurred. Since manipulation is not possible, the researcher can’t draw firm conclusions about cause and effect (Leedy, 2013). Experimenter cannot control for confounding variables that may provide alternative explanations for any group differences that are observed (Leedy, 2013). Lacks control element so can’t draw definite conclusions about cause and effect (Leedy, 2013).


This study aimed to determine differences between permissive and authoritarian parenting in play activities motion against the fundamental movement skills on second grade of elementary school students.


The research was carried out in 5 villages at Rawamangun Elementary School in East Jakarta. The population consists of 183 students however 36 students were selected.


Fundamental movement skills were divided into three activities such as running and jumping, manipulative activities such as throwing and catching, and stabilizing and balancing activities such as walking on the bridge. The motion is divided into three fundamental movement categories, namely locomotor skills, non-locomotor skills, and manipulative skills. (1) Locomotor skills refer to movement that uses the body to move from one place to another or liftthe body up like jumping and hopping. Other examples include walking, running, skipping, running like leaping, slidingand galloping, (2) Non-Locomotor skills are defined as a form of motion without transferring from one place to another. This category includes movement: bending, stretching, pushing, and pulling, twisting, turning, and shaking, (3) Manipulative skills


are categorized as a movement in a game when a kid is holding some kind of object or tool. Most of these capabilities involve the hands and feet, but other parts of the body can also be used. Most manipulative things are fundamental for a lot of skills in games like throwing, catching, and kicking.

Based on the definitions and explanations, it can be concluded that the fundamental movement skills is a pattern of behavior that is expressed through three motion activities that have different characteristics and it is related to (1) moving motion (locomotor): running, jumping, (2) unmoving motion (non-locomotor): balance, flexibility, and (3) manipulative motion: throwing, catching, kicking.

Parenting refers to the ways parents apply reciprocally in dealing with their children to establish attitudes and behavior as expected of parents and the community with the aim to become mature in time. The effect of treatment on the parents during infancy and early age can affect the development and status of the children themselves because the involvement of children in this age is strongly influenced by parental care. Parenting is related to how the family provides huge impact for the development of a child. Parenting is not only about caring for or supervising children, but parenting also includes: education, manners, discipline, responsibility, knowledge and relationships which are rooted in the parents' knowledge.


This study used ex-post facto method that collects data through questionnaires and tests the appearance of motion. In order to collect data related to parenting types, indicators consisting three levels of scale using a range of up to three were used to measure the shape of the patterns of parenting in motion play activities at home. Test battery consisting of tests run, jump, kick, catch, throw,


balance and, flexibility was used to measure the fundamental movement skills.

This research used ex-post facto method, by collecting data through questionnaire and test motion performance. To collect data related to parenting questionnaire, indicators that measure how the shape parenting to play in motion activities at home were prepared/developed. Test battery such as run, jump, kick, catch, throw, balancing and flexibility was used to test the fundamental movement skills.

Before determining the selected samples, population was first established in accordance with the purpose of research. The selected population characteristics are as follows:

a. The population consists of students of Second grade of Elementary School located in the village Rawamangun, of which the physical education teacher's academic background is sports science in education.

b. A total of 183 Second grade of Elementary School students from 5 (five) elementary schools located in the village Rawamangun who suit the criteria qualified as the samples.

The sampling measure was conducted as follows: Samples were collected by using Total Sampling i.e. 183 students. The questionnaires classified students into two groups namely (1). Students with permissive parenting, (2). Students with authoritarian parenting. 36 students were selected for the study.

The key findings:

The analysis found that there were significant differences in fundamental movement skills of second grade elementary school students between permissive parenting and authoritarian parenting. The results show that the fundamental movement skills of


permissive parenting are better than the fundamental movement skills of authoritarian parenting.

Data of Fundamental Movement Skills of Students with Permissive Parenting :

The overall data of fundamental movement skills of students with permissive parenting reveal 18 students with the highest score of 60.45 and the lowest score of 44.22, an average score of 51.97, standard deviation 4.30, modus 52.8 and median 57.7

Data of Fundamental Movement Skills of Students (with Authoritarian Parenting)

The overall data of fundamental movement skills of students with authoritarian parenting show 18 students with the highest score of 56.49 and the lowest score of 39.5, an average score of 48.42, standard deviation 4.79, modus 45.09 and median 50.19.

The Differences between the Fundamental Movement Skills of Permissive and Authoritarian Parenting

Results of fundamental movement skills of the students with permissive parenting showed an average value of 51.97 with a standard deviation of 4.30, while the fundamental movement skills of the students with authoritarian parenting showed an average value of 48.42 with a standard deviation of 4.79.

Results of analysis of variance (ANOVA) related to the differences in fundamental movement skills of students with permissive parenting with fundamental movement skills of students with authoritarian parenting as a whole can be seen in the following table.

The null hypothesis is rejected. By rejecting the null hypothesis,


this means the alternative hypothesis is accepted. Thus, it means that there are significant differences between the fundamental movement skills of students with permissive parenting with fundamental movement skills of students with authoritarian parenting. Then the study of alternative hypothesis which states that the fundamental motor skills of students with permissive parenting are better than the fundamental movement skills of students with authoritarian parenting was received.


Based on the data obtained, the hypothesis testing results and discussion of the results of this study, it can be concluded that there were significant differences in fundamental movement skills at second grade elementary school students between permissive and authoritarian parenting. The calculation results showed that overall score of fundamental movement skills of students with permissive parenting better than the score of fundamental movement skills of students with authoritarian parenting. It also confirms that there are significant differences between the scores of fundamental movement skills of students with permissive and authoritarian parenting suggesting that different parenting type results in different score.

1) Parents should allow time for the child to perform a variety of playing activities because the motion activities that children do have an important thing in stimulating the development of a child's basic motion.

2) Limited time or opportunity for parents to accompany the child to play does not make an excuse for parents to restrict children to play because the restrictions will affect a child's basic motor development.

3) The scope of this research is still limited and the population of students with limited sample, thus generalization can only be done on the population. It is suggested that further research should


examine a bigger sample in a different area to get better result.

4) Other researchers can search for and examine other variables that can affect the fundamental movement skills of elementary school students.

Reference:

Sari, E. F. N. (2014). Parenting and fundamental movement skills. Asian Social Science, 10(5), 22-27. Retrieved from http://search.proquest.com/docview/1510275764?accountid=35812

DescriptiveThis analysis should indicate the means, standard deviations, and range of scores for variables (Leedy, 2013). Involves collecting data in order to test hypotheses or answer questions regarding the participants of the study. Data, which are typically numeric, are collected through surveys, interviews, or through observation (Simon, 2013). The investigator reports the numerical results for one or more variables on the participants or units of analysis of the study (Simon, 2013).


Injuries are one of the main reasons why people stop participating in health-enhancing physical activities. It is proposed that musculoskeletal sporting injuries sustained dining youth can impair mobility later in life and have a detrimental influence on the aging population. Sport injury and its prevention are important public health issues and areas of concern. The prevention of sports injuries relies on several levels of operation for optimal implementation and requires active participation from large numbers of individuals. Future research should study the role and effect of purposefully prescribed exercises in decreasing the incidence and severity of musculoskeletal injuries sustained during recreational alpine skiing and snowboarding. The aim of this study was to systematically review the literature for injury prevention recommendations specific to recreational alpine skiers and snowboarders. The focus was to discern recommendations that targeted physical fitness, exercise and/or training in the prevention of musculoskeletal injuries in these two sports.



Fourteen electronic databases were systematically searched in October 2011 using relevant MeSH terms, keywords. Booleans and truncation symbols. The databases searched were: AMED (1985-), CINAHL® (1981-), Cochrane Central Register of Controlled Trials (1898-), Cochrane Database of Systematic Reviews (1995-), Database of Abstracts of Reviews of Effects (1994-), EMBASE (1947-), MEDLINE® (1948-), PEDro (1929-), PsycINFO® (1806-), PubMed (1951-), SciVerse Scopus (1823-), SPORTDiscus(TM) (1985-), Web of Knowledge(TM) (1864-) and Web of Science® (1898-). The search strategy employed was: "(skiing OR snowboarding) AND ((wounds and injuries) OR injur*) AND ((prevention and control) OR (accident prevention) OR (primary prevention) OR prevent*))". In addition to the systematic electronic database search, the reference lists of all articles subsequently included in the review were manually searched, as were relevant journals and key authors in the field of injury prevention research.


Study Selection Articles were included if they addressed injury prevention, recreational alpine skiing or snowboarding and musculoskeletal injuries. Only original research articles published in peer-reviewed journals, and in the English-language, were reviewed. Articles on elite athletes were excluded.

Study Appraisal and Synthesis Methods Two independent reviewers quality assessed articles meeting inclusion criteria using a modified version of the Downs and Black Quality Assessment Checklist. Data on study population, study design, study location and injury prevention recommendation(s) were extracted from articles using a standard form and subsequently categorized to facilitate data synthesis.


A total of 30 articles met the inclusion criteria and were


reviewed, having an average ± standard deviation quality score of 72 % ± 17 % (range: 23-100 %). Overall, 80 recommendations for the prevention of musculoskeletal injuries in recreational alpine skiers and snowboarders were identified and classified into five main groups: equipment (n = 24), education and knowledge (n = 11), awareness and behaviour (n = 15), experience (n = 10) and third-party involvement (n = 20). No recommendations pertained to physical fitness, exercise and/or training per se, or its role in preventing injury.

The key findings:

The importance of targeting physical fitness in injury prevention is accepted in sports medicine and rehabilitation; yet, there was a paucity of articles included in this review that explicitly investigated this aspect with regards to recreational alpine skiing and snowboarding. The most frequent recommendations for preventing skiing and snowboarding injuries concerned equipment or the involvement of third parties. The dominance of equipment related measures in the injury prevention literature may be rationalized from a sports biomechanics viewpoint, as these activities involve high velocities and impact forces. Nonetheless, this also indicates a need for appropriate levels of strength, endurance and conditioning to meet the technical demands of these sports.


Future research is encouraged to investigate the role of physical fitness, exercise and training in decreasing the incidence and severity of skiing and snowboarding injuries in recreational athletes.

References:Hébert-Losier, K., & Holmberg, H. (2013). What are the exercise-based injury prevention recommendations for recreational alpine


skiing and snowboarding? A systematic review. Sports Medicine, 43(5), 355-66. Retrieved from http://search.proquest.com/docview/1462389486?accountid=35812

References for Primary Characteristics of Research Designs

Bridges, G. S., Gillmore, G. M., Pershing, J. L., & Bates, K. A. (1998). Teaching quantitative research methods: A quasi-experimental analysis. Teaching Sociology, 26(1), 14. Retrieved from http://search.proquest.com/docview/223521919?accountid=35812

Creswell, J. W. (2014). Research design. Qualitative, quantitative, and mixed methods approaches. (4th ed.). Retrieved from The University of Phoenix eBook Collection database.

Leedy, P. D., & Ormrod, J. E. (2013). Practical research: Planning and design (0th ed.). Boston: Pearson.

Simon, Marilyn (2013). Quantitative Research: The “N” Side in the Paradigm War. Retrieved from University of Phoenix website.

quantitative research methods by cheryl vierheilig

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